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Researchers detail texture-swapping AI that could be used to create deepfakes

July 8, 2020 Big Data

In a preprint paper published on Arxiv.org, researchers at the University of California, Berkeley and Adobe Research describe the Swapping Autoencoder, a machine learning model designed specifically for image manipulation. They claim it can modify any image in a variety ways, including texture swapping, while remaining “substantially” more efficient compared with previous generative models.

The researchers acknowledge that their work could be used to create deepfakes, or synthetic media in which a person in an existing image or video is replaced with someone else’s likeness. In a human perceptual study, subjects were fooled 31% of the time by images created using the Swapping Autoencoder. But they also say that proposed detectors can successfully spot images manipulated by the tool at least 73.9% of the time, suggesting the Swapping Autoencoder is no more harmful than other AI-powered image manipulation tools.

“We show that our method based on an auto-encoder model has a number of advantages over prior work, in that it can accurately embed high-resolution images in real-time, into an embedding space that disentangles texture from structure, and generates realistic output images … Each code in the representation can be independently modified such that the resulting image both looks realistic and reflects the unmodified codes,” the coauthors of the study wrote.

The researchers’ approach isn’t novel in the sense that many AI models can edit portions of images to create new images. For example, the MIT-IBM Watson AI Lab released a tool that lets users upload photographs and customize the appearance of pictured buildings, flora, and fixtures, and Nvidia’s GauGAN can create lifelike landscape images that never existed. But these models tend to be challenging to design and computationally intensive to run.

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By contrast, the Swapping Autoencoder is lightweight, using image swapping as a “pretext” task for learning an embedding space useful for image manipulation. It encodes a given image into two separate latent codes — a “structure” code and a “texture” code — intended to represent structure and texture, and during training, the structure code learns to correspond to the layout or structure of a scene while the texture codes capture properties about the scene’s overall appearance.

In an experiment, the researchers trained Swapping Autoencoder on a data set containing images of churches, animal faces, bedrooms, people, mountain ranges, and waterfalls and built a web app that offers fine-grained control over uploaded photos. The app supports global style editing and region editing as well as cloning, with a brush tool that replaces the structure code from another part of the image.

“Tools for creative expression are an important part of human culture … Learning-based content creation tools such as our method can be used to democratize content creation, allowing novice users to synthesize compelling images,” the coauthors wrote.

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AI tools could improve fake news detection by analyzing users’ interactions and comments

June 1, 2020 Big Data

In a paper published on the preprint server Arxiv.org, researchers affiliated with Microsoft and Arizona State University propose an approach to detecting fake news that leverages a technique called weak social supervision. They say that by enabling the training of fake news-detecting AI even in scenarios where labeled examples aren’t available, weak social supervision opens the door to exploring how aspects of user interactions indicate news might be misleading.

According to the Pew Research Center, approximately 68% of U.S. adults got their news from social media in 2018 — which is worrisome considering misinformation about the pandemic continues to go viral, for instance. Companies from Facebook and Twitter to Google are pursuing automated detection solutions, but fake news remains a moving target owing to its topical and stylistic diverseness.

Building on a study published in April, the coauthors of this latest work suggest that weak supervision — where noisy or imprecise sources provide data labeling signals — could improve fake news detection accuracy without requiring fine-tuning. To this end, they built a framework dubbed Tri-relationship for Fake News (TiFN) that models social media users and their connections as an “interaction network” to detect fake news.

Interaction networks describe the relationships among entities like publishers, news pieces, and users; given an interaction network, TiFN’s goal is to embed different types of entities, following from the observation that people tend to interact with like-minded friends. In making its predictions, the framework also accounts for the fact that connected users are more likely to share similar interests in news pieces; that publishers with a high degree of political bias are more likely to publish fake news; and that users with low credibility are more likely to spread fake news.

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To test whether TiFN’s weak social supervision could help to detect fake news effectively, the team validated it against a Politifact data set containing 120 true news and 120 verifiably fake pieces shared among 23,865 users. Versus baseline detectors that consider only news content and some social interactions, they report that TiFN achieved between 75% to 87% accuracy even with a limited amount of weak social supervision (within 12 hours after the news was published).

In another experiment involving a separate custom framework called Defend, the researchers sought to use as a weak supervision signal news sentences and user comments explaining why a piece of news is fake. Tested on a second Politifact data set consisting of 145 true news and 270 fake news pieces with 89,999 comments from 68,523 users on Twitter, they say that Defend achieved 90% accuracy.

[W]ith the help of weak social supervision from publisher-bias and user-credibility, the detection performance is better than those without utilizing weak social supervision. We [also] observe that when we eliminate news content component, user comment component, or the co-attention for news contents and user comments, the performances are reduced. [This] indicates capturing the semantic relations between the weak social supervision from user comments and news contents is important,” wrote the researchers. “[W]e can see within a certain range, more weak social supervision leads to a larger performance increase, which shows the benefit of using weak social supervision.”

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Google’s federated analytics method could analyze end user data without invading privacy

May 28, 2020 Big Data

In a blog post today, Google laid out the concept of federated analytics, a practice of applying data science methods to the analysis of raw data that’s stored locally on edge devices. As the tech giant explains, it works by running local computations over a device’s data and making only the aggregated results — not the data from the particular device — available to authorized engineers.

While federated analytics is closely related to federated learning, an AI technique that trains an algorithm across multiple devices holding local samples, it only supports basic data science needs. It’s “federated learning lite” — federated analytics enables companies to analyze user behaviors in a privacy-preserving and secure way, which could lead to better products. Google for its part uses federated techniques to power Gboard’s word suggestions and Android Messages’ Smart Reply feature.

“The first exploration into federated analytics was in support of federated learning: how can engineers measure the quality of federated learning models against real-world data when that data is not available in a data center? The answer was to re-use the federated learning infrastructure but without the learning part,” Google research scientist Daniel Ramage and software engineer Stefano Mazzocchi said in a statement. “In federated learning, the model definition can include not only the loss function that is to be optimized, but also code to compute metrics that indicate the quality of the model’s predictions. We could use this code to directly evaluate model quality on phones’ data.”

As an example, in a user study, Gboard engineers measured the overall quality of word prediction models against raw typing data held on phones. Participating phones downloaded a candidate model, locally computed a metric of how well the model’s predictions matched words that were actually typed, and then uploaded the metric without any adjustment to the model itself or any change to the Gboard typing experience. By averaging the metrics uploaded by many phones, engineers learned a population-level summary of model performance.

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In a separate study, Gboard engineers wanted to discover words commonly typed by users and add them to dictionaries for spell-checking and typing suggestions. They trained a character-level recurrent neural network on phones, using only the words typed on these phones that weren’t already in the global dictionary. No typed words ever left the phones, but the resulting model could then be used in the datacenter to generate samples of frequently typed character sequences — i.e., the new words.

Beyond model evaluation, Google uses federated analytics to support the Now Playing feature on its Pixel phones, which shows what song might be playing nearby. Under the hood, Now Playing taps an on-device database of song fingerprints to identify music near a phone without the need for an active network connection.

When it recognizes a song, Now Playing records the track name into the on-device history, and when the phone is idle and charging while connected to Wi-Fi, Google’s federated learning and analytics server sometimes invites it to join a “round” of computation with hundreds of phones. Each phone in the round computes the recognition rate for the songs in its Now Playing history and uses a secure aggregation protocol to encrypt the results. The encrypted rates are sent to the federated analytics server, which doesn’t have the keys to decrypt them individually; when combined with the encrypted counts from the other phones in the round, the final tally of all song counts can be decrypted by the server.

The result enables Google’s engineers to improve the song database without any phone revealing which songs were heard, for example, by making sure the database contains truly popular songs. Google claims that in its first improvement iteration, federated analytics resulted in a 5% increase in overall song recognition across all Pixel phones globally.

“We are also developing techniques for answering even more ambiguous questions on decentralized datasets like ‘what patterns in the data are difficult for my model to recognize?’ by training federated generative models. And we’re exploring ways to apply user-level differentially private model training to further ensure that these models do not encode information unique to any one user,” wrote Ramage and Mazzocchi. “It’s still early days for the federated analytics approach and more progress is needed to answer many common data science questions with good accuracy … [B]ut federated analytics enables us to think about data science differently, with decentralized data and privacy-preserving aggregation in a central role.”

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AI Weekly: Machine learning could lead cybersecurity into uncharted territory

February 15, 2020 Big Data

Once a quarter, VentureBeat publishes a special issue to take an in-depth look at trends of great importance. This week, we launched issue two, examining AI and security. Across a spectrum of stories, the VentureBeat editorial team took a close look at some of the most important ways AI and security are colliding today. It’s a shift with high costs for individuals, businesses, cities, and critical infrastructure targets — data breaches alone are expected to cost more than $ 5 trillion by 2024 — and high stakes.

Throughout the stories, you may find a theme that AI does not appear to be used much in cyberattacks today. However, cybersecurity companies increasingly rely on AI to identify threats and sift through data to defend targets.

Security threats are evolving to include adversarial attacks against AI systems; more expensive ransomware targeting cities, hospitals, and public-facing institutions; misinformation and spear phishing attacks that can be spread by bots in social media; and deepfakes and synthetic media have the potential to become security vulnerabilities.

In the cover story, European correspondent Chris O’Brien dove into how the spread of AI in security can lead to less human agency in the decision-making process, with malware evolving to adapt and adjust to security firm defense tactics in real time. Should costs and consequences of security vulnerabilities increase, ceding autonomy to intelligent machines could begin to seem like the only right choice.

We also heard from security experts like McAfee CTO Steve Grobman, F-Secure’s Mikko Hypponen, and Malwarebytes Lab director Adam Kujawa, who talked about the difference between phishing and spear phishing, addressed an anticipated rise in personalized spear phishing attacks ahead, and spoke generally to the fears — unfounded and not — around AI in cybersecurity.

VentureBeat staff writer Paul Sawers took a look at how AI could be used to reduce the massive job shortage in the cybersecurity sector, while Jeremy Horwitz explored how cameras in cars and home security systems equipped with AI will impact the future of surveillance and privacy.

AI editor Seth Colaner examines how security and AI can seem heartless and inhuman but still relies heavily on people, who are still a critical factor in security, both as defenders and targets. Human susceptibility is still a big part of why organizations become soft targets, and education around how to properly guard against attacks can lead to better protection.

We don’t know yet the extent to which those carrying out attacks will come to rely on AI systems. And we don’t know yet if open source AI opened Pandora’s box, or to what extent AI might increase threat levels. One thing we do know is that cybercriminals don’t appear to need AI to be successful today.

I’ll leave it to you to read the special issue and draw your own conclusions, but one quote worth remembering comes from Shuman Ghosemajumder, formerly known as the “click fraud czar” at Google and now CTO at Shape Security, in Sawers’ article. “[Good actors and bad actors] are both automating as much as they can, building up DevOps infrastructure and utilizing AI techniques to try to outsmart the other,” he said. “It’s an endless cat-and-mouse game, and it’s only going to incorporate more AI approaches on both sides over time.”

For AI coverage, send news tips to Khari Johnson and Kyle Wiggers and AI editor Seth Colaner — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI Channel.

Thanks for reading,

Khari Johnson

Senior AI Staff Writer

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Big Data – VentureBeat

Jussie Smollett Could Return To Finale Season Of ‘Empire’

January 4, 2020 Humor

The sixth and final season of the Fox musical drama Empire has continued on without the inclusion of Jussie Smollett, who played Jamal Lyon. However, in an interview with TV Line, showrunner Brett Mahoney revealed that discussions are underway for a possible Smollett return.

“It would be weird in my mind to end this family show and this family drama of which he was such a significant part of without seeing him,” Mahoney said. “It’s fair to say it’s being discussed, but there’s no plan as of yet to bring him back. There’s been no decision made.”

For those unfamiliar, Smollett was written out at the end of the fifth season following accusations of filing a false police report. The report claimed he was the victim of a racist and homophobic attack in Chicago in January. Charges were eventually dropped against Smollett in March.

Empire‘s midseason finale aired on Tuesday, December 17, 2019.

Tone Bell Comedy From Cedric the Entertainer In Works At CBS

WATCH: Jennifer Hudson Is Aretha Franklin In ‘Respect’ Biopic

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AI has a privacy problem, but these techniques could fix it

December 22, 2019 Big Data

Artificial intelligence promises to transform — and indeed, has already transformed — entire industries, from civic planning and health care to cybersecurity. But privacy remains an unsolved challenge in the industry, particularly where compliance and regulation are concerned.

Recent controversies put the problem into sharp relief. The Royal Free London NHS Foundation Trust, a division of the U.K.’s National Health Service based in London, provided Alphabet’s DeepMind with data on 1.6 million patients without their consent. Google — whose health data-sharing partnership with Ascension became the subject of scrutiny in November — abandoned plans to publish scans of chest X-rays over concerns that they contained personally identifiable information. This past summer, Microsoft quietly removed a data set (MS Celeb) with more than 10 million images of people after it was revealed that some weren’t aware they had been included.

Separately, tech giants including Apple and Google have been the subject of reports uncovering the potential misuse of recordings collected to improve assistants like Siri and Google Assistant. In April, Bloomberg revealed that Amazon employs contract workers to annotate thousands of hours of audio from Alexa-powered devices, prompting the company to roll out user-facing tools that quickly delete cloud-stored data.

Increasingly, privacy isn’t merely a question of philosophy, but table stakes in the course of business. Laws at the state, local, and federal levels aim to make privacy a mandatory part of compliance management. Hundreds of bills that address privacy, cybersecurity, and data breaches are pending or have already been passed in 50 U.S. states, territories, and the District of Columbia. Arguably the most comprehensive of them all — the California Consumer Privacy Act — was signed into law roughly two years ago. That’s not to mention the Health Insurance Portability and Accountability Act (HIPAA), which requires companies to seek authorization before disclosing individual health information. And international frameworks like the EU’s General Privacy Data Protection Regulation (GDPR) aim to give consumers greater control over personal data collection and use.

AI technologies have not historically been developed with privacy in mind. But a subfield of machine learning — privacy-preserving machine learning — seeks to pioneer approaches that might prevent the compromise of personally identifiable data. Of the emerging techniques, federated learning, differential privacy, and homomorphic encryption are perhaps the most promising.

Neural networks and their vulnerabilities

The so-called neural networks at the heart of most AI systems consist of functions (neurons) arranged in layers that transmit signals to other neurons. Those signals — the product of data, or inputs, fed into the network — travel from layer to layer and slowly “tune” the network, in effect adjusting the synaptic strength (weights) of each connection. Over time, the network extracts features from the data set and identifies cross-sample trends, eventually learning to make predictions.

Neural networks don’t ingest raw images, videos, audio, or text. Rather, samples from training corpora are transformed algebraically into multidimensional arrays like scalars (single numbers), vectors (ordered arrays of scalars), and matrices (scalars arranged into one or more columns and one or more rows). A fourth entity type that encapsulates scalars, vectors, and matrices — tensors — adds in descriptions of valid linear transformations (or relations).

In spite of these transformations, it’s often possible to discern potentially sensitive information from the outputs of the neural network. The data sets themselves are vulnerable, too, because they’re not typically obfuscated, and because they’re usually stored in centralized repositories that are vulnerable to data breaches.

By far the most common form of machine learning reverse engineering is called a membership inference attack, where an attacker — using a single data point or several data points — determines whether it belonged to the corpus on which a target model was trained. As it turns out, removing sensitive information from a data set doesn’t mean it can’t be re-inferred, because AI is exceptionally good at recreating samples. Barring the use of privacy-preserving techniques, trained models incorporate compromising information about whatever set they’re fed.

In one study, researchers from the University of Wisconsin and the Marshfield Clinic Research Foundation were able to extract patients’ genomic information from a machine learning model that was trained to predict medical dosage. In another, Carnegie Mellon and University of Wisconsin-Madison research scientists managed to reconstruct specific head shot images from a model trained to perform facial recognition.

A more sophisticated data extraction attack employs generative adversarial networks, or GANs — two-part AI systems consisting of generators that produce samples and discriminators that attempt to distinguish between the generated samples and real-world samples. They’re trained to generate samples closely resembling those in the original corpus without having access to said samples and by interacting with the discriminative deep neural network in order to learn the data’s distribution.

In 2017, researchers demonstrated that GANs could be trained to produce prototypical samples of a private set, revealing sensitive information from this set. In another study, a team used GANs to infer the samples that were used to train an image-generating machine learning model, with up to a 100% success rate in a “white-box” setting where they had access to the target model’s parameters (e.g., the variables a chosen AI technique uses to adjust to data).

Fortunately, there’s hope in the form of approaches like federated learning and differential privacy.

Federated learning

Quite simply, federated learning is a technique that trains an AI algorithm across decentralized devices or servers (i.e., nodes) holding data samples without exchanging those samples, enabling multiple parties to build a common machine learning model without sharing data liberally. That’s in contrast to classical decentralized approaches, which assume local data samples are widely distributed.

A central server might be used to orchestrate the steps of the algorithm and act as a reference clock, or the arrangement might be peer-to-peer (in which case no such server exists). Regardless, local models are trained on local data samples, and the weights are exchanged among the models at some frequency to generate a global model.

It’s an iterative process broken up into sets of interactions known as federated learning rounds, where each round consists of transmitting the current global model state to participating nodes. Local models are trained on the nodes to produce a set of potential model updates at each node, and then the local updates are aggregated and processed into a single global update and applied to the global model.

Federated learning has been deployed in production by Google, a federated learning pioneer. Google uses it for personalization in its Gboard predictive keyboard across “tens of millions” of iOS and Android devices. Alongside the Pixel 4 launch, Google debuted an improved version of its Now Playing music-recognizing feature that aggregates the play counts of songs in a federated fashion, identifying the most popular songs by locality to improve recognition. And the company recently debuted a module for its TensorFlow machine learning framework dubbed TensorFlow Federated, which is intended to make it easier to experiment with deep learning and other computations on decentralized data.

Of course, no technique is without its flaws; federated learning requires frequent communication among nodes during the learning process. Tangibly, in order for the machine learning models to exchange parameters, they need significant amounts of processing power and memory. Other challenges include an inability to inspect training examples, as well as bias due in part to the fact that the AI models train only when power and a means of transmitting their parameters is available.

Differential privacy

Federated learning goes hand in hand with differential privacy, a system for publicly sharing information about a data set by describing patterns of groups within the corpus while withholding data about individuals. It usually entails injecting a small amount of noise into the raw data before it’s fed into a local machine learning model, such that it becomes difficult for malicious actors to extract the original files from the trained model.

Intuitively, an algorithm can be considered differentially private if an observer seeing its output cannot tell if a particular individual’s information was used in the computation. A differentially private federated learning process, then, enables nodes to jointly learn a model while hiding what data any node holds.

The open source TensorFlow library, TensorFlow Privacy, operates on the principle of differential privacy. Specifically, it fine-tunes models using a modified stochastic gradient descent that averages together multiple updates induced by training data examples, clips each of these updates, and adds noise to the final average. This prevents the memorization of rare details, and it offers some assurance that two machine learning models will be indistinguishable whether a person’s data is used in their training or not.

Apple has been using some form of differential privacy since 2017 to identify popular emojis, media playback preferences in Safari, and more, and the company combined it with federated learning in its latest mobile operating system release (iOS 13). Both techniques help to improve the results delivered by Siri, as well as apps like Apple’s QuickType keyboard and iOS’ Found In Apps feature. The latter scans both calendar and mail apps for the names of contacts and callers whose numbers aren’t stored locally.

For their part, researchers from Nvidia and King’s College London recently employed federated learning to train a neural network for brain tumor segmentation, a milestone Nvidia claims is a first for medical image analysis. Their model uses a data set from the BraTS (Multimodal Brain Tumor Segmentation) Challenge of 285 patients with brain tumors, and as with the approaches taken by Google and Apple, it leverages differential privacy to add noise to that corpus.

“This way, [each participating node] stores the updates and limits the granularity of the information that we actually share among the institutions,” Nicola Rieke, Nvidia senior researcher, told VentureBeat in a previous interview. “If you only see, let’s say, 50% or 60% of the model updates, can we still combine the contributions in the way that the global model converges? And we found out ‘Yes, we can.’ It’s actually quite impressive. So it’s even possible to aggregate the model in a way if you only share 10% of the model.”

Of course, differential privacy isn’t perfect, either. Any noise injected into the underlying data, input, output, or parameters impacts the overall model’s performance. In one study, after adding noise to a training data set, the authors noted a decline in predictive accuracy from 94.4% to 24.7%.

An alternative privacy-preserving machine learning technique — homomorphic encryption — suffers from none of those shortcomings, but it’s far from an ace in the hole.

Homomorphic encryption

Homomorphic encryption isn’t new — IBM researcher Craig Gentry developed the first scheme in 2009 — but it’s gained traction in recent years, coinciding with advances in compute power and efficiency. It’s basically a form of cryptography that enables computation on plaintext (file contents) encrypted using an algorithm (also known as ciphertexts), so that the generated encrypted result exactly matches the result of operations that would have been performed on unencrypted text. Using this technique, a “cryptonet” (e.g, any learned neural network that can be applied to encrypted data) can perform computation on data and return the encrypted result back to some client, which can then use the encryption key — which was never shared publicly — to decrypt the returned data and get the actual result.

“If I send my MRI images, I want my doctor to be able to see them immediately, but nobody else,” Jonathan Ballon, vice president of Intel’s IoT group, told VentureBeat in an interview earlier this year. “[Homomorphic] encryption delivers that, and in addition, the model itself is encrypted. So a company … can put that model [on a public cloud], and that [cloud provider] has no idea what their model looks like.”

In practice, homomorphic encryption libraries don’t yet fully leverage modern hardware, and they’re at least an order of magnitude slower than conventional models. But newer projects like cuHE, an accelerated encryption library, claim speedups of 12 to 50 times on various encrypted tasks over previous implementations. Moreover, libraries like PySyft and tf-encrypted — which are built on Facebook’s PyTorch machine learning framework and TensorFlow, respectively — have made great strides in recent months. So, too, have abstraction layers like HE-Transformer, a backend for nGraph (Intel’s neural network compiler) that delivers leading performance on some cryptonets.

In fact, just a few months ago, Intel researchers proposed nGraph-HE2, a successor to HE-Transformer that enables inference on standard, pretrained machine learning models using their native activation functions. They report in a paper that it was 3 times to 88 times faster at runtime in terms of scalar encoding (the encoding of a numeric value into an array of bits) with double the throughput, and that additional multiplication and addition optimizations yielded a further 2.6 times to 4.2 time runtime speedup.

IBM senior research scientist Flavio Bergamaschi has investigated the use of hardware at the edge to implement homomorphic encryption operations. In a recent study, he and colleagues deployed a local homomorphic database on a device equipped with an AI camera, enabling search to be performed directly on that camera. They report that performance was “homomorphically fast,” with lookup taking only 1.28 seconds per database entry, which amounted to a 200-entry query in five minutes.

“We are at what I call inflection points in performance,” he told VentureBeat in a recent phone interview. “Now, fully homomorphic encryption is fast enough in terms of performance that it’s perfectly adequate for certain use cases.”

On the production side, Bergamaschi and team worked with a U.S.-based banking client to encrypt a machine learning process using homomorphic techniques. That machine learning process — a linear regression model with well over a dozen variables — analyzed 24 months of transaction data from current account holders to predict the financial health of those accounts, partly to recommend products like loans. Motivated by the client’s privacy and compliance concerns, the IBM team encrypted the existing model and the transaction data in question, and they ran predictions using both the encrypted and unencrypted model to compare performance. While the former ran slower than the latter, the accuracy was the same.

“This is an important point. We showed that if we didn’t have any model for [our] prediction, we could take transaction data and perform the training of a new model in production,” Bergamaschi said.

Enthusiasm for homomorphic encryption has given rise to a cottage industry of startups aiming to bring it to production systems. Newark, New Jersey-based Duality Technologies, which recently attracted funding from one of Intel’s venture capital arms, pitches its homomorphic encryption platform as a privacy-preserving solution for “numerous” enterprises, particularly those in regulated industries. Banks can conduct privacy-enhanced financial crime investigations across institutions, so goes the company’s sales pitch, while scientists can tap it to collaborate on research involving patient records.

But like federated learning and differential privacy, homomorphic encryption offers no magic bullet. Even leading techniques can calculate only polynomial functions — a nonstarter for the many activation functions in machine learning that are non-polynomial. Plus, operations on encrypted data can involve only additions and multiplications of integers, which poses a challenge in cases where learning algorithms require floating point computations.

“In domains where you can take 10 seconds to turn around your inference, [homomorphic encryption] is fine, but If you need a three-millisecond turnaround time today, there’s just no way to do it,” Ballon said. “The amount of computation is too high, and this goes back to the domain of engineering.”

Since 2014, Bergamaschi and colleagues have experimented with hardware approaches to accelerating homomorphic operations. Historically, bandwidth has been the biggest stumbling block — while accelerators yield strong benchmark performance individually, they don’t yield strong systems performance overall. That’s because the data required to perform the operations requires a lot of bandwidth between processors and the accelerator.

The solution might lie in techniques that make more efficient use of processors’ on-chip memory. A paper published by researchers at the Korea Advanced Institute of Science and Technology advocates the use of a combined cache for all normal and security-supporting data, as well as memory scheduling and mapping schemes for secure processors and a type-aware cache insertion module. They say that together, the combined approaches could reduce encryption performance degradation from 25%-34% to less than 8%-14% in typical 8-core and 16-core secure processors, with minimal extra hardware costs.

A long way to go

New techniques might solve some of the privacy issues inherent in AI and machine learning, but they’re in their infancy and not without their shortcomings.

Federated learning trains algorithms across decentralized edge devices without exchanging their data samples, but it’s difficult to inspect and at the mercy of fluctuations in power, computation, and internet. Differential privacy, which exposes information about a data set while withholding information about the individuals, suffers dips in accuracy caused by injected noise. As for homomorphic encryption — a form of encryption that allows computation on encrypted data — it’s somewhat slow and computationally demanding.

Nevertheless, folks like Ballon believe all three approaches are steps in the right direction. “This is very similar to going from HTTP to HTTPS,” Ballon said. “We’ll have the tools and capabilities to make [privacy in machine learning] seamless someday, but we’re not quite there yet.”

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Big Data – VentureBeat

Artificial stupidity: ‘Move slow and fix things’ could be the mantra AI needs

October 6, 2019 Big Data

“Let’s not use society as a test-bed for technologies that we’re not sure yet how they’re going to change society,” warned Carly Kind, director at the Ada Lovelace Institute, an artificial intelligence (AI) research body based in the U.K. “Let’s try to think through some of these issues — move slower and fix things, rather than move fast and break things.”

Kind was speaking as part of a recent panel discussion at Digital Frontrunners, a conference in Copenhagen that focused on the impact of AI and other next-gen technologies on society.

The “move fast and break things” ethos embodied by Facebook’s rise to internet dominance is one that has been borrowed by many a Silicon Valley startup: develop and swiftly ship an MVP (minimal viable product), iterate, learn from mistakes, and repeat. These principles are relatively harmless when it comes to developing a photo-sharing app, social network, or mobile messaging service, but in the 15 years since Facebook came to the fore, the technology industry has evolved into a very different beast. Large-scale data breaches are a near-daily occurrence, data-harvesting on an industrial level is threatening democracies, and artificial intelligence (AI) is now permeating just about every facet of society — often to humans’ chagrin.

Although Facebook officially ditched its “move fast and break things” mantra five years ago, it seems that the crux of many of today’s technology problems come down to the fact that companies have moved (and continue to move) too fast — “full-steam ahead, and to hell with the consequences.”

‘Artificial stupidity’

Above: 3D rendering of robots speaking no evil, hearing no evil, seeing no evil.

Image Credit: Getty Images / Westend61

This week, news emerged that Congress has been investigating how facial recognition technology is being used by the military in the U.S. and abroad, noting that the technology is just not accurate enough yet.

“The operational benefits of facial recognition technology for the warfighter are promising,” a letter from Congress read. “However, overreliance on this emerging technology could also have disastrous consequences if faulty or inaccurate facial scans result in the inadvertent targeting of civilians or the compromise of mission requirements.”

The letter went on to note that the “accuracy rates for images depicting black and female subjects were consistently lower than for those of white and male subjects.”

While there are countless other examples of how far AI still has to go in terms of addressing biases in the algorithms, the broader issue at play here is that AI just isn’t good or trustworthy enough across the spectrum.

“Everyone wants to be at the cutting edge, or the bleeding edge — from universities, to companies, to government,” said Dr. Kristinn R. Thórisson, an AI researcher and founder of the Icelandic Institute for Intelligent Machines, speaking in the same panel discussion as Carly Kind. “And they think artificial intelligence is the next [big] thing. But we’re actually in the age of artificial stupidity.”

Thórisson is a leading proponent of what is known as artificial general intelligence (AGI), which is concerned with integrating disparate systems to create a more complex AI with humanlike attributes, such as self-learning, reasoning, and planning. Depending on who you ask, AGI is coming in 5 years, it’s a long way off, or it’s never happening — Thórisson, however, evidently does believe that AGI will happen one day. When that will be, he is not so sure — but what he is sure of is that today’s machines are not as smart as some may think.

“You use the word ‘understanding’ a lot when you’re talking about AI, and it used to be that people put ‘understanding’ in quotation marks when they talked about it in the context of AI,” Thórisson said. “When it comes down to it, these machines don’t really understand anything, and that’s the problem.”

For all the positive spins on how amazing AI now is in terms of trumping humans at poker, AlphaGo, or Honor of Kings, there are numerous examples of AI fails in the wild. By most accounts, driverless cars are nearly ready for prime time, but there is other evidence to suggest that there are still some obstacles to overcome before they can be left to their own devices.

For instance, news emerged this week that regulators are investigating Tesla’s recently launched automated Smart Summon feature, which allows drivers to remotely beckon their car inside a parking lot. In the wake of the feature’s official rollout last week, a number of users posted videos online showing crashes, near-crashes, and a general comical state of affairs.

So, @elonmusk – My first test of Smart Summon didn’t go so well. @Tesla #Tesla #Model3 pic.twitter.com/yC1oBWdq1I

— Roddie Hasan – راضي (@eiddor) September 28, 2019

This isn’t to pour scorn on the huge advances that have been made by autonomous carmakers, but it shows that the fierce battle to bring self-driving vehicles to market can sometimes lead to half-baked products that perhaps aren’t quite ready for public consumption.

Crossroads

The growing tension — between consumers, corporations, governments, and academia — around the impact of AI technology on society is palpable. With the tech industry prizing innovation and speed over iterative testing at a slower pace, there is a danger of things getting out of hand — the quest to “be first,” or to secure lucrative contracts and keep shareholders happy, might just be too alluring.

All the big companies, from Facebook, Amazon, and Google through to Apple, Microsoft, and Uber, are competing on multiple business fronts, with AI a common thread permeating it all. There has been a concerted push to vacuum up all the best AI talent, either through acquiring startups or simply hiring the top minds from the best universities. And then there is the issue of securing big-name clients with big dollars to spend — Amazon and Microsoft are currently locking horns to win a $ 10 billion Pentagon contract for delivering AI and cloud services.

In the midst of all this, tech firms are facing increasing pressure over their provision of facial recognition services (FRS) to the government and law enforcement. Back in January, a coalition of more than 85 advocacy groups penned an open letter to Google, Microsoft, and Amazon, urging them to cease selling facial recognition software to authorities — before it’s too late.

“Companies can’t continue to pretend that the ‘break then fix’ approach works,” said Nicole Ozer, technology and civil liberties director for the American Civil Liberties Union (ACLU) of California. “History has clearly taught us that the government will exploit technologies like face surveillance to target communities of color, religious minorities, and immigrants. We are at a crossroads with face surveillance, and the choices made by these companies now will determine whether the next generation will have to fear being tracked by the government for attending a protest, going to their place of worship, or simply living their lives.”

Then in April, two dozen AI researchers working across the technology and academia sphere called on Amazon specifically to stop selling its Rekognition facial recognition software to law enforcement agencies. The crux of the problem, according to the researchers, was that there isn’t sufficient regulation to control how the technology is used.

Above: An illustration shows Amazon Rekognition’s support for detecting faces in crowds.

Image Credit: Amazon

“We call on Amazon to stop selling Rekognition to law enforcement as legislation and safeguards to prevent misuse are not in place,” it said. “There are no laws or required standards to ensure that Rekognition is used in a manner that does not infringe on civil liberties.”

However, Amazon later went on record to say that it would serve any federal government with facial recognition technology — so long as it’s legal.

These controversies are not limited to the U.S. either — it’s a global problem that countries and companies everywhere are having to tackle. London’s King’s Cross railway station hit the headlines in August when it was found to have deployed facial recognition technology in CCTV security cameras, leading to questions not only around ethics, but also legality. A separate report published also discovered that local police had submitted photos of seven people for use in conjunction with King’s Cross’s facial recognition system, in a deal that was not disclosed until yesterday.

All these examples serve to feed the argument that AI development is outpacing society’s ability to put adequate checks and balances in place.

Pushback

Digital technology has often moved too fast for regulation or external oversight to keep up, but we’re now starting to see major regulatory pushbacks — particularly relating to data privacy. The California Consumer Privacy Act (CCPA), which is due to take effect on Jan 1, 2020, is designed to enhance privacy rights of consumers living across the state, while Europe is also currently weighing a new ePrivacy Regulation, which covers an individual’s right to privacy regarding electronic communications.

But the biggest regulatory advance in recent times has been Europe’s General Data Protection Regulation (GDPR), which stipulates all manner of rules around how companies should manage and protect their customers’ data. Huge fines await any company that contravenes GDPR, as Google found out earlier this year when it was hit with a €50 million ($ 57 million) fine by French data privacy body CNIL for “lack of transparency” over how it personalized ads. Elsewhere, British Airways (BA) and hotel giant Marriott were slapped with $ 230 million and $ 123 million fines respectively over gargantuan data breaches. Such fines may serve as incentives for companies to better manage data in the future, but in some respects the regulations we’re starting to see now are too little too late — the privacy ship has sailed.

“Rolling back is a really difficult thing to do — we’ve seen it around the whole data protection field of regulation, where technology moves much faster than regulation can move,” Kind said. “All these companies went ahead and started doing all these practices; now we have things like the GDPR trying to pull some of that back, and it’s very difficult.”

From looking back at the past 15 years or so, a time during which cloud computing and ubiquitous computing have taken hold, there are perhaps lessons to be learned in terms of how society proceeds with AI research, development, and deployment.

“Let’s slow things down a bit before we roll out some of this stuff, so that we do actually understand the societal impacts before we forge ahead,” Kind continued. “I think what’s at stake is so vast.”

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AI Weekly: Automation in the workplace could disproportionately affect women

September 21, 2019 Big Data

As AI and machine learning transform industries by automating much of the work currently done by humans, women’s careers will be disproportionately affected. That’s according to a McKinsey Global Institute report published earlier this year (“The future of women at work: Transitions in the age of automation“), which found that in the 10 economies (Canada, France, Germany, Japan, the U.K., the U.S., China, India, Mexico, and South America) contributing over 60% of GDP collectively, an average of 20% of women working today (107 million) could find their jobs displaced by 2030. That’s compared with 21% of men (163 million) in the same period.

Mekala Krishnan, a senior fellow at McKinsey’s Boston-based business and economics research arm and a member of the board of the Global Fund for Women, spoke about the research (which she coauthored) at MIT Technology Review’s EmTech MIT conference at the MIT Media Lab. Krishnan pointed out that monotonous or repetitive tasks are ripe for automation. She pegs the share of work that can be automated at greater than 90% in about 10% of occupations. Those particularly susceptible to disruption are construction, tech, and manufacturing (which she noted tend to be male-dominated), as well as female-dominated segments like health care and education.

Those sentiments jibe with a March 2019 report from the U.K.’s Office for National Statistics (ONS), which found that 10% of the U.K.’s workforce (about 1.5 million workers) occupy jobs that are at “high risk” of automation. ONS forecasted that service workers — chiefly waiters and waitresses, retail inventory restockers, and entry-level salespeople — would be disproportionately affected, as well as those in agricultural, automotive, and service industries. And the department predicted that women, who in 2017 held 70.2% of high-risk jobs, would bear the brunt of the coming labor market shifts.

“Essentially, the potential for job losses and job gains could be roughly the same for men and women on average,” said Krishnan. “On the other hand, there’s also opportunities for new jobs to be created.”

Krishnan anticipates that in industries like manufacturing, work might transition from shop floor production, say, to engineering and design. But she also expects a global paradigm shift leading to the creation of entirely new occupations that don’t yet exist.

It wouldn’t be unprecedented. Decades ago, roles like “social media manager” and “data scientist” hadn’t been conceived, much less sought after. Krishnan said that typically, roughly 10% of employment at any given time is in these newly emerged groups of occupations, amounting to 160 million jobs globally.

Whether they take up new work or acquire new skills in their current fields, Krishnan anticipates that tens of millions of workers will have to make some sort of occupational transition by 2030. Many of those workers are women — as many as 40 million to 160 million globally.

Encouragingly, in both developed and emerging markets, the new jobs that are expected to come into vogue are likely to be higher-wage, according to Krishnan. Those jobs will furthermore involve less drudgery, which will be traded for tasks ostensibly more socially and intellectually stimulating. In fact, Krishnan believes that this future of work will require more interpersonal know-how of the workers who occupy its roles.

Of course, the challenge for men and women in the workforce is figuring out how to navigate the transition.

It’s all but certain that advanced degrees and higher levels of education will be demanded of workers in mature and emerging markets. They’ll be expected to have strong leadership and managerial skills, and to excel at creative pursuits involving the use of technology. In fact, Krishnan expects that tech skills — competency with computers and a range of basic software — will be the highest growth area of any skills category in the next decade.

But retraining and reskilling are easier said than done, and geographic immobility remains a major barrier. Krishnan said that the percentage of U.S. workers unwilling or unable to relocate is at an all-time high, a fact that’s doubly problematic considering opportunities are expected to emerge in select pockets of the U.S. at the expense of others.

Those in developing countries won’t have it easier. Krishnan expects the bulk of new roles created by automation will be likewise concentrated in urban as opposed to rural regions, disadvantaging those without access to safe and affordable transportation.

“Women may have less time to reskill in many parts of the world, and they might have lower educational attainment,” Krishnan added. “They’re also going to have to learn to work more with technology, and this may be a particular barrier for women.”

If the transition proceeds smoothly, though, the benefits could be enormous. A separate McKinsey study forecasted that AI could contribute an additional 1.2% to gross domestic product growth (GDP) for the next decade, and that it could help to capture an additional 20-25% in net economic benefits (equating to $ 13 trillion globally) in the next 12 years.

“This is not a systemic challenge that companies are going to have to deal with, and it’s not just a challenge that governments are going to have to deal with. [V]arious stakeholders [are going to] have to work together to solve this issue,” said Krishnan. “Beyond solving the question of reskilling workers, we also need to think about agendas [and policies that] ensure women are not left further behind. If we act now, we can step into a better future, but it’ll require finding ways to solve some of the barriers and challenges.”

As always, if you come across a story that merits coverage, send news tips to Khari Johnson and Kyle Wiggers — and be sure to bookmark our AI Channel and subscribe to the AI Weekly newsletter.

Thanks for reading,

Kyle Wiggers

Senior AI staff writer

P.S. Please enjoy this video of MIT Technology Review’s Gideon Lichfield using deepfake technology provided by Pinscreeen at the EmTech MIT conference.

https://twitter.com/techreview/status/1174316798700924929?s=20

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CRM Watchlist 2019: The big engines that could (Part 2B, Microsoft)

August 14, 2019 CRM News and Info

So far, so good

I’ve gotten five of the 13 reviews done now. Here are the links. Interest seems to be high in them, despite their insane length.

If you have any thoughts about any of them you’d like to share, please feel free to email me at paul-greenberg3@the56group.com. I’ll respond. Even if you’re mean to me.

Once again…to be clear

I am assuming — mistakenly, I hope — that this is the first Watchlist winner review you are reading. If it’s not your first, forgive my repetition; if it is, pay close attention, please.

You probably know that I’m an analyst and that I am vendor-agnostic when it comes to the competitive wars that the enterprise vendors (or small business vendors) fight amongst themselves. I don’t care who wins. This is a big marketplace – and includes CRM, CX and customer engagement technology. Gartner recently released the 2018 numbers for what they call CRM/CX market. They peg it at $ 48.2 billion in 2018 and growing 15.6% that year alone (from 2017), and expect double-digit growth rates through at least 2022. These vendors inhabit the largest business software market in the world. Thank you very much.

That said, part of my living is earned as an adviser to these companies, who are all apprised of the fact that I don’t care who wins those competitive wars. For example, at the moment, of those I’ve written about, Salesforce is a client, Microsoft is to be, Oracle might be, and SAP, Adobe and Infor are not. Client or not is irrelevant to me as an analyst. If public criticism is deserved, they will get it, client or not – and my body of work attests to that. That said, this is a post about one of the 13 who won the Watchlist. So it is going to be biased to the positive –because they won. And there were good reasons they won. While not perfect, this company has had the impact to be a meaningful player in the markets that they address. So, please make note: Don’t tell me this is one sided as if that’s a bad thing. It is one sided. It’s why they won the Watchlist. But the purpose of the post is not just to highlight their wonderfulness, but to show how they are thinking and acting as a company and how that can and does impact the markets and the world – and some of the things they have to do to sustain that impact.

So let’s dive in.

The Vision

Microsoft, like its five brethren before it, is complex entity to dissect when trying to understand why they won the Watchlist. To that end, some of what I am going to be discussing occurred this year, outside the purview of the Watchlist entry but within the context of how Microsoft is executing their strategy to sustain their efforts and impact over the years to come – and interestingly enough, it is entirely consistent with both their vision and their strategies – for the most part.

To actually capture Microsoft’s intent and game plan, its important to understand Satya Nadella’s vision for the entire company. I outlined it in a detailed way – at least what I inferred at the time (it has proven to be right since then, ahem. Thank you very much) but to reiterate it if you don’t want to read the entire post, it’s this:

Microsoft wants to be the mission-critical infrastructure for all 21st-century business.

I was very careful how I crafted that sentence. Because, to understand what Microsoft has been doing since the rise of Satya Nadella in 2014 you will need to understand what that sentence means. The company is aimed squarely at no less than that objective – and are progressing with varying degrees of success. This effort includes reorganizing the company, building strategic alliances with other vendors among them competitors. It adds the production of the needed market-aligned products and services and encompasses their huge but low-profile philanthropic efforts. Let’s construct the building now that you’ve seen the blueprint. There is no way in anything less than another book (and I’m NOT writing it) that I could show you ALL of it, but I am covering the germane parts for this – the products/platform, the partners/ecosystem, the philanthropic efforts,

The Products/Platform

To understand anything that Microsoft is doing, unlike many companies, you have to understand its products. Keep in mind, that goes counter to almost everything I both generally believe and ever say about any company, but in this case, it is justified. In order to understand its products and the role they play individually in Microsoft’s strategy, there is one particular one that is the single most important thing that Microsoft produces, and that is their Infrastructure as a Service (IaaS) offering – Azure (pronounced, depending on who you ask, AZ-ure or more exotically, Az-URE. I lean to the former.). Satya Nadella said it well on the Q2 2019 earnings call (as reported in this excellent ZDNet post on the state of all the cloud providers by Larry Dignan) :

“From a mix of services, it starts always with, I would say, infrastructure. So this is the edge and the cloud, the infrastructure being used as compute. In fact, you could say the measure of a company going digital is the amount of compute they use. So that’s the base. Then on top of that, of course, all this compute means it’s being used with data. So the data estate, one of the largest things that happens, is people consolidate the data that they have and so that they can reason over it. And that’s where things like AI services all get used. So we definitely see that path where they’re adopting the layers of Azure.”

This is not only his sincerely held belief but is the foundation of all things Microsoft from their other products Office 365 to more germane to the Watchlist, Dynamics 365 to their vision, mission and strategy. Azure is the framework and the centerpiece.

This is not an unfounded premise. Azure is becoming the provider of choice for enterprise scale businesses. In 2018, the JP Morgan CIO Survey, with 154 respondents, said that Microsoft Azure was their trusted IaaS choice, with 27 percent with AWS a distant second at 12 percent. Among other reasons, because it is the most secure of the cloud infrastructure services according to most reviews. I’m not here to debate Azure, AWS and Google Cloud. That said, if the JP Morgan Survey holds true, then Azure is generally the most trusted by businesses and its 90 percent growth from 2017 to 2018 shows that the trust is yielding results. Microsoft is making Azure not only their central offering but their polemic. It is at the core of how they think about the technological underpinnings and frameworks for 21st century business infrastructure. Azure is the theme of all those enabling products Microsoft provides, all those tools that Microsoft offers for developing on its platform and all the strategic partnerships that Microsoft engages in. It is Microsoft’s Olympus (my own overblown metaphor) – the dwelling place of the larger than life outcomes that Microsoft claims to provide if you respond to their ministrations. In order to help me out, on July 19, The Gartner Report for Cloud Infrastructure as a Service, Azure was named one of the three leaders sitting between AWS and Google Cloud. Thanks Gartner.

Azure may be the technological framework, but the toolkit and workbench for the realization of this vision is the Microsoft Power Platform.

powerplatform CRM Watchlist 2019: The big engines that could (Part 2B, Microsoft) — Microsoft Power Platform

This simple diagram shows a well-integrated, tight knit but actually complex platform. What makes this even more remarkable is that if you look at Microsoft’s platform offering of several years ago, it wasn’t really very good. At best, it was rudimentary. Less nicely, it wasn’t a platform at all. Just a crude set of authoring tools with some okay to good analytics. Think Dynamics XRP. I wasn’t a big fan. Other large vendor platforms – notably Salesforce’s Customer Success Platform, were far better than what Microsoft had. Additionally, for the past several years, significant vendors, were feverishly moving ahead on their own platform plays including Oracle’s soon to be released CX Unity, Pegasystems’ Pega Infinity (more in an upcoming post) and bpm’online’s Studio (same), among others, which might have forced Microsoft’s hand in bringing their best platform efforts forward with a rapidly accelerated time to market. That also might be idle speculation on my part. Doesn’t matter, really, because whether or not that’s the case, the Power Platform is a high-quality real deal platform on par with anyone’s and in some areas e.g. Power Apps, superior to what’s else is out there.

PowerApps: In the hands of the people

PowerApps is a “citizen apps builder” at its best. Citizen apps builder is a euphemism for a set of tools that allows non-technical people, more likely than not line of business folks, to build applications without the development skills normally needed to do so.

There have been similar toolsets over the years. In the earliest days – two decades ago, the misunderstood Lotus Notes was a prime example of this kind of toolbox. I remember the Notes practice lead at Coopers and Lybrand (C&L) (yes, it was THAT long ago when C&L was separate from PW – if you actually understand what I just said…you are OLD!) told me that Notes was so versatile they had a massive technology management problem at C&L. Over 2000 Notes applications developed by the line of business staff members had been deployed. Say what?

Authoring tool kits such as those provided by PeopleSoft (People Tools) required a more sophisticated power user to create anything of value, but as time passed, these kind of tool kits became easier and easier to use and more and more valuable to have. PowerApps , in my experience, is without a doubt the most user-friendly, elegant version of these kinds of tools. I watched CRM and CI powerhouse Brent Leary and enterprise tech guru Josh Greenbaum build simple working apps in about 25 minutes at a Microsoft Analyst Day in 2018. The kicker? PowerApps is free with Office 365 though to get access to the whole basket of data sources (over 200 I think), you do have to pay. A limited amount of data sets come with the toolset itself.

The other two components of the platform – PowerBI, their analytics engine and Flow, their workflow and process management engine — deserve coverage, but in the interests of space I’m not going to cover them here. Go to the links to learn more about them.

Office 365 powered by Windows…uh..10…mostly

But it’s not just Azure, which provides the framework, nor just the Power Platform in Azure’s nuclear core, but it’s also the transformation of Office 365 from a productivity suite to a unified communications hub – a comms layer that helps their customers communicate with their customers and each other and allows the products communicate with each other. One big gabfest of a hub.

If I ask you what does Office 365 consist of, you’re pretty likely to tell me Word, Excel, PowerPoint and if you’re a bit more knowledgeable you’ll add Access (though why that still exists is beyond me). However let me list what else Office365 provides as part of the subscription you more than likely have.

Word
Excel
PowerPoint
Outlook
One Note
One Drive
SharePoint
Teams
Access (again. Why?)
Exchange
Project
Visio
PowerApps
Bookings
MileIQ
Invoicing
MyAnalytics
Outlook Customer Manager
Planner
Publisher
Skype for Business
Staff Hub
Stream
Sway
To Do
Whiteboard
Yammer

That’s 27 products which are clearly not just a productivity suite, but encompasses productivity, communications, authoring, and content distribution – among other things. Couple this with Windows as the by far most prevalent desktop (and also, business) operating system at 87.48 percent (Net Marketshare: July 2019) and there is no question of Microsoft’s dominance in this space. As they increasingly focus Office 365 around becoming a unified communications hub that is embedded/interwoven with Azure and their operating system runs this hub, they are going to be increasingly difficult to dislodge by anyone attempting to capture the business market. At some levels, though I am stating this very cautiously, they are already mission-critical to many companies, with Office 365, Windows 10 and increasingly with Azure as the backbone. One of the critical missing pieces though is their complete lack of presence in the mobile space. If you are providing a unified communications hub, a lack of mobile presence can (and likely will) derail you. That said, at the moment, Microsoft, led by Satya Nadella, is making Microsoft products, applications and systems interoperable with IOS and Android, rather than trying to take over the mobility market with Windows Phone. This strategic integration strategy flows to more advanced communications such as conversational interfaces. Cortana, which is not really competitive in the CI space, is now (or soon will be) interoperable with Alexa.

So if we can step back for a second (watch the ledge), we see an Azure backbone/infrastructure; an Office 365 communications layer, a Power Platform, well, platform and the authoring tools through Power Apps and workflow tools (don’t look back, this is the first mention) in Microsoft Flow. But it still needs the business applications that will enable the activities and foster the outcomes in the customer ecosystem. Those are where Microsoft Dynamics 365 and the partner applications come into the grand scheme.

On to Dynamics 365

Ultimately, it’s the customer-facing business applications that are my personal sweet spot/wheelhouse – you pick the term. That means what has been called over time, MSCRM, Microsoft CRM on Demand, Microsoft Dynamics CRM and Intelligent Customer Engagement (ICE – though given the current political climate, that’s not the best acronym) and now of course Dynamics 365. I am speaking about their Sales, Marketing, and Customer Service applications/solutions. What makes this piece of the vision’s implementation super interesting is that:

By Microsoft’s standards their business applications are a small part of their business (about an estimated $ 2 billion plus. Can you imagine – that’s small?). According to the 4th quarter 2019 (FY) results (their fiscal year ended June 30, 2019), Microsoft’s total revenues for were $ 125,843,000,000 (roughly).
Yet if the vision is true and it directs their strategy, business applications’ importance far outweighs its revenues. How do you become the mission critical infrastructure if you can’t run the business or at least enable the running of the business? The answer? You can’t. So you have to have the business applications to support that. This gives that unit a huge strategic importance in the execution of Satya Nadella’s vision for the company.
This led to a dramatic repositioning of the business unit. At last check, the unit itself was being run by James Phillips and Alysa Taylor. It rolled up under the Microsoft Cloud business unit run by Scott Guthrie as part of the Azure efforts. Could this have changed? I don’t see anything, nor I have heard anything to suggest it has. This org structure (rolling up Biz Apps under the Cloud unit) supports the overarching game plan that I’ve mentioned three million times or thereabouts in this post.
What increases both the complexity and the value of the business applications is the partnership with Adobe. What it means for biz apps (as Microsoft likes to call it) is that Adobe’s Experience Cloud which includes their B2C enterprise grade marketing automation solution is what Microsoft positions at the enterprise level. With the Adobe acquisition of Marketo, its going to be interesting to see if Microsoft uses that as their B2B enterprise-grade marketing automation platform/solution or they go with their own, which at its current level of development is suited more for the mid-market.

What Microsoft is wisely doing on the customer facing business applications side is doubling down on AI and machine learning. This effort aligns them with the markets that they are attacking and at the same time, provides them with some differentiation in those markets. One very good example that I was lucky enough to see at Microsoft’s Analyst Forum in February 2019, was an AI for Sales demo from the product team which is led by the always amazing Dina Apostolou. This was a mature product that was both valuable in the insights it provided. It still managed to be easy to use too. Rather than explain it, just watch this 2018 demo of the product (which I am sure is more advanced now than then) given at Microsoft Ignite.

As you will read shortly, they aren’t limiting their AI and machine learning efforts to product evolution or customer engagement initiatives – though they have ample examples of that. They are focused on much more. In due time, you will read this. But before we get to that, another strategic component of their outlook and their execution of the vision…

The Partners/Ecosystem

One thing that has characterized Microsoft even pre-SN (Satya Nadella) was that they were ecosystem thinkers. To be clear when I something an ecosystem, I mean a business ecosystem, as somewhat clumsily but nonetheless accurately defined by James Moore in his 1993 Harvard Business Review. article, “Predators and Prey: A New Ecology of Competition.” Okay, that’s a long time ago, but then again, so was the invention of the wheel and your car still has them – and you still drive it.

The definition:

“An economic community supported by a foundation of interacting organizations and individuals—the organisms of the business world. The economic community produces goods and services of value to customers who are themselves members of the ecosystem. The member organizations also include suppliers, lead producers, competitors and other stakeholders. Over time, they coevolve their capabilities and roles and tend to align themselves with the directions set by one or more central companies. These companies holding leadership roles may change over time, but the function of ecosystem leader is valued by the community because it enables members to move toward shared visions to align their investments and to find mutually supportive roles.”

Microsoft’s partner ecosystem has been the best reflection of its thinking and in the context of its current vision, has been mission critical to the effort. In its very earliest days it was 54 programs (this is over 15 years ago) and it was consolidated to one program. This got out of hand and once again in 2017 it was consolidated once again to a single program. Even in its more fragmented days, one thing that Microsoft understood was strategic partnerships. They saw all partners – strategic or not – as part of a partner ecosystem, not just a company doing adjacent business. That meant, if done right, that the partners were companies that filled gaps or extended existing domains that were meaning for customers. It could fill a market space; for example a company like ClickDimensions which has marketing automation for the mid-market and is built on the Microsoft platform. It could be missing capability; for example Episerver who has web content management, some marketing and a solid ecommerce platform (for the midmarket only though). It could be a company fulfilling core functions better than Microsoft itself. The obvious example of that is Adobe whose Experience Cloud fulfills both B2C enterprise scale marketing and literally the ability to build consumable experiences. As we will see below, the strategic partnerships on the one hand fill important and specific holes in the Microsoft ecosystem AND they are fit well into the fulfillment of Microsoft’s all-encompassing vision. In the current parlance, these are purpose-built partnerships. For the want of a better term, Microsoft is an ecosystem thinker.

The Partnerships

Part of the corroboration of the grand plan can be seen in the flurry of recent go-to-market partnerships that Microsoft signed. More than 12 months ago, they and Adobe came together in what I call the GARP – the Get-A-Room-Partnership where the intimacy and joint execution between the two companies in so many areas is so close, it’s almost embarrassing. The purpose, value and reasons for the resounding success of that partnership can be found in a post I wrote in 2018 called Microsoft, Adobe Get a Room Partnership, Creating a New Category?. My conclusion, BTW, was that it wasn’t creating a new category, but it was a successful, intelligent and paradigmatic strategic partnership.

That has been around for a bit but in the last two months Microsoft signed three more BIG partnerships that, in conjunction with the Adobe partnership, are significant steps forward in the achievement of Microsoft’s vision. A brief look at each of them.

Microsoft, AT&T 5G Partnership – This one makes total sense and possibly $ 2 billion for Microsoft. This one is as much a deal as it is a partnership. Described in the Geekwire post this way:

“The two companies today announced an “extensive, multi-year alliance” that will see Microsoft become AT&T’s preferred cloud provider for non-network data. As part of the deal, Microsoft will help AT&T with its 5G push by bringing processing capabilities closer to the actual devices through edge computing.”

This helps Microsoft by adding the speed of 5G (with AT&T as the preferred provider) to the unified communications hub and to Azure as a comms layer. Very, very smart…and potentially lucrative for Microsoft.

Microsoft Oracle Cloud Partnership – This is perhaps the oddest, given the competitive nature of both companies who could compete (and often do) in multiple horizontal and vertical ways. The concept is interoperability via a direct connection between the Oracle Cloud and Azure. I think it’s a tacit admission by Oracle that Azure holds court in the enterprise space and Oracle is wise enough to recognize that there is an advantage for Oracle to be able to run its enterprise applications via Azure and Microsoft recognizing the depth of Oracle’s penetration via its apps and its database (still) in the enterprise. Smart and though starting out in a limited way – meaning between one Oracle data center (in Ashburn VA) and Azure’s U.S. Eastern node – there is lots of room for expansion.

Microsoft ServiceNow Highly Regulated Industries Partnership – This is a gateway strategic partnership for both companies. It has a narrowly constricted initial focus – Azure for ServiceNow in highly regulated industries such as (their words not mine) public sector – which technically is the regulator as much or more than the regulated – and for Microsoft using what appears to be the ITSM product (less clear on CSM to me) to handle employee focused workflows across Microsoft itself. However, from what I have gathered from both sides, this is only the beginning of what both hope to be a more expansive strategic partnership with much greater and wider scope over time.

Microsoft & Sony Partnership – This one, with its Memorandum of Understanding (MoU) signed in May, is interesting because it is tying the consumer side products of Sony (PS4 possibly?) to Azure. All the others are related to enterprises. This one, consumers. Given the Xbox and PS4 competition, this is particularly telling – and a reflection (along with several of the other strategic relationships highlighted here) of Microsoft’s Azure “coopetition” efforts. Smart thinking. Here’s a paragraph from the MoU:

“Under the memorandum of understanding signed by the parties, the two companies will explore joint development of future cloud solutions in Microsoft Azure to support their respective game and content-streaming services. In addition, the two companies will explore the use of current Microsoft Azure datacenter-based solutions for Sony’s game and content-streaming services. By working together, the companies aim to deliver more enhanced entertainment experiences for their worldwide customers. These efforts will also include building better development platforms for the content creator community.”

Each of these has a place in the Microsoft ecosystem, but, even more germane, Microsoft is tying all of them into Azure IaaS and ultimately, all the frameworks and services Azure provides (IoT etc.) – which is a reflection of the enormous – and likely successful – bet that Microsoft is placing on Azure as the IaaS for 21st century business.

Doin’ Good, Just Fine, Thank You

The other pillar of their victory – and despite a lack of much fanfare – their company – is the extraordinary scope of their corporate social responsibility efforts. The 2018 numbers overall speak for themselves:

$ 1.4 billion in software and services donations to non-profits
$ 169 million in cash from the company (2017); $ 158 million from employees (2018)
$ 3 billion worth of business with minority, LGBTQ, women-owned, veteran, and disabled owned businesses
$ 30 million cash for first responders
100% score on carbon footprint reduction

This is only a partial list of their enormous efforts. They are #8 in the world in the most philanthropic companies according to CareerAddict.com a site I never heard of but at least is ranking these companies from the standpoint of most total cash donations, so, presuming some accuracy on the dollar numbers, I have no reason to doubt the validity of the site.

Aside from the obvious, why would their philanthropy matter to the Watchlist victory or more importantly, to the achievement of the Microsoft overall vision? After all, if I’m right, that’s a business vision not a “do-gooder” vision, right? Yes, but wrong. Aside from the genuine desire of the company’s principals to just do some good with the resources and power they have, it is increasingly obvious that the customers and future customers of businesses are making their decisions about their willingness to transact and interact with these businesses based on more than just good products and services. Again a section from my new book (to be candid, I’m not really trying to promote the book, more than I’m lazy about doing new research to justify my point. Though feel free to think I’m promoting the book if you like and buy it):

“Self-interest, when it comes to businesses and work, goes beyond just making sure that companies satisfy customers with the right products and services.

To highlight the importance of understanding what the idea of self-interest means both individually and at scale, ladies and gentlemen, I enter into the record the 2015 Cone Communications/Ebiquity Global CSR Study. For those of you not aware of what CSR means in this case, it is the abbreviation for “corporate social responsibility,” not “customer service representative.” In other words, it refers to what the company is doing to support the world, or at least the community, that it resides in. The data Cone gathered shows how much deeper self-interest truly goes when it comes to engagement with customers. Here are some of the more meaningful numbers.

Companies that support social and environmental issues

have a more positive image to consumers (93 percent)

are more likely to be trusted by consumers (90 percent)

engender more customer loyalty (88 percent)

than companies that don’t.

This has a direct impact on the company’s bottom and top lines. The study found that customers would be

more likely to pay more for an environmentally and/or socially responsible product (71 percent).

willing to reward a socially/environmentally responsible company by buying its products over others and/or speaking about it positively to others (31 percent)

willing to punish a company that was clearly operating counter to environmentally/socially responsible efforts (19 percent)

willing to buy a product from a relatively unknown company if it were shown to be operating socially/environmentally responsibly (80 percent).

In 2017, Forrester Research, in their Forrester Data Consumer Technographics, North American Omnibus Survey, found that 52 percent of all customers evaluate company values when making a purchase—up a whopping 21 percent over the prior year.”

But one of the reasons that sets them apart from any other companies that are doing their philanthropic good deeds, is Microsoft’s AI for Good, initiative, their effort to use artificial intelligence (consistent with their overall increasing focus on AI and ML) in service of humanity.

This is a truly innovative approach to doing good (no catcalls from the competitive peanut gallery please), and they are committing $ 115 million to carry it out. At the UN ITU AI for Good Summit, this past May, in an interview, Microsoft’s Jean-Philippe Courtois, EVP and President, Microsoft Global Sales, Marketing & Operations, laid out the strategy and approach (note their commitment, like pretty much all larger tech companies, to the UN’s 17 Sustainable Development Goals) for AI for Good:

“Our mission is to empower every person and organization on the planet to achieve more, and participating in the conversation around AI for Good is essential to realizing our shared vision. The pace of AI innovation we see today provides a unique opportunity for us to take on society’s greatest challenges at scale. Equally important is the responsibility that we share across academia, civil society, industry and government to ensure we are taking a principled approach to the creation and use of AI to ensure it is used in a way that fosters trust and enables its broader adoption so the potential of AI can be realized. We recognize the United Nations’ Sustainable Development Goals are addressing urgent and complex societal issues that are bigger than any one organization. That’s why Microsoft is partnering across public, private and nonprofit sectors to deepen and broaden our collective impact…”

Notable – and laudable.

They are dividing the AI for Good initiative into four pillars. They are (with brief explanation):

Earth – Reduced somewhat inadequately to its fundamentals, this is their sustainability initiative. It covers climate, agriculture, biodiversity and water by providing grants and APIs to monitor, model and manage sustainability in those four areas. Example: Vulcan Earth Ranger, a software platform development project that monitors activities in protected areas via AI and sensors. This can materially reduce wildlife poaching in African protected refuges. (Grant to Earth Ranger)
Accessibility – This one is easier to explain. They provide the grants to organizations for the use of AI to develop the means to amplify human capabilities for, among other things, seeing, hearing, skills training, communications and social connection. Example: AI driven app called InnerVoice to support communications skills for those on the autism spectrum and with other learning disabilities e.g the association of communications and facial expressions. (Grant to iTherapy)
Humanitarian Action ($ 40 million over 5 years) – This is their AI initiative to support refugees, children, disaster response and prevention of human rights violations. Example: Working with the Asylum Seeker Advocacy Project (ASAP) to provide legal aid to those trying to escape persecution in their home countries. Using speech-to-text, Azure database and prioritization algorithms to make sure that legal assistance is available when it is most needed (Grant to t Asylum Seeker Advocacy Project)
Cultural Heritage ($ 10 million over 5 years) – This will harness the power of Microsoft AI to empower people and organizations to preserve and protect the heritage of people, language, places and historical artifacts. Example: Taking the entire 1.5 million piece collection of the Metropolitan Museum of Art and digitizing it so it is accessible to 3.9 billion internet connected people over the world. (Grant to Metropolitan Museum of Art). BTW, this is my favorite museum in the world. I LOVE this place.

However, even with all this – The visionary master plan, the plan’s execution via their product development and strategic partnerships; their corporate reorganization, and their commitments that go beyond just providing products and services to focus more on their company’s role in supporting good works on the planet – there are several things I would suggest that they do to make sure that they continue to impact the world even better than they are now.

Three Things to Consider

At the risk of being boring, once again…These three suggestions are three of many. For the purposes of space, and my desire to remain mysterious and cagy about all the things that I would suggest Microsoft (as well as all the other Watchlist winners) do, there are only three here.

Ecommerce, ecommerce, ecommerce – Let me be clear again on ecommerce. It is NOT the fourth pillar of CRM. There is also no need for it to be that. It is a substantially important piece of any customer engagement (or if you must call it this, CX, which I won’t call it) platform or even solution set. Where CRM (sales, marketing, customer service) has become the operational core of a larger customer engagement effort, ecommerce has become the transactional core of that same effort – and a source for both transactional and behavioral data on individual customers. So, while not CRM, the value is manifestly obvious. Microsoft does not have ecommerce. They have partners who are ecommerce providers, notably Episerver, but none of their own – at least not yet. Think of it this way – SAP has hybris, Salesforce has Commerce Cloud, a pretty well stitched version of Demandware and Cloud Craze; Oracle has Commerce Cloud who’s distant relative is ATG (which I believe is still offered on premise), but fundamentally retooled in the cloud, Adobe has Magento. Microsoft has….um, uh, nothing. This is the time before it gets too late to build, buy or partner but to do something.

Talkin’ the Product Talk – It’s fascinating (at least to me) that Microsoft actually has a rough hewn corporate narrative around their vision. But their product stories, of uneven quality, beyond the Azure story, don’t really strongly support that corporate narrative, often leaving some serious disconnects on how this all fits in. For example, in your view, readers, if you buy the visionary picture that Microsoft paints (see above) then how does business applications fit in – and how does business applications given the big picture intersect with Office 365 – and the cloud? How does consumer products like X-box play into this narrative – or do they? That’s just business apps. We have the platform, PowerApps, which is embedded in Office 365. The “new” Office 365 which is no longer just a productivity suite but a unified communications hub. That one seems apparent, but where is Microsoft’s product marketing and stories around that concept – if they truly believe that. Lots of Azure activity, and a visionary corporate, rough, not totally complete corporate narrative, but no stories and focus on how the products and platform fit into the ecosystem. To add fuel to the flames, here, the conversations that Microsoft has with its public (not just customers) leans to product focused rather than outcomes focused – and in this day and age, that is not just a nice thing to do but a need to do. Outcomes, what the customer will accomplish that he/she needs to accomplish for both the company they represent and for themselves. How do Microsoft products support those outcomes? All part of what needs a lot of tweaking but has some structure already.

Open the Pipes…Where are the hydrants? Microsoft’s relationship to LinkedIn, their $ 26 billion baby, remains…peculiar. On the one hand, the acquisition of all that business-related individual professional data, makes the acquisition valuable. On the other, their continued approach to LinkedIn, keeps the value well south of the acquisition price. To explain, when Sales Navigator, LinkedIn’s dopey little SFA product was created (long prior to the acquisition), it was also used as the justification for closing off what had been an open firehose of LinkedIn data that was being used by companies across many industries to develop applications, solutions and profiles that were helping to personalize the interactions they had with customers. LinkedIn was dominant in part because it was so open with its plumbing and data.

One would think that if Microsoft was to be consistent with its global game plan, that the firehose would be opened and the hydrants flowing again but…no. Instead, they have actually created a very confusing model for access. They’ve created this product bundle called Relationship Sales which is by their own hand, “Dynamics 365 for Sales Enterprise + LinkedIn Sales Navigator” for $ 145.09 per user per month. Along with the $ .09 as part of the price, this combination looks ridiculous. LinkedIn Sales Navigator is described as an SFA tool and it thus looks like you are buying two sales tools. No one in their right minds would buy two SFA tools to do what one could do. What they are really buying is access to the firehose and, thus, the ability to use that data. So, here’s what I suggest, making life simple for everyone, including Microsoft. Sunset Sales Navigator. It was NEVER a good product and there is nothing that will make it good for SFA. What it has is the pipeline – so sell that as an add-on service for Microsoft Dynamics 365 for Sales Enterprise – and make access to the firehose also a standalone offering, even if you have to charge for it (though cheaper if you buy Dynamics 365 Sales). Don’t make it exclusive to an otherwise useless product. With AI for Sales – a brilliantly executed product and the analytics present in PowerBI, or some other way since there are many smarter than me on this, provide what is missing in Dynamics 365 Sales to optimize LinkedIn firehose use. Get rid of the Relationship Sales bundle. This plan aligns far better with the vision, opens the firehose back up, and can be a revenue stream for Microsoft without confusing everyone with two SFA packages in the same bundle – one excellent, the other miserable. It also makes the acquisition worth the cost – and then some. Though, to be fair, it was a good acquisition. regardless.

Microsoft is clearly more complex than most and thus I had to take it well beyond the customer-facing business applications they offer. There is no way to think about Microsoft properly without understanding their grandest vision and their willingness to spend the time, money and labor power to invest in its accomplishment. To their credit, part of what goes along with being part of mission critical infrastructure is their commitment to use Azure and also their vast financial resources to accomplish good in the world. Thus, my friends, they were a winner of the CRM Watchlist 2019 due to:

Their tightly constructed and well-planned vision;
Their ecosystem and platform thinking;
The amalgamation of products/services/infrastructure aligned with the needs and interests of the market they serve;
The reorganization of the company around the achievement of the vision, with Azure at the epicenter
The strategic partnerships to fulfill the needs of their ecosystem and their business vision AND
Their philanthropic commitments to accomplish their efforts for social good.

Whew. A lot, a really successful effort and yet, more to do.

Okay, I hope that this worked for you. If not, please comment. If so, please comment. Send me the comments if you prefer not posting them to: paul-greenberg3@the56group.com and I will respond. Ask anyone who has sent them (though I won’t tell you their names). I really will.

Next up: Pegasystems and bpm’online

Note: If you are interested in participating in the CRM Watchlist 2020, please email me at paul-greenberg3@the56group.com and ask for registration form. Remember, you will have to qualify to participate. There are criteria. Also, if you are interested in reading my new book on customer engagement, The Commonwealth of Self-Interest: Business Success through Customer Engagement (2019), please click here.

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CRM Watchlist 2019: The big engines that could (Part 2A, Oracle)

July 15, 2019 CRM News and Info

Needed to say (if it were needless, I wouldn’t say it), I’ve gotten my reviews of four of the thirteen 2019 Watchlist winners done. I’ve had a lot of kudos which I appreciate and one complaint that one of the posts was too wordy – and the content was insufficient – which I would have appreciated if there had been anything constructive in the complaint – meaning, for example, what content would that person have liked to have seen – but there wasn’t, so I didn’t. Verbose is what I have always been and will continue to be, like it or not. But I do take complaints about lack of content seriously – and I need more feedback than that to understand what is missing. So, the one useful thing sparked by that gentleman’s complaint is that I am asking you to please let me know what content you would have liked to have seen if you think I am missing something. Please. You are welcome to either put it in the comments area, in the comments area on the LinkedIn share or email me at paul-greenberg3@the56group.com.

So, like it or not, here are the (wordy) posts on the four winners so far.

Now on to the next mega-giant – Oracle.

Oracle

Originally, this post was going to cover both Oracle & Microsoft. Once again, I either changed my mind or reneged on my promise – depending on how kind you want to be toward me. The reason is that this particular post is very long, and it was, as simply as it may read, incredibly complex to piece together. It also has taken me more than two weeks to write. So, I decided that rather than wait another two weeks plus, it would be wise to get this one out. That also allows me to focus exclusively on Microsoft for however long it takes me to write that one.

And once again – a caveat. This is a post on why Oracle won the Watchlist so, while it will be as always entirely honest, it, like all the Watchlist winner posts, is slanted to the good – they WON the Watchlist – and there were good reasons for that. I presume I can’t be any clearer. So please don’t waste cycles calling this one sided. It is. Its positive even when it’s critical. They won the Watchlist. Won it. Didn’t lose it. Won…it.

Oracle’s Submission

Let’s begin this with some kudos, huzzahs, hoorays, and congrats. Oracle actually sent me what I consider the most complete and best written questionnaire submission in the history of the Watchlist. Over the more than a decade since I began the Watchlist (12 competitions over 13 years), I only had awarded one company an extra bonus point (or partial point) for the brilliance of the submission. That was Ernst and Young. This was the second one – and it surpassed the other one in its completeness, attention to detail, and just quality of the writing. So, thank you Oracle for making what I do better. The submission was an A+++.

Evolution to Victory

In order to understand Oracle’s present success, it’s necessary to understand in some detail, Oracle’s past. It isn’t an entirely pleasant past – to put it nicely but it also will show you just how far Oracle has come – especially in the last two years or so. It also was what it was – and regardless of what was bad and good about it – it shaped a lot of what Oracle is as a company.

So, here is the (unvarnished) path Oracle has taken to get to the impact it had in 2018, is having in 2019 and will have beyond.

Oracle is Oracle. It is a nearly forty billion dollar mega-company with a dramatic presence in the technology world, who’s most visible presence is a larger than life guy who is one of the world’s richest human beings. On the yin side of the yin-yang, It has been an incubator (of sorts) for companies like Salesforce and some of the most important, influential business leaders in the world launched their careers in its ranks. All to its great credit.

However, on the yang side, it has been an historically difficult and very complex company and, in the past, equally difficult to work with. For many years it seemed to be charting a course that was more aligned to its own legacy and needs than the markets that it was supposed to address and the new markets that it had to address. It also seemed like they didn’t give a damn what people thought of that either – and proceeded to act accordingly. Even with the world changing at the pace it was, at the time, Oracle wasn’t. To be clear, please note I’m using the past and past perfect tenses.

Despite the sharp, sometimes acrimonious nature and history of its culture, there were two things that Oracle could always do really well.

Most important, build products that competed with anyone’s in the market – and were as good or better than those they competed with. Oracle Sales Cloud and Service Cloud – natively built (with acquisitions tucked into the solution) – had some of the strongest feature sets and tight integration of any company’s I had ever seen. I remember a few years ago seeing the mobile treatment of their Sales Cloud applications. At that time (3 years ago), it had the best navigability I had seen in a sales mobile app.
Second, acquire companies with products that were equally as good – and not only competed in the market but were market leaders (Eloqua being a really good example of that).

But in the last several years, as Oracle began to grasp the cloud and its increasing ubiquity, it began to change and shake off decades of dust and grime and reinvent itself a bit at a time. Even as some of the sharpness of its culture persisted, Oracle still could build products – competitively strong cloud-based products – and they made acquisitions that were very well aligned with what they needed to do to foster that alignment. Acquisitions from 2011 on such as RightNow (2011), Collective Intellect (2012) Eloqua (2013) Compendium (2013), Responsys (2013), Big Machines (2013) BlueKai (2014), Maxymiser (2015), NetSuite (2016) and DataFox (2018) are great examples of acquisitions that provided them with the technology they needed to foster cloud-based ecosystems. That only touches on what they’ve acquired. For example, Eloqua was the foundation for the Oracle Marketing Cloud. That offering was extended by Compendium and Responsys, giving them a Marketing Cloud that competes well as one of the best in the market in 2019. Collective Intellect and Blue Kai were among the fabrics stitched by the incredible Tara Roberts (currently VP of Adaptive Intelligence) into the Social Cloud which serves as the core social communications layer across Oracle CX clouds. (For a general list of Oracle’s acquisitions and some detail on what they were for, here is the Wikipedia entry)

This goes to the heart of one of Oracle’s greatest strengths – their “middle management” – the only terms I can come up with for the people at the level of management that is getting what Oracle does done well. They are the ones who came up with the varying cloud-based products such as Sales Cloud, Engagement Cloud (more on this later), Service Cloud, Marketing Cloud (nee Eloqua), CPQ Cloud (probably not really a cloud as much as part of one) and even Social Cloud (again, probably not a cloud as much as a communications layer that rests cross-cloud. More on why that in a “platform-not- stack” strategy later). I can literally name about 20 people at Oracle who are for the most part VPs to EVPs who have been producing competitive and even superior products for years, despite the at times toxicity of the culture and the insular yet combative defensiveness of the company. These folks still kept Oracle CX in the game as a true competitor and influential company. They are under the aegis of the often under-estimated but industry insider well esteemed EVP of Applications Development at Oracle, Steve Miranda, who has been outstanding in his job – and is a truly nice human being – for the last nearly 27 years.

But even with this, Oracle had been crippled by their legacy – however successful it had been. They had been masters of the (relational database) universe for years and their customers attested to the value their product provided. But business applications, due to a long development time with Fusion and some misplaced priorities at the company on the global level, were not as prominent on Oracle’s public stage. Plus, they marketed and messaged as an infrastructural product company – so CX and the ERP applications took a back seat to the Oracle database products and to their hardware and later on to their Cloud offerings per se.

Yet the markets and world were evolving and due to the communications revolution of now two decades, the demands and needs of customers were changing and what that did to the technology market was force a dramatic change in how customers and companies engaged. Customers controlled the conversation. Oracle’s CX technology stack, as much as it was a stack, still was the kind of technology that business leaders needed because, if seen and applied properly, it could enable outcomes.

Value of Outcomes

So that we are clear on what is important in enabling outcomes, and because I am slightly lazy, I’m going use an excerpt of from my new book “The Commonwealth of Self Interest: Business Success Through Customer Engagement”( see below for details) on outcomes:

“When it boils down to it, the customer doesn’t care about whether your product is a good product or has a plethora of uses for a lot of different kinds of companies. What he or she cares about is how does it help their company and, equally if not more importantly, how does it help them get the outcomes that they need to reach whatever their goals are. To illustrate the point, several years ago I was at a party with a very senior executive of a big company who made some of his company’s larger buying decisions. I asked him about a software selection, since he seemed to be particularly savvy when it came to technology. I said, “If you were down to two choices, one which had a 100 percent benefit to the company and none to you and the other which was 80 percent beneficial to the company but had some benefit to you, which would you take?” His answer, with literally no hesitation was, “The second one.” When I asked why, he said, “It has some benefit to me.” This isn’t terribly surprising. Like all other human beings, your customers or prospects need to see how the products and services you provide are going to help them achieve the outcomes that are relevant to them. To that end, one of the smartest efforts at product marketing I’ve ever seen was done in its earlier days by the sales optimization business software company Lattice Engines. Their customer testimonials weren’t just about how the software benefited their customer companies, but instead were testimonials from individuals on how they got promoted because of their success with Lattice Engines. The sales approach means show the customer how it gets the outcomes that benefit the business and the individual buyer.”

In the last two years and especially with the ascension of Rob Tarkoff as EVP of Oracle CX about a year ago, Oracle realized this, and, along with other revelations, it led to important shifts in the company’s perspective, vision, mission, product strategy, marketing and, most importantly, culture.

I began getting hints about the cultural shift roughly two years ago when more than one Oracle senior leader who I was chatting with told me if I am boiling it all down into a single conversation – “Oracle is changing the way that we have dealt with customers over the years. We realize that we have been both too arrogant, assuming that we are always right about what we are saying and also too anxious to sell to them rather than provide them with what they need. So, we’ve got (institutionalized) initiatives going that are designed to accomplish two things. First, actually listen to customers and second, be willing to say no to unrealistic requests – meaning those that we can’t actually accomplish. There is nothing wrong in letting our customers know that we do want to do what they need but don’t want to promise what we can’t do.”

Of course, that is a distillate of multiple conversations with group of people with a wide variety of organizational responsibilities – which is what made me think it was more than a just public posture or an isolated condition. There was a fundamental change in the culture of Oracle underway.

Fast forward to 2018-2019, and the culture change is starting to actually show roots.

Aligning with the Market

Oracle has always wanted to be #1 in whatever it does – like any other company I’ve ever known (with one exception). That hasn’t changed and probably won’t. but in looking a bit more carefully, in Oracle’s particular case they are so intertwined that I’m going to just go ahead and describe it all in one giant gulp Though to pre-empt the musical literalists amongst you, in Oracle’s case, they are doing it by not doing it alone.

Originally, I was going to show you the transition and the roots of the change via breakdowns of the following:

Mission & Vision
Strategy – Overall and Product
Product releases
Marketing, Messaging, Naming – “The Experience Economy”

but in looking a bit more carefully, in Oracle’s particular case they are so complex and intertwined that I’m going to just go ahead and describe it all in one giant gulp.

It begins with their primary vision, framework and message – The Experience Economy. If you’re interested in the longest version of this, watch this YouTube video that Rob Tarkoff delivered at Oracle Open World in 2018. This is the studio version of that speech.

For those of you who might be unaware of it, while Oracle (and SAP) is emphasizing the Experience Economy, they didn’t create the concept. The term and the framework which has marked the contemporary landscape was actually created in 1999 by major league (Yankees-level) thought leader Joe Pine and his business bud, James Gilmore. They wrote a book called “The Experience Economy” which transformed the way that businesses looked at their interactions over time with customers. One of the great services that this book and its 2nd edition written in 2011 did for the business world was identify what I call “consumable experiences” – created experiences that can be monetized and at the same time would impact customers to support their good impressions on a larger scale of the businesses that they were interacting and transacting with. Effectively, though this might sound a bit strange, it introduced the right brain into business thinking – meaning how customers were enticed to behave and how they felt actually mattered for the longevity of the relationship between the customers and the company.

For several years after the initial impact the power of the emotional in business was lost under the weight of process and “agile” and efficiencies and black belts in Six Sigma. But with the communications revolution, the demands of the customers got personal – and the responses needed to be personalized. Thus, once again, right brained activity became paramount to business success and thus the world of CRM expanded to customer experience and customer engagement as foremost on the mind of corporate strategists – and the practitioners who wanted to see it in action – via programs, culture and technology among other things. Thus, Oracle and “The Experience Economy” – in a very compacted overly simplified nutshell of an explanation.

But this is also how Oracle was (and is) able to transform their culture to a more customer-centric one – still a work in progress – as are most companies at this point. They felt the upward pressure from customers to personalize the interactions and at the same time had the support from the leadership due to that pressure, so the adjustments occurred. This impacted the culture to the point that when defining Oracle’s mission and vision, this is how it was described in the Watchlist entry on how they communicate it and evolve their products:

We have shifted our public facing Oracle CX Cloud communications over the last 3 years from Oracle’s prior focus on technical product superiority to our current focus on customer benefit of our solutions. We have integrated this approach in our conferences, blogs, social media, press releases and press interactions, analyst communications, keynote speeches, website, etc.

It is at the core of our product development and go to market efforts. Oracle is a business that itself is in transformation (from on-premise and CIO buyer to cloud and LOB buyer). Across Oracle CX Cloud and Oracle itself, there are multiple initiatives (e.g., simplified buying, field integration, digital sales, one cloud operations, etc.) moving forward to better serve our customers.

This has also dramatically impacted their product strategy too. One story should suffice.

Two years ago, at Ray Wang’s always awesome Constellation Connected Enterprise (CCE) event, I had a discussion with Melissa Boxer and then still at Oracle, Jack Berkowitz about their strategy for AI. I asked if their approach was similar to Salesforce’s – making their AI a part of the platform. At that time, I was told “no, it’s going to be an add on for our existing solutions.” In the earlier part of this year, I had another discussion with one of my organizational favs Melissa Boxer and asked her if the AI strategy of solution add on is still the same. The answer was “well, it’s going to be both part of the platform and a solution add-on.”

This might not sound like much but it’s a big deal. It reflects a change I see all across CX at Oracle. Historically CX was a tech stack with no particularly strong story line for its purpose though, as always, there was a solid group of solutions in the stack. Now if you look at it, it looks much more like what Oracle needs to compete – a platform and an ecosystem (though with individual solutions still out there). Oracle is metaphorically moving from the vertical to the horizontal.

There are countless indicators.

For example. On June 5, 2019, Oracle announced a partnership with Microsoft to integrate Microsoft Azure with the Oracle Cloud to better serve enterprise customers. In the past, this strategic partnership wouldn’t have been even a thought much less a reality. But when you are thinking about platforms and ecosystems, rather than merely tech stacks and the ordinary behaviors around old fashioned competitive activities, this kind of alliance makes sense because it serves the needs of the customer – providing an enterprise level company with the cloud infrastructure it needs to function in a unified way – rather than disparate systems trying to communicate. It also is curated. In this case, that means practically both Oracle and Microsoft are sitting down and figuring out what in their systems already communicate effectively for the present and the future and what they will need to work on to make the communications more effective or at times even initially effective at all. But they are working together to make this happen. (More on the partnership initiative side of this in the Microsoft section of this post below.). In the past Oracle wasn’t devoid of partnerships but at the same time, they were a “go it alone” company for the most part – and Microsoft was more on the “go at it” list than the “go with it” list.

Overarching – Ecosystems

The customer-engaged ecosystem starts from the premise that the customer is seeking both direct and, if possible, related value from the brand they are interacting with. Is that an ironclad premise? No. A company needs to work with their customers to find out what the customer expects or, if not expects, would like from the company. The customer-engaged ecosystem is working from the outside in. It is a business that is defining its strategy around the idea that the customers of the company need to be engaged with that company and trust that company and, most importantly for the purposes of thinking around an ecosystem, get what they think they need from that company, even if the company doesn’t produce the products or services directly that the customer is seeking. I say that with a major caveat, though. The model has constraints. The most important one is that no one company can provide everything that a customer thinks they need. No ecosystem built around customer needs is built around all of them but, instead, is built around what the company that parents the ecosystem believes that they can provide—when it comes to both the resources and the company’s narrative.

Breathe in, Let it Go

Let’s stop for a second and take a breath. I hope that I’ve established what a significant thing that this $ 40 billion entity has done by aligning itself with the markets, the world at large and the people it serves. It’s impressive. Making a realignment away from their own internal legacy to a more customer-focused effort, gives them an opportunity to have impact at levels that they haven’t had to date – despite their formidable history – but of course that is dependent on making some tweaks and doing a few things that they haven’t done yet. Before we get to some of them, though, we need to talk about the Oracle CX product portfolio – in part. There are two that I am going to focus on that are interestingly strategic, for different reasons. Some of the missing pieces will be pointed out in the Three Things to Consider part.

The Products are the Platform?

A blanket statement. Oracle’s release of CX Unity shortly will be their most important CX release in the last several years.

To understand why I would say that, take a look at the Oracle CX portfolio organized “platform style.”

oracle cx suite CRM Watchlist 2019: The big engines that could (Part 2A, Oracle) — Source: Oracle (thank you)

Now imagine all of THAT interspersed with a customer intelligence platform – their description for CX Unity. The elements of CX Unity that are important is its dynamic profiles (or as both Thunderhead and SAP call it somewhat more accurately “adaptive profiles”) that allows the company to see increasingly rich individual customer data in real time, allowing for a more personalized interaction. While this isn’t particularly revolutionary as an idea or as a product, it is, for Oracle, an important part of their platform – and the very fact that it is a part of a platform, not just another piece in a stack of technology pieces, is of significance. It is part of the shift Oracle is making from stack to platforms & ecosystems. Thus, there is value as a “product” and strategic value in that if reflects the state of mind of the company and its direction. BTW, without going into either detail or a rant – to be entirely clear -CX Unity is NOT a CDP (Customer Data Platform), so please don’t go there.

Design and Delivery

Aligning with market needs is also a matter of cultural alignment. That means in the case of technology companies, the alignment of ‘tastes” and “likes” and “dislikes”. Alignment with the things that attract people to them, within the extant constraints. At tech companies, more often than not, this takes the form of the user experience and the design – the look and feel – of something. The man in charge of Oracle’s designs, SVP of User Experience Design, (and very funny, apparently prominent Lego dude) Hillel Cooperman, has done a brilliant job in reimagining Oracle CX’s interfaces, navigation experience and the overall look and feel of Oracles business applications. Two examples:

CRM Watchlist 2019: The big engines that could (Part 2A, Oracle) — Source: Oracle (Thank you)

See what I mean. Genuinely attractive and simple to use – yet with a lot of enterprise level power behind it. This is contemporary “art” in the technology world in 2019. This can’t be underestimated as to its power. Keep in mind, UX in an enterprise world isn’t just about look and feel but also about usability which in contemporary terms is equated with “simplicity.” It goes to the heart of the most important thing a company can provide to a customer when it comes to a customer’s experience – and that isn’t delight. Its convenience. Simplicity supports convenience in every way. Here is a good article by a company that sells Enterprise UX design services called Justinmind. If you can ignore the obvious self-interest in the article you can learn something about what it takes to build an enterprise-ready UX.

Cooperman’s design theory and practice is reflected well in this LinkedIn article he posted on Medium called “Creating a Design Culture One Artifact at a Time.” While its purpose is recruiting for Oracle, he gives you a good idea of his working practices as the key design thinker for the company. For example, “Helping everyone work simpler and smarter…” While this is a principle of great value, the philosophy reflected in the statement and in the job description are important for understanding Oracle’s dramatic makeover. It goes to the constantly recurring theme of this post and Oracle’s victory in the Watchlist – alignment with the markets and the cultures of the present-day customer.

Three Things to Consider

Get the Narrative Together – Oracle’s transition from inward out to outward in and from a product based IT company to an ecosystems and platforms (I’m going to call that E&P from now on) company requires a major change in the framework, the content and the flow of their CX and corporate narrative. Up to and including how they tell stories, communicate messages, and name products. For example, CPQ Cloud is not a cloud, it’s a feature or at most a solution but doesn’t deserve a separate cloud. Oracle Analytics Cloud is not a cloud nor is Social Cloud a cloud – they are layers in a platform. They are horizontal not stacked vertically. Thus, part of the narrative is to fix the naming. Do away with the CPQ Cloud, Integration, Analytics Cloud, and Social Cloud. CPQ is a feature not a cloud. Integration, Social and Analytics are either a layer in or intertwined with the platform.

But they need to do more than just name and rename products. They need to normalize the corporate narrative. At the recent Oracle MCX (Modern Customer Experience) conference in Las Vegas in March, multiple analysts there including me, took notice of the unevenness of the narrative throughout the conference. It was reflected in the way that the keynotes were done – which to make it truly short – went from concept to product demo to big idea to product to product to outcome to product. You get the idea. The narrative needs to become outcomes-focused, visionary and mission driven and at the same time show continuity over time so that the evolutionary string of the narrative isn’t broken. You lose customer trust when they can’t understand not how you evolved, but why you did. Oracle still has work to do there – though they are on the path.

Thought leadership – Normally, I would be advocating for thought leadership that is internally derived since most companies do engagements with external thought leaders who provide them with validation via the white papers, webinars, videos, podcasts, and other content that supports the concepts that drive the company and its vision and mission. In this case, if they are focused on the Experience Economy, as is SAP, then Oracle needs to engage the external thought leaders such as the father of the Experience Economy, Joe Pine (who SAP via Qualtrics is engaging) and others to support the concept since it is both time honored and constantly evolving. Plus, they need to provide some intellectual meat behind the idea from their perspective internally. As an example of genius in this regard, use what Tien Tzuo, CEO of Zuora has done as the paradigm for owning a space. He created the idea of the Subscription Economy and then went and built out the thought leadership with an intellectual framework, programmatic thinking, ongoing institutionalized news/content, and strong public presence (his own). He now owns the concept which is ubiquitous enough (just enough) to get itself lower cased – i.e. the subscription economy. Oracle doesn’t need to provide all of that – Joe Pine and James Gilmore did that already. But they need to own their own ideas and content related to it. They are starting to. Rob Tarkoff’s keynote at Oracle MCX on “The Value of Time in the Experience Economy” is a reflection of that. But they have a LONG way to go to be able to own it.

Missing journey orchestration in platform – To understand why this hole in their portfolio needs to be filled, I’ll use Oracle’s own explanations and definitions. First, let’s start with their definition of customer experience:

“Customer experience (CX) refers to how a company engages with its customers across every aspect of the customer journey―from marketing to sales to service and every point in between. It’s the sum total of all interactions a customer has with a brand. However, the most important thing for you to know when it comes to your customers’ experience is what they feel about it (PG note: emphasis mine). You have important decisions to make at every touchpoint on the customer journey, and those decisions can influence how your customers view your brand and how successful your business will be as a result.”

Then we move to their discussion of what they see as the purpose and value of understanding the customer’s digital journey:

“Digitization has completely upended the traditional customer journey model. Customers are now more in control than ever. For example, digital capabilities and the personalization that they enable―and that customers expect―require brands to understand purchase history and offer the right products and services at the appropriate time. Brands that want to please customers must also offer self-service options that allow customers to quickly and easily purchase products and services and resolve issues.

Savvy brands understand the importance of “digital body language” and the insights they can glean from a customer’s online activity―which allow these companies to provide customers with a personalized and relevant digital experience. Offering a user-friendly, connected ecosystem that allows customers to enter at any point and receive a tailored and seamless experience will leave customers with a great impression of your brand.”

Before I get into the suggestion, let me just make the point that both these definitions are quite good.

That said, they lack customer journey orchestration – the ability to track the journeys at scale or individually in real time and to identify and/or anticipate customer behavior in order to optimize the communications between the company and customer – again, in real time. There are companies out there like Thunderhead (Note: a client of mine) and Kitewheel (in touch as an analyst) that handle that. There are companies with products that handle an historical version of that – real time interaction management like Infor Omnichannel Campaign Management (born and raised as Epiphany) – but Oracle lacks it in any real sense, and given everything they are doing around what they call Smarter CX and are evolving toward Real Time CX that will emerge as Oracle CX Unity is released screams customer journey orchestration, it’s time to fix it.

This was a very complex victory post. To understand why they won the Watchlist, I had to clarify:

The evolution of their culture which had been entrenched for decades and
The Successful (still a work in progress) alignment of their strategies, programs and products to the market
AND finally their ability to institutionalize that in a way that actually shows that the impact that they are having in this new way is sustainable.

I think I’ve done that. At least I hope so. But Oracle’s victory in the Watchlist in 2019 wasn’t just because they were big and already having an impact all these years – which they were but instead because they were changing and continuing to have an impact – even though they have changed and thus the nature of their impact has too – and will continue for years to come.

Next up (for real): Microsoft ONLY.

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