| Constellation Research

Enterprise tech buyers wary of generative AI hype, security

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

Generative AI is the topic du jour on earnings conference calls and technology press releases, but enterprise customers are wary of data security, compliance and hype. There's a generative AI rocket ship ahead, but the timing of lift off is debatable.

Speaking at Domino Data Lab's Rev 4 conference in New York City, Jan Zirnstein, Director of Data Science at Honeywell Connected Enterprise, said the company has been looking at generative AI use cases but questions remain.

"Generative AI has tipped the public perception of what AI is, but tipped it a little too far," said Zirnstein. "There's nothing in the actual training model and architecture that's tied to truth and factual correctness. We're looking at use cases tied to where factualness isn't imperative like saving time on the creative side. There are also use cases on the summarization side."

Zirnstein said generative AI can speed up software development, but there's also a chance that the technology can simply scale poor code.

Neil Constable, head of quantitative research and investments at Fidelity, said at Rev 4 that there are multiple data safety issues to consider with generative AI. "If you use ChatGPT and think what you put in won't show up in some future version you're sadly mistaken," said Constable. Nevertheless, Constable said enterprises should explore generative AI, but "a lot of work should go into looking at what you should and shouldn't do."

He said it's worth bringing in smaller models and learning how to find tune them. "There's a lot of proprietary data I'd like to throw into it," said Constable. "When trained properly there's the ability to use generative AI across the organization but only internally. The data security issue is no joke."

These concerns were echoed by CEOs speaking on earnings conference calls in recent weeks. The big issue is transparency into how large learning models and transformer architecture work.

Those security concerns are why vendors like Salesforce are pushing a trust layer. "Large customers must maintain data compliance as a critical part of their governance, while using generative AI and LLMs. This is not true in the consumer environment, but it is true for our customers, our enterprise customers who demand the highest levels of this capability," said Salesforce CEO Marc Benioff on the company's earnings conference call.

He added:

"Where customers who for years have used relational databases as the secure mechanism of their trusted data, they already have that high level of security to the row and cell level. We all understand that. And that is why we have built our GPT trust layer into Einstein GPT. The GPT trust layer gives connected LLM secure real time access to data without the need to move all of your data into the LLM itself."

Beyond Nvidia, however, no tech vendor has meaningfully raised guidance based on generative AI demand. Yes, hyperscale cloud providers are ramping up generative AI infrastructure, but the other layers in the tech stack aren't benefiting just yet.

C3 AI CEO Tom Siebel said there are inbound calls about AI. He said:

"I do not believe that it's an overstatement to say that there is no technology leader, no business leader and no government leader, who is not thinking about AI daily. AI chipmakers like NVIDIA are accelerating production to try to keep up with the very real demand that's out there. And all of this is being accelerated by the advent of generative AI.

The interest in AI and in applying AI to business and government processes has never been greater. Business inquiries are increasing, the opportunity pipeline is growing, demand is increasing."

But Siebel also noted that enterprise customers' interest won't translate into revenue right away. "In terms of applying AI to enterprise we're in first half of the first inning. This is an embryotic market," he said. "We're going to see where this goes in the next few years."

Generative AI guide: ChatGPT: Hype or the Future of Customer Experience

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Dell Technologies Q1 better than expected, but sales fell 20% from a year ago

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Dell Technologies saw its revenue fall 20% in the first quarter, but lower operating expenses enabled it to handily beat expectations.

The company reported first quarter earnings of 79 cents a share on revenue of $20.9 billion, down 20% from a year ago. Non-GAAP earnings in the first quarter were $1.31 a share.

Wall Street was expecting Dell Technologies to report first quarter non-GAAP earnings of 85 cents a share on revenue of $20.27 billion.

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The company said it maintained pricing discipline, cut operating expenses and benefited from a normalized supply chain.

In prepared remarks, Chuck Whitten, co-Chief Operating Officer at Dell Technologies, said:

"We continued to see demand softness across our major lines of business, all regions, all customer sizes, and most verticals. In what was a challenging demand backdrop, we executed extremely well and stayed focused on what we could control. Looking ahead, we expect the cautious IT spending environment to continue in Q2."

Whitten said he expects demand to be muted for infrastructure and PCs with some pockets of stabilization.

Even with better-than-expected results, Dell Technologies' core units saw revenue declines. By the numbers:

The Infrastructure Solutions Group had revenue of $7.6 billion, down 18%. Storage revenue was $3.8 billion, and servers and networking sales were $3.8 billion. Operating income for the unit was $740 million.
Client Solutions Group had revenue of $12 billion, down 23%. Commercial revenue was $9.9 billion of that sum. Operating income was $892 million. Commercial revenue was down 18% and consumer revenue fell 41%.

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Salesforce Q1 better than expected, margins improve

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Salesforce reported better-than-expected first quarter earnings and CEO Marc Benioff said the company will infuse "trusted, secure generative AI across our entire product portfolio."

Not surprisingly, Salesforce was talking about generative AI. After all, what vendor isn't talking about generative AI? Benioff, however, noted Salesforce has a portfolio of generative AI brands including Einstein GPT, Slack GPT and Tableau GPT. The company also said its Salesforce GPT Trust Layer is designed to deploy generative AI in a way that secures enterprise data.

As previously reported, enterprises are currently working on how to leverage generative AI and tune using corporate data sets securely.

Speaking on an earnings conference call, Benioff said every CEO realizes that it must invest in generative AI. "Every CEO wants more productivity, automation and intelligence by using AI," he said.

Customers also need to understand where their data is going while keeping compliance, said Benioff. Enterprise customers will have to worry about compliance, security and regulation more than consumer industries will, he added. "This AI revolution is just getting started," said Benioff, who said AI will lead to a new super cycle. He said he was in a customer meeting and all anyone wanted to talk about was generative AI.

Salesforce is planning an AI event in New York in June.

Salesforce reported first quarter earnings of 20 cents a share on revenue of $8.25 billion, up 11% from a year ago. Non-GAAP earnings were $1.69 a share. Wall Street analysts were expecting Salesforce to report first quarter earnings of $1.61 a share on revenue of $8.17 billion.

Benioff also added that Salesforce has become more efficient by improving its non-GAAP margin by 1,000 basis points from a year ago. Salesforce reiterated its revenue outlook for fiscal 2024 and updated its earnings outlook.

"We are transforming every corner of our company," said Benioff, who said Salesforce is improving profitability as well as efficiency. "While the economy isn't in our control our margins are."

For the second quarter, Salesforce is projecting revenue of $8.51 billion to $8.53 billion, up about 10% from a year ago. Non-GAAP earnings for the quarter will be $1.89 a share to $1.90 a share.

For fiscal 2024, Salesforce is projecting sales of $34.5 billion to $34.7 billion with non-GAAP earnings of $7.41 a share to $7.43 a share.

By cloud, Salesforce's Sales Cloud had first quarter revenue of $1.81 billion, up from $1.63 billion a year ago.

Service Cloud had revenue of $1.96 billion, up from $1.76 billion a year ago.
Platform and Other had revenue of $1.57 billion, up from $1.42 billion a year ago.
Marketing and Commerce had revenue of $1.17 billion, up from $1.09 billion a year ago.
And Data Cloud had revenue of $1.13 billion, up from $955 million a year ago.

Also:

Marketing Transformation Innovation & Product-led Growth Next-Generation Customer Experience Tech Optimization Future of Work Data to Decisions New C-Suite Digital Safety, Privacy & Cybersecurity AI ML Machine Learning Generative AI Analytics Automation B2B B2C CX EX Employee Experience business Marketing SaaS PaaS Growth Cloud Digital Transformation eCommerce Enterprise Software CRM ERP Leadership Social Customer Service Content Management Collaboration GenerativeAI LLMs Agentic AI Disruptive Technology Chief Marketing Officer Chief Executive Officer Chief Information Officer Chief Technology Officer Chief AI Officer Chief Data Officer Chief Analytics Officer Chief Information Security Officer Chief Product Officer

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Box CEO Levie on generative AI, productivity and platform neutrality

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Box CEO Aaron Levie outlined his take on generative AI, software table stakes, productivity and the importance of being neutral as enterprises race to integrate the technology.

The comments from Levie came on Box's first quarter earnings conference call. The company reported first quarter earnings of 2 cents a share on revenue of

$251.9 million, up 5.6% from a year ago. For the second quarter, Box projected revenue of $260 million to $262 million with non-GAAP earnings of 34 cents a share to 35 cents a share. The results and outlook were better than expected.

Here's a look at what Levie had to say about large language learning models and generative AI in the enterprise.

LLMs can bring visibility into unstructured corporate data. Levie said:

"For years we've been able to ask questions about our structured data, like the information that's in a database, ERP system, or CRM system. You can ask those systems for financial forecasts, sales pipeline results, inventory levels, supply chain details, and more. But we’ve had limited ability to ask questions of our unstructured data, like content, which is 80% of corporate data. And now we can. By safely bringing leading AI models to enterprise data, enterprises can truly unlock the value that lies in their content.

To do this, we need a way to connect these models safely, securely, and compliantly to our enterprise content.

Imagine being able to instantly ask things like how many days of parental leave can I take? on an HR document or please summarize this report and provide five key takeaways on a quarterly earnings document or how would you pitch this product to a customer in the automotive industry when looking at a product overview document."

Greystone CIO Niraj Patel on generative AI, creating value, managing vendors

Accenture's Paul Daugherty: Generative AI today, but watch what's next

Neutrality on AI will matter to enterprise customers. Levie noted:

"As a platform-neutral vendor, we will also be AI-neutral, which means as new AI breakthroughs emerge from more vendors over time, we’ll be in a position to bring the full power of their technology to Box and our customers. In addition to our collaboration with OpenAI, we recently announced that we are building on our strategic partnership with Google Cloud to integrate Google’s advanced AI models into Box AI to create new ways for joint customers to work smarter and more productively with generative AI."

OpenAI collaboration will likely lead to more Microsoft integration for Box.

He said:

"One, we're partnering with OpenAI which by virtue leads you to partnering more broadly over time with Microsoft as well, given the OpenAI models generally running on Azure. So, there's, I think, a lot of exciting potential in that collaboration and an area that we're going to cooperate with them, we think, pretty meaningfully. And so, customers will be able to basically leverage the exact same AI that they would be seeing in any Microsoft products, but within Box as well. So that kind of adds a bit of a bit of benefit to our relationship there with CoPilot."

Analysis: Microsoft's AI and Copilot Announcements for the Digital Workplace

OpenAI's Brockman: Industry use cases, developers next stage for generative AI, ChatGPT

New uses cases, table stakes and new models. From a product standpoint, Levie said some generative AI capabilities will simply be table stakes. Think about generating AI with content, asking questions about content and workflows. Incremental monetization could come through platform APIs or multiproduct suites. "We've already shown and done are very kind of public about now is we are going to be building this technology full force, and we think it's transformational in how we can work with our unstructured data and our content," said Levie.

Levie said:

"I think everybody is trying to figure out their strategy of how they bring generative AI to their enterprise use cases, which is going to -- which requires a substantial amount of work in kind of the abstraction layer between AI models, customer data and cloud infrastructure, and that's exactly what we're building out."

Productivity gains. Levie said:

"With AI, I think you have such a rapid alignment of customers on testing new use cases, trying out new products and capabilities. Obviously, things like ChatGPT have been front and center. Some companies are fully banning that. Some customers are -- some companies are enabling that. And what I think companies are trying to figure out is where is the productivity gain going to most come from? Is it going to come from going into an AI interface and just like a ChatGPT and asking a question and getting an answer back? Or is it going to come from AI reasoning over existing data and existing workflows in an enterprise and then becoming a productivity boost for those kinds of use cases."

He added that his personal opinion was that generative AI is going to boost productivity by taking on a variety of subtasks a knowledge worker has to handle.

"I strongly believe that this is going to have a net positive impact to just knowledge worker productivity as opposed to a net replacement to kind of large swaps of knowledge work. If you look at the actual tasks that any one of us do in any of our jobs kind of across our roughly 2,500 employees or kind of anybody that we interact with, the vast majority of work that we're actually doing is sort of a collection of many subtasks; hundreds, thousands of subtasks that require us to have a large degree of context that we kind of maintain.

And I think AI is going after those individual subtasks and in some cases, collections of subtasks, but really in a way that will just make us more productive overall. Maybe some roles will be 5% more productive, some roles may be 50% more productive. But I think the net result of that is that we just accelerate into the future faster as opposed to we kind of like do less work.

Instead of having maybe a sales rep or an engineer waste time trying to search or find information, they can be doing the more fun productive parts of their job of working with a customer or getting code released and building a feature. And so, I think that's the kind of impact on the total knowledge worker population.

So, I'm firmly in the optimist camp on this one in terms of what it does to jobs."

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HP: AI can change the role of PCs

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

HP's PC business in the second quarter was down 29% from a year ago a consumer and commercial units fell. HP's second quarter earnings were better than expected.

The company's results highlight the pandemic PC hangover that tech vendors are facing. HP reported second-quarter earnings of $1.07 a share on revenue of $12.9 billion, down nearly 22% from a year ago. Non-GAAP earnings in the second quarter were 80 cents a share.

Wall Street was expecting non-GAAP second quarter earnings of 76 cents a share.

Enrique Lores, CEO of HP, said the company is focused on "disciplined execution and strong innovation in a tough macro environment." Lores said on a conference call that it’ll move to lower channel inventory.

On a conference call, Lores said:

"Like last quarter, we estimate that the sell-out to customers exceeded sell-in to the channel, which means that end-user demand was stronger than revenue shipments.

This helped us further reduce our channel inventory. There are still pockets where we need to improve, but we are making good progress as per our plan."

By unit, HP's personal systems unit delivered second quarter revenue of $8.2 billion, down 39% from a year ago. Commercial PC units were down 23% and consumer units fell 28%. The printing division had second-quarter revenue of $4.7 billion, down 5%.

Lores said generative AI has the potential to boost PC sales as customers look for new architectures and designs.He argued that the personal systems unit remains a strong long-term growth driver. "AI will transform the role PCs play in our lives," he said.

For the third quarter, HP projected non-GAAP earnings of 81 cents a share to 91 cents a share. For fiscal 2023, HP projected non-GAAP earnings between $3.30 a share to $3.50 a share. Lores said that the PC unit is expected to improve due to lower channel inventory and seasonality along with its cost savings efforts.

According to HP, 46% of its second quarter revenue was commercial PC sales with 17% consumer PCs. Printing supplies were 23% of revenue with commercial printing at 9% and consumer printing 5%.

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HPE rides edge computing, HPC growth in fiscal Q2

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Hewlett Packard Enterprise saw strong fiscal second quarter growth from its intelligent edge and high performance computing and AI units as its earnings were better than expected.

HPE reported second quarter earnings of 32 cents a share on revenue of $7 billion, up 4% from a year ago. Non-GAAP earnings were 52 cents a share. Wall Street analysts were expecting HPE to report non-GAAP earnings of 48 cents a share on revenue of $7.31 billion.

As for the outlook, HPE said its third quarter revenue will be between $6.7 billion to $7.2 billion with non-GAAP earnings between 44 cents a share to 48 cents a share. For fiscal 2023, HPE sees revenue growth of 4% to 6% in constant currency with non-GAAP earnings of $2.06 a share to $2.14 a share.

Constellation ShortList™ Digital Transformation Target Platforms

HPE has been pivoting to more of an as-a-service company for software as well as hardware. The company's HPE Greenlake platform now has annual recurring revenue of $1.1 billion and total as-a-service total contract value topping $10 billion.

Antonio Neri, CEO of HPE, said the company's shift to higher margin products is paying off. Indeed, HPE's intelligent edge unit delivered second quarter revenue of $1.3 billion, up 50% from a year ago. The HPC and AI unit had revenue of $840 million, up 18% from a year ago. Compute revenue in the second quarter was $2.8 billion, down 8% from a year ago, and storage sales fell 3% from a year ago to $1 billion.

HPE's growth by business unit highlight technology investment trends. For instance, AI is driving HPC systems. HPE's HPC and AI unit delivers standard and custom hardware, software and data management systems for data-intensive workloads. The intelligent edge unit features platforms and services such as wireless local area networks, switching and software defined networking.

Speaking on a conference call, Neri said:

"In the second quarter, we saw some decline in the health of microeconomic conditions, causing unevenness in customer demand, particularly in general purpose compute. We also see unevenness when comparing customer size, industry, or geography. European, Asian, and mid-sized company deals are holding up better than expected, while large enterprise businesses and customers in certain sectors such as financial services manufactured in North America have been more conservative with our spend.

In the last few months, sales cycles have elongated because customers are more reluctant to quickly commit to large projects or some will seek additional internal approvals at the time of the order. We continue to focus on our soft processes to accelerate closing deals wherever possible."

Constellation Research analyst Holger Mueller said:

"HPE had a solid quarter, doing better than a year ago (4% up), but worse than last quarter (11%) revenue wise. Double digit less revenue in Compute, HPC & AI as well as Storage did not help Antonio Neri and team. But with good cost control, and even a reduction in cost of sales as well as total costs and expenses, both ToT and QoQ, show a better earnings per share than a year ago – now all eyes are on Q3."

Among the takeaways:

Intelligent edge and HPC and AI units are now 30% of revenue.
Compute is 39% of HPE's revenue.
87% of HPE's second quarter operating profits were via the intelligent edge and compute units.
HPC and AI's order book is topping $2 billion in awarded contracts. HPE has four of the global top 10 supercomputers and three of the top 5.

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CrowdStrike launches Charlotte AI, generative AI to uplevel, democratize cybersecurity analysis

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

CrowdStrike is using generative AI to tap into its Falcon platform and deliver natural language answers and recommendations on cybersecurity.

According to CrowdStrike, the generative AI rollout called Charlotte AI, is designed to turn everyone into a cybersecurity power user. The company said Charlotte AI can also close cybersecurity skills gaps and speed response time.

The broader takeaway is that companies with vibrant data sets can leverage large language models (LLMs) for competitive advantage. CrowdStrike's Charlotte AI taps into a platform that captures trillions of data points on cybersecurity incidents via its CrowdStrike Threat Graph.

CrowdStrike added that Charlotte AI will also tap into human-validated content to create a human feedback loop in addition to threat intelligence. In a blog post, CrowdStrike noted:

"Generative AI opens up a new world of possibilities by creating net-new outputs based on the patterns and structures inherent to the training data. But the limiting factor will always be the quality, context and completeness of the underlying data."

Palo Alto Research CEO Nikesh Arora had a similar take when the company reported earnings.

Use cases outlined by CrowdStrike include:

Answering CXO and business user questions about risks related to a recent vulnerability.
Empowering lower-level security analysts to perform more high-level analysis.
Automate repetitive tasks including data collection, extraction and threat searches and detection.

Charlotte AI is in private preview.

Accenture's Paul Daugherty: Generative AI today, but watch what's next

OpenAI's Brockman: Industry use cases, developers next stage for generative AI, ChatGPT

Greystone CIO Niraj Patel on generative AI, creating value, managing vendors

C3 AI CEO Tom Siebel: Generative AI enterprise search will have wide impact

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Lenovo launches ThinkReality VRX headset, targets enterprise training, collaboration

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Lenovo launched an all-in-one virtual reality headset, ThinkReality VRX, that's aimed at enterprise use cases including employee training, collaboration and design. Lenovo's announcement lands days ahead of Apple's mixed reality headset that will reportedly launch at WWDC June 5.

While Apple's headset for developers is likely to garner headlines next week, enterprise VR will be a core topic at the AWE USA conference this week.

Enterprise use cases for VR make the most sense to date as consumer adoption has been slow. Meta has bet big on the metaverse and its Oculus brand, but the effort hasn't delivered returns.

Lenovo's ThinkReality VRX starts at $1,299 and will be powered by the Snapdragon XR2+ Gen 1 processor. Lenovo is also including the ThinkReality VRX in its device as a service offering. Lenovo already offers the ThinkReality A3, an augmented reality headset.

ThinkReality VRX, which also had hand controllers, includes the following features:

Slim form factor with pancake optics and a 6900 mAh battery that's positioned for weight distribution.
A venting system that channels heat from the display away from a user's face.
Support for mobile device management programs.
Support for Snapdragon Spaces XR Developer Platform and OpenXR-based SDK.
Android 12, 95-degree field of vision, 4-camera 6DoF optical tracking, 12GB of RAM, 128GB of storage and 2280 x 2280 resolution per eye.

On Lenovo's fourth quarter earnings conference call, Kirk Skaugen, president of the company's infrastructure solutions group, said omniverse and metaverse are part of the growth strategy.

"We're in collaboration with Microsoft and Nvidia and we are currently installing and building the world's largest Omniverse instance in the cloud. And I think we're very excited about that. We're working with some of the largest automotive companies in the world as they build new factories for their electric vehicles new digital twins for the factories, planning next-generation smart cities, planning next-generation 5G networks. So Metaverse, Omniverse is a critical part of our AI story. And of course, Lenovo is unique because we can do Edge to cloud and pocket to cloud. So we have the AR/VR devices. We have the workstations, where the servers only have the storage, which means we're simplifying that for our end users."

Related Shortlists:

Future of Work Innovation & Product-led Growth Next-Generation Customer Experience Marketing Transformation Metaverse Chief Information Officer

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Greystone CIO Niraj Patel on generative AI, creating value, managing vendors

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Niraj Patel, CIO of multifamily lender Greystone, has a knack for bringing digital transformation to the financial services and real estate industries.

Greystone's Patel has led teams at DMI, IBM. Selex and GMAC before coming to Greystone. Through all of those CXO roles, Patel brought innovation, value and data lens to projects.

I caught up with Patel to talk about generative AI, vendor management and creating value with data. Here's a recap of our conversation.

CIO challenges. "Most challenges of CIOs aren't about technology, but adoption, change management and value creation," said Patel. "How do we bring that technology in and get productivity for the customer?"

Examples of value. Patel said CIOs need to know their industry and what really matters. For instance, in financial services and real estate success is a leverage game and that means interest rates are everything. "Technology is nice, but if we can arm sales folks with better information and insights they can get a better rate," said Patel.

Generative AI and AI. Patel is a veteran of using AI and noted that the technology has been around for a while. In 2012, Patel oversaw a project to use AI to predict energy efficiency. Later, AI was used for vision and natural language processing. "Today all of those pieces can be working together for generative AI," said Patel, who noted that streamlined forms and wider data sets can drive productivity.

One example would be property inspections where the inspector on scene captures forms, pictures and videos. AI can analyze unstructured data which can then be combined with the company's financial data and third-party data on the structure of the building as well as its content. "AI brings granularity to the inspection," said Patel.

Chatbots vs. generative AI. Greystone has invested in chatbot technology and robotics process automation. Now generative AI and technologies like ChatGPT can advance the ball. When asked about chatbot sprawl, Patel said it helps to put guardrails around the technology and the data sets used. "We think generative AI can accelerate chatbots to make them more individualistic and bring interactions to you specifically," said Patel. "Today chatbots are more generic horizontal ones, but they can become more vertical with the enterprise data it's accessing."

Large language models. When it came to LLMs, Patel said there's an art to using foundational models and tuning them with enterprise data. Using off-the-shelf models is about acquiring data from the outside world. "Off the shelf is data about the building and what's around," said Patel. "Financial data like underwriting and rents are my domain."

Integration. The various data sets in a commercial real estate transaction will require integration. For instance, property managers have one software platform and lenders have another. Then there's the decision on where to put the data, which will be partly on premises and cloud. Patel said mobile devices can also process data. "We'll probably want more processing on the edge," said Patel. "You also need to decide whether you want just actionable data or everything."

Vendor management. Patel said he prefers to have a set of vendors that can provide innovation and be manageable. Greystone is a Microsoft shop so leveraging Azure is a natural fit. However, Patel said he works with Google Cloud and AWS too as well as smaller vendors.

"Having all big vendors is a problem so we have smaller ones too. I break vendors up into small, medium, and large, but we don't do too many and all of them need a strong integration later," said Patel. "Smaller vendors bring speed and more innovative thinking. Smaller players are also more functional in nature."

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Analysis: Microsoft's AI and Copilot Announcements for the Digital Workplace

Dion Hinchcliffe

Vice President and Principal Analyst
Constellation Research

Title: About Dion Hinchcliffe Dion Hinchcliffe is an internationally recognized business strategist, bestselling author, enterprise architect, industry analyst, and noted keynote speaker. He is widely regarded as one of the most influential figures in digital strategy, the future of work, and enterprise IT. He works with the leadership teams of large enterprises as well as software vendors to raise the bar for the art-of-the-possible in their digital capabilities. He is currently Vice President and Principal Analyst at Constellation Research. Dion is also currently an executive fellow at the Tuck School of Business Center for Digital Strategies. He is a globally recognized industry expert on the topics of digital transformation, digital workplace, enterprise collaboration, API…...

This week's big announcements at Microsoft Build 2023 mostly centered, as one might expect these days, around the adoption of artificial intelligence across Microsoft's various products. And by AI, specifically it's generative AI based on vast foundation models that is the focus these days. The well-known annual developer conference in Seattle was a veritable hotbed of AI news, featuring a series of updates aimed at making the company's flagship Windows operating system and productivity apps smarter, more intuitive, and potentially more ubiquitous.

Since the AI-powered Bing's launch earlier this year, users have engaged in over half a billion chats and created more than 200 million images using the Bing Image Creator. Furthermore, daily downloads of the Bing mobile app have seen an eightfold increase since launch, demonstrating the substantial interest in this AI enhancement, especially as it also provides still-rarified access to the latest version of the OpenAI's most powerful new large language model, GPT-4. The conference also marked a new stage in the partnership between Microsoft and OpenAI, as Bing was integrated into ChatGPT, a move that promises to provide users with more grounded answers and easy-to-access citations.

But by far the most interesting news for digital workplace and end-user computing professionals was Microsoft's many announcements aimed at incorporating AI into important aspects of the company's desktop platform and productivity applications.

AI is Coming for All Your Microsoft Products (and the Data in Them)

Further complementing the scope of Copilot announcements for Microsoft365 a couple of months ago, Microsoft introduced a native OS AI "Copilot", designed to edit, summarize, create, and compare documents across Windows 11. This tool will provide quick access to personalized answers and relevant suggestions and can perform tasks such as changing a screensaver or turning on Bluetooth. This integration of an AI 'copilot' into Microsoft's platforms is a significant move and is widely seen as a watershed moment for the future of computing as a key way to accelerate work, amplify innovation, and eliminate tedious tasks.

This major announcement moves Microsoft's generative AI capabilities directly onto the desktop. Dubbed Copilot for Windows 11, the tool helps users edit, summarize, create, and compare documents across Windows 11 just as it underscores the company's ambitious plans for AI integration. Microsoft's latest Work Trend Index research -- you can read my detailed summary of its fjndings -- has shown that workers are spending two full days of the work week managing email and attending meetings, leading to a growing demand for such AI assistants to tackle and/or reduce the growing volume of digital work.

Copilot in Windows 11 sits right alongside the desktop to provide instant AI assistance

Microsoft 365 Copilot is now the overall brand for end-user AI assistance, which combines the power of AI foundation models directly with users' business data in Microsoft Graph and Microsoft 365 apps. Copilot aims to streamline virtually all types of work and increase productivity. Another key highlight of the conference was the announcement of extensibility for Microsoft 365 Copilot with plugins to greatly enrich what an AI assistant can do. This move is designed to empower developer everywhere to integrate their apps and services into Microsoft 365 Copilot, aiming to reach hundreds of millions of users and create productive, AI-powered new ways of working. Microsoft announced three types of plugins for Microsoft 365 Copilot: ChatGPT plugins, Teams message extensions, and Microsoft Power Platform connectors. The latter will allow developers to use existing software and tooling investments and skills.

Copilot Whiteboard in Microsoft Teams can auto-generate ideas, organize ideas into themes, create designs

Customers in the Microsoft 365 Copilot Early Access Program will have access to over 50 plugins from partners such as Atlassian, Adobe, ServiceNow, Thomson Reuters, Moveworks, and Mural, with thousands of additional plugins being enabled in the coming months.

In terms of user safety and protections, Microsoft announced it is continuing to add AI safety technology such as the ability to help users determine if images are AI-generated based on information included in their metadata. Many other safety mechanisms are planned, something that IT administrators and CISOs will care greatly about.

Developers can now create Teams message extensions today that will function as plugins for Microsoft 365 Copilot, with the company introducing new capabilities in Teams Toolkit to make it easier to create, test, and debug plugins.

The new Semantic Index for Copilot is a sophisticated AI map of your user and company data

In terms of its line of business apps, Microsoft 365 Copilot can now also access structured data from Microsoft Dynamics 365 and Microsoft Power Platform stored in Microsoft Dataverse, meaning Copilot responses will be grounded in business data in addition to user data in Microsoft Graph, which is certainly turning into the strategic business data platform I long suspected it would become.

Remarkably, the news above only scratches the surface of what Microsoft announced at Microsoft Build, with many other niche but significant AI announcements that will be challenging to fully absorb for many of their customers. But the arrival of powerful, democratized AI is a game-changing event in the industry, and most vendors know the stakes: Don't adopt AI fast enough, and users may vote with their feet for platforms that provide the potent capabilities that the latest AI systems have proven to deliver

IT Departments: Starting Preparing for Copilot Now

Wrapping up, the Microsoft Build 2023 conference heralded a new era of AI advancements, with a strong focus on increasing user productivity and streamlining workflows. The integration of Bing into ChatGPT and the expansion of Microsoft's AI tool, Copilot, each demonstrate in their own way Microsoft's commitment to harnessing the power of AI for practical, everyday use. The introduction of plugins for Microsoft 365 Copilot, coupled with robust user safety measures, reflect a vision of a more efficient and user-friendly AI-enabled future. But the star of the show by many accounts was Copilot for Windows 11, which is being billed as the first major AI assistant for a PC platform.

These advancements are likely to have a significant impact on what businesses can achieve with technology in their daily work, ushering in a new era of convenience and productivity for those that adopt them. My recommendation is that digital workplace and employee experience teams immediately begin researching these new additions to Microsoft's platforms and applications thoroughly and train users on the ones that have meaningful impact as they become available, using digital adoption tools if necessary to broadly help with uptake. The rewards for most workers are considerable and for now, the risks appear to remain manageable, but AI skills will soon become the long pole in getting value, not feature availability.

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