Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Intel outlined its Intel Foundry results as it recast its financial reporting segments and added a new CFO as the unit aims for break even.

The chipmaker outlined its new reporting segments and historical results. It also named Lorenzo Flores as chief financial officer of Intel Foundry. Flores was CFO of Xilinx.

Intel's new operating model creates a foundry relationship between Intel Foundry, the manufacturing unit, and Intel Products, the product business units. In an SEC filing, Intel said Intel Foundry operating revenue in 2023 was $18.9 billion, down from $27.5 billion in 2022. The operating loss for Intel Foundry in 2023 was $7 billion, up from $5.2 billion in 2022. Intel Foundry's external revenue was $953 million in 2023.

• Intel Foundry sets roadmap, aims to be No. 2 foundry by 2030
• Intel's Q4 strong amid PC rebound, outlook light: Here are the takeaways

Pat Gelsinger, Intel CEO, said the products and foundry business can drive long-term sustainable growth. Intel Products will include the company's client computing group, data center and AI group, and network and edge units. Altera, Mobileye and Other will be in an All Other category. Intel Foundry will be reported separately and include external revenue as well as sales from Intel Products.

Intel is aiming for 60% non-GAAP gross margins and 40% non-GAAP operating margins in 2030.

Intel CEO Pat Gelsinger said on a conference call with analysts that "we see this clear path to driving Intel Foundry to break even over the next few years." 

"What we see as a self-sustaining model with solid returns for Intel Foundry and the consolidated company by 2030," he added. "We are also making fundamental moves to standard intellectual property blocks and processes, where we became so disconnected from the industry. We are also addressing the rising capital requirements by optimizing for cost efficiency and extending the life of our assets. We are addressing the scale challenges by bringing in external designs into our factory network and monetizing the IP in both our products and our foundry."

Constellation Research analyst Holger Mueller said the burden of profit proof is on Intel. Mueller said:

"Intel goes 'fabless' – though only for the balance sheet. It remains to be seen if investors will fall for valuing the 'fabless' Intel the same as other truly fabless competitors like e.g. Nvidia or Qualcomm. Intel needs to finance the losses of the fab business – unless this is the start of spinning that off – something we saw in the past e.g. with IBM and GlobalFoundries. Intel is not producing competitive chips and for the sake of competition and budgets we can only hope that the old Intel will roar back and create some much needed competition in the years ahead."

The big breakout from the new reporting structure is Intel Foundry. Intel said the unit's operating losses are expected to peak in 2024 and the company will hit break-even operating margins midway to its 2030 goals. Intel Products already has solid operating margin en route to the 2030 targets.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

It's open season for middle managers across technology and multiple industries, but it remains to be seen how short-sighted this trend ultimately becomes.

Yes folks, this is an ode to middle managers--the people who manage projects, serve as a buffer to executives, and lead teams in smaller batches. Today, we've gone from the COVID-19 era of over hiring to cutting out layers of management. In true American fashion, the pendulum swings all the way to the other side with little to no balance in the middle.

According to Live Data Technologies, manager-level or higher layoffs made up nearly half of all observed layoffs in 2023.

In 2024, it appears to be a similar layoff story via Layoff.fyi.

Consider:

• Mark Zuckerberg, CEO of Meta, said on the company's fourth quarter earnings call that he has "come around to thinking that we operate better as a leaner company." Zuckerberg just closed out 2023, dubbed a year of efficiency, and noted that "in 2024, we do have a big recruiting backlog because part of the layoffs that we did included teams basically swapping out certain talent profiles for others." Translation: Engineers and salespeople in. Everyone else out. Meta ended 2023 with 67,317 employees, down 22% from 2022. As the end of 2021, Meta had 71,970 employees up 23% from 2020.
• Sundar Pichai, CEO of Google parent Alphabet, said "teams are working to focus on key priorities and execute fast, removing layers and simplifying their organizational structures."
• UPS Carol Tome said the company is going to fit its organization to focus on "what's wildly important." UPS is cutting 12,000 positions to save $1 billion and most of those reductions are managers.
• Citi CEO Jane Frasier said it will cut more managers to simplify the org structure and save more than $1 billion. Citi restructurings aren't exactly a new trend since its core competency appears to be adding management layers and penning restructuring announcements later.

I realize it's a bit contrarian to defend middle managers--especially when one of my recent reads was Bullshit Jobs: A Theory--but there is going to be some fallout to this middle management purge.

This post first appeared in the Constellation Insight newsletter, which features bespoke content weekly and is brought to you by Hitachi Vantara.

Here are some of the ramifications from the middle manager cuts.

Morale. The UKG Workforce Institute found that 86% of managers’ report feeling burnout. That job burnout rate is higher than employees and CXOs. However, 75% of employees feel that their manager cares for and has empathy for them.

You can't tell me that a super flat organization where 200 people report to one executive who doesn't have time for them is going to boost morale.

Decline of project management. Many of these middle manager types getting cut happen to be project managers. Simply put, project managers have a tough gig. They're caught in the middle between the technology and business sides of an enterprise. These project managers are more human and happen to be bilingual in tech and business. Over time, enterprises are going to realize that you need a human in the middle of the Venn diagram to get things done.

Customer success teams at your friendly neighborhood vendor will suffer. Anecdotally, enterprise vendors have been cutting customer success managers who aren’t technical or salespeople. These customer success structures have always been a bit wonky. One setup could have a technical lead, a sales-ish type person focused on success (and a never-ending upsell cycle) and a manager who is more neutral and may want the customer to actually succeed. Guess what happened? The relationship person was cut (no credit for revenue and/or code). Enterprise customers have been asking where their core contacts went over the last 18 months. It's quite possible that customers are stuck with a tech-oriented contact with little people skills and another who is focused on relationship building with a heavy dose of upsell.

SaaS companies are going to lose seats. Live Data Technologies also had a post on the shrinking total addressable market for SaaS companies. Guess who SaaS users were? Yep, those managers who were cut over the last year and change.

A few companies will realize that the talent that has been purged can be very useful in the right cases. AuthID CFO Ed Sellitto said on the company's recent earnings call that it is building out its customer success team. "This team is laser focused on deploying and refining proven customer implementation methodology to help customers meet projected go live dates, establish full-service rollouts across contracted use cases and meet and ultimately exceed their minimum contracted commitments," said Sellitto.

Enterprises could quantify middle managers. If project managers and folks who run teams can be credited for some slice of the overall revenue pie the economics may look dramatically different. To date, the sales department runs off with the quantification (as if that person sold everything with no support whatsoever) and middle managers look like overhead. The person who keeps the product launch running on time is taken for granted. Maybe a slice of revenue attribution for the supporting cast is warranted.

The problem for managers is the same as it was in the 1980s: It's hard to quantify middle managers. In the age of AI, soft metrics aren't going to fly.

Executives are going to feel all the burnout middle managers faced. Middle managers were facing extreme burnout. Well, now that the middle tier is gone and all those gripes, employee morale issues and balancing act between KPIs and reality will hit higher-level executives. Once those execs in flattened orgs feel the pain look for enterprises to start adding a layer or two back. The harsh reality is someone must absorb the blows and executives are going to find it difficult to yell at algorithms that replaced those human managers just cut.

What the large language models think of middle managers

Given I was going to take the unlikely position of supporting middle management, it made sense to ask generative AI--the technology that'll replace these people--what it thought. Here's a look at what Gemini and ChatGPT gave me when I asked for an ode to the middle manager.

Gemini:

Oh, middle manager, you unsung hero,

You keep the wheels of industry turning.

You bridge the gap between upper management,

And the front-line workers who do the earning.

You're responsible for implementing strategies,

And making sure that work plans are followed.

You're also responsible for the execution,

Of your own and your team’s work.

You're often beholden to others' expectations,

Deadlines, and schedules.

But you always try your best,

To make sure that the job gets done.

You're the glue that holds the organization together,

And you deserve to be recognized for your hard work.

So, here's to you, middle manager,

We appreciate all that you do.

And the ever-wordy ChatGPT:

Ode to the Middle Manager, steadfast and true,

Keeper of balance, in skies often not blue.

In the realm of the cubicles, amidst fluorescent light,

You stand as a beacon, a guide through the night.

With a coffee in one hand, reports in the other,

You navigate the waters, a skilled helmsman and brother.

Between the titans above and the sea below,

You chart the course, steady and slow.

In meetings, you're the diplomat, the voice of reason,

Bridging gaps, through every season.

Your inbox, a testament to battles fought,

Each email is a volley, each decision a thought.

You translate the visions, from lofty to real,

Into actionable plans, with zeal you seal.

The dreams of the high, to the hands of the low,

Through you, they take shape, and begin to grow.

You're the unsung hero, in the corporate fray,

Juggling demands, come what may.

The mentor, the coach, on the office floor,

Guiding your team, to strive for more.

When the storm clouds gather, and morale is low,

You're the first to arrive, and the last to go.

With a joke, a smile, or a listening ear,

You dispel the shadows and bring back the cheer.

For the middle manager, no song is sung,

No praises shouted; no bells are rung.

But know this truth, as clear as day,

Without you, the ship would lose its way.

So, here's to you, in your unassuming might,

The linchpin that holds, the day and night.

In the heart of the maze, you find the path,

Middle manager, you deserve our gratitude and laughter.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Blue Yonder's acquisition of One Network Enterprises, which offers a platform for autonomous supply chain resiliency, for $829 million is a bid to become a data hub for diversified sourcing and value chain collaboration.

With the purchase, Blue Yonder, a standalone unit within Panasonic, is looking to provide a platform to plan, execute and network supply chains. One Network is known for its Digital Supply Chain Network platform.

Here's a look at One Network's platform.

Blue Yonder offers a broad supply chain management suite under its Luminate platform. The company provides supply chain planning modules, execution tools for logistics, modeling, warehouse management and fulfillment and an omni-channel commerce suite.

Supply chain transformation critical as resilience worries stack up

Duncan Angove, CEO of Blue Yonder, said the plan is to leverage One Network to become a value chain collaboration hub so enterprises can share information and resources. Angove added in a statement that Blue Yonder will be able to offer "a unified, end-to-end supply chain ecosystem that is resilient enough to withstand today’s challenges, and synthesized with innovative, future-focused technologies."

Blue Yonder has spent about $1 billion on merges and acquisitions since the fourth quarter. Blue Yonder recently announced the purchase of flexis AG, a factory planning application firm, and Doddle, which focuses on returns and reverse logistics.

Going forward, Blue Yonder plans to leverage One Network along to do the following:

• Orchestrate and optimize supply chains across multiple tiers.
• Automate the movement of orders from planning to fulfillment via actionable data, alerts and artificial intelligence.
• Offer real-time visibility across the supply chain.
• Unify data silos across the entire supply chain.
• Offer carriers and suppliers services for everything from shipment scheduling to tracking and insights.
• Tap into the 150,000 trading partners in One Network's platform for collaboration and streamlined processes.

Greg Brady, Chairman of One Network, said Blue Yonder's platform combined with One Network will be able to create "a resilient and collaborative supply chain."

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Amazon said it will invest another $2.75 billion into Anthropic to bring its total investment to $4 billion. The deal highlights the urgency of the generative AI arms race as hyperscalers create spheres of large language model influence.

Under the AWS partnership, Anthropic uses AWS as its primary cloud provider and uses AWS Trainium and Inferentia chips. Anthropic gets more distribution and heft behind its Claude model. In September, AWS and Anthropic outlined their initial partnership. AWS exercised its option to invest more in Anthropic.

The LLM orbits break down like this:

• Microsoft and OpenAI.
• Microsoft partnerships with Mistral and talent grabs to ensure it has LLM brainpower and keep regulators at bay.
• AWS and Anthropic.
• Google Cloud and Anthropic to a lesser degree than AWS.
• Open-source models such as Meta's Llama that can be customized through various marketplaces, GitHub and Hugging Face.
• Oracle and Cohere.
• Google Cloud and its own Gemini models. 
• A bevy of smaller models that are targeted at specific enterprise use cases.
• DBRX, a Databricks entry that goes along with Mosaic ML models.

Today, it's clear that enterprise cloud and software giants are teaming up with LLM specialists as fast as possible. It's an arms race and why you'll need a chief AI officer to sort out the LLM strategy.

But the larger question is what happens when LLMs become commoditized. Of course, no one is thinking about that possibility yet since the party is just starting. These foundational models will lose importance as the game really becomes about customization with company-specific data.

Constellation Research's take

Dion Hinchcliffe:

"Ultimately, it's all about the data. If AI offerings can entangle themselves in their customers' data in a way that is beneficial for the customer, yet hard to leave, then it’s a win. Commodity offerings won’t matter as much when switching costs are high. Such switching costs involve data gravity, product skill switching, lost training time (weeks/months to train the new model on enterprise data), and especially a track record — or a lack thereof — of trust/privacy. AI is likely the new lock-in. Yes, this implies private LLMs are where the big money is, and that is likely where we’ll end up. Commodity AI gets the public model market, hyperscale offerings get the enterprise data market. Use of public models with enterprise data is also another avenue for non-commodity offerings."

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Databricks launched DBRX, an open-source large language model that aims to enable enterprises to customize models with their own data.

DBRX, available on GitHub and Hugging Face, Databricks and cloud providers, is a general performance model that outperforms open-source models on standard benchmarks. For Databricks, which acquired Mosaic ML, the DBRX LLM rides shotgun with its popular data platform. DBRX was developed by the Mosaic team that previously built the MPT model family.

Ali Ghodsi, CEO of Databricks, said DBRX is designed to help enterprises to "understand and use their private data to build their own AI systems." Using private data to tailor LLMs securely has been a recurring theme in recent months from multiple vendors. The most cost effective way to customize models is to use smaller language models and leverage open source models. 

Nvidia GTC highlighted how the software building blocks for generative AI are in place. The company launched Blackwell GPUs, but Nvidia Inference Microservices (NIMs) will ultimately be just as important. NIMs are pre-trained AI models packaged and optimized to run across the CUDA installed base.

• Enterprise generative AI use cases, applications about to surge

Doug Henschen, analyst at Constellation Research, said the Databricks move with DBRX fills a need to enable enterprises to customize open-source models. Henschen said:

"With its DBRX launch, Databricks is a step ahead of rivals in helping customers to use their private data to build their own AI systems. Yes, having another open LLM to choose from is great, but the point is accelerating the move to custom models built on the customer’s own data."

What to Look for in Analytical Data Platforms for a Cloud-Centric World

• Databricks acquires Arcion for $100 million
• Databricks adds on to the Lakehouse, acquires MosaicML for $1.3 billion
• Databricks launches Lakehouse Apps, aims to be development platform
• JPMorgan Chase: Digital transformation, AI and data strategy sets up generative AI
• Databricks Data + AI Summit: LakehouseIQ, Lakehouse AI and everything announced

As for the technical details, here are some key points about DBRX:

• DBRX was trained on 3072 NVIDIA H100s connected by 3.2Tbps InfiniBand. Databricks built DBRX on its suite of tools used by customers.
• The LLM uses a mixture-of-experts architecture, which is cost effective and efficient leveraging tokens per second. DBRX is efficient for inference tasks.
• DBRX is a transformer-based decoder only LLM with 132B total parameters with 36B parameters active on any input.
• DBRX was pretrained on 12T tokens.
• DBRX is being integrated into Databricks' GenAI products and has surpassed GPT 3.5 Turbo in applications like SQL and retrieval augmented generation (RAG) tasks.
• Early customers and partners include Accenture, Allen Institute for AI, Block, Nasdaq and Zoom.

Databricks Platform customers can leverage DBRX for RAG and to build custom models. DBRX is also on AWS, Google Cloud and Microsoft Azure via Azure Databricks. DBRX will also be available through Nvidia's API Catalog and supported on Nvidia's NIM inference microservice.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Adobe laid out a host of Experience Cloud enhancements, data collaboration and customer journey applications, custom Firefly models and a marketing copilot co-developed with Microsoft to connect Adobe's Experience platform with Microsoft 365 applications.

The barrage of news kicked off Adobe Summit in Las Vegas, the company's enterprise focused conference. With a format that targets CIOs and CMOs and everything in between, Adobe is hitting on the core themes of the content supply chain, generative AI and insights that drive returns.

At a high level, here's a look at Adobe's product updates across its portfolio. In addition to the marketing copilot collaboration with Microsoft, Adobe Summit will feature partnerships with IBM, Accenture, and Omnicom and customer thought leadership from companies such as GM, Delta and Pfizer.

During his keynote at Adobe Summit, Adobe CEO Shantanu Narayen said: "When we think about the magic and value of AI, we know that it actually comes only from the seamless integration into the workflows that all of you as customers already know and love."

Narayen added that generative AI led by Firefly will be embedded across its creative production and marketing workflows. He added that Adobe has leveraged generative AI to bolster its marketing returns. Narayen said:

"I loved engaging with our marketing teams, the products, the engineering and design teams to develop as they developed Adobe Gen studio so that we could empower marketers to quickly plan, create, store, deliver and measure all marketing content. What's constant is that content, data and journeys will at the core of how you engage with customers."

For Adobe, generative AI is seen as a technical bridge between experience, creative and marketing teams. "Our goal is to make sure that creative professionals and marketing professionals can use generative AI in their workflows. And our plan is to do that through a mix of models," said Ely Greenfield, CTO, Digital Media at Adobe.

• Adobe: Strong Q1, outlook a bit light, GenAI on deck for Experience Cloud
• Adobe, Figma call off $20 billion merger
• Adobe Max 2023: Firefly's new model, GenStudio and everything announced

Adobe Summit kicks off as marketing is in flux as a function with generative AI readiness, cookie depreciation and driving returns as key issues. Adobe is also infusing generative AI across its platform as it increasingly caters to multiple personas ranging from CFOs to CIOs to CMOs and digital and data chiefs.

"For enterprise users, we really see the combination of generative AI and the predictive AI unlocking genAI applications to go from toys to tools," said Lily Chiu-Watson, Director of Product Marketing, Adobe.

Here's a look at the enterprise-focused news from Adobe Summit.

Microsoft and Adobe teamed up on a Marketing Copilot. The two companies co-developed a marketing copilot that uses the data assets and insights within Adobe Experience Cloud and makes them available in Microsoft applications. Adam Justis, Director of Product Marketing for Adobe Experience Cloud, said the Microsoft copilot collaboration is "a meaningful way to democratizing access to insights from Experience Cloud.

• Inside Adobe Max through the eyes of a marketer | What if a Trillion PDFs Could Talk?

Adobe Experience Platform AI Assistant is a conversational AI interface that will enable Adobe Experience Cloud customers to easily get answers on status of data, audience segments and recommendations. AI Assistant can automate routine tasks, answer multiple types of questions, and serve a broad range of users. 

• Constellation ShortListâ„¢ Digital Experience (DX) Platforms
• Constellation ShortListâ„¢ Digital Asset Management (DAM) for Digital Experiences

Real-time data collaboration in Adobe Customer Data Platform. As cookies are phased out, first party data collaboration between brands, publishers and advertisers will be critical to identify audiences and market to them within strict privacy guidelines. Adobe will also add federated audience capabilities and the ability to tap into enterprise data in on-premise systems as well as cloud data warehouses such as Snowflake, Google Cloud's Big Query, AWS Redshift and others.

Adobe Gen Studio, which will be more integrated into the content supply chain and analytics workflows across customer journeys and campaigns.

Enhanced Adobe Journey Optimizer for B2C use cases as well as a standalone B2B edition. Customers will be able to harmonize brand campaigns as well as one-to-one customer interactions on the same surface.

As for Adobe Journey Optimizer B2B Edition, Adobe is taking the leads focus with Marketo and combining it with Accounts Based Marketing features. The biggest enhancement is the Adobe is adding the ability to target buying groups and the individuals with them.

Firefly 2.1, custom models and Firefly Services. Adobe Summit will mark the one-year anniversary of Adobe's Firefly text-to-image generative AI model. Adobe will add capabilities to customize Firefly with brand assets to create content variations with guardrails. Firefly Services will include a host of APIs that will automate asset variations to account for audiences, channels and geographies.

"The more we learn and observe the way people are using Firefly, the more we can identify ways to remove more friction out of the system and allow them to focus on the work that they that is meaningful for their business," said Justis.

• Adobe Firefly heads to enterprises, Adobe Express

A pilot for new commercial terms on Experience Cloud. Justis said Adobe has been running a pilot with its largest Adobe Experience Cloud customers revolving on unifying metrics.

"There's an interest in some inherent flexibility in the purchase process where rather than just having a kind of a line item or a contract for all of these given applications, we have been exploring a unified metric commercial model," said Justis. "Our arrangement with a given organization might be credit models where customers can apply those credits across multiple products. So, it gives them the flexibility of not feeling completely locked into how they intend to use one product for a year, two years, three years. They can say this is the level of commitment that we're willing to make to Adobe, and the Adobe Experience Cloud and have the flexibility to leverage credits."

Adobe said the pilots have been well received and are available to the company's top 1,000 accounts.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Canva said it will acquire Affinity, a UK company with a design suite that competes with Adobe's Creative Cloud.

The timing of the Canva-Affinity deal is notable given Adobe Summit kicks off this week.

In a blog post, Canva Co-Founder and COO Cliff Obrecht said Affinity's photo editing and design software is used by more than 3 million people. Canva plans to scale that reach by pitching Affinity to the 175 million people who have used its software.

Constellation ShortListâ„¢ Digital Canvas Workboards

Obrecht said:

"While our last decade at Canva has focused heavily on the 99% of knowledge workers without design training, truly empowering the world to design includes empowering professional designers too. By joining forces with Affinity, we’re excited to unlock the full spectrum of designers at every level and stage of the design journey."

Affinity's software is available on Windows, Mac and iPad. The company has 90 employees. The stack includes:

• Affinity Designer, a vector-based graphics application for illustrations, art, graphics and brand design.
• Affinity Photo, an image editor to cover a wide range of use cases.
• Affinity Publisher, a layout application for Web, publications and marketing content.

Version 2 of the Affinity suite for individuals goes for $164.99, but is on sale for $114.99. The company runs on a license model without subscriptions. A universal license for multiple business users is $109.24 per license currently. Each application in the Affinity family is also sold separately.

Canva has a free version of its software with tiers for businesses, teams and enterprises. Canva Pro for one person is $119.99 for a year, $300 for a team of 5 people and a range of plans for a minimum of 100 people.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

If 2023 was the year of generative AI pilots, 2024 will be about moving to production and 2025 will likely be warp speed. Why? The generative AI building blocks are falling into place.

In recent weeks, three mileposts have highlighted where enterprise generative AI was headed.

• Nvidia GTC highlighted how the software building blocks for generative AI are in place. The company launched Blackwell GPUs, but Nvidia Inference Microservices (NIMs) will ultimately be just as important. NIMs are pre-trained AI models packaged and optimized to run across the CUDA installed base.
• SAP, ServiceNow, Cohesity, CrowdStrike, Snowflake, NetApp, Dell, Adobe and a bevy of others are rallying behind NIMs.
• Nvidia's AI Enterprise 5.0, which will include NIMs and capabilities that will speed up development, enable private LLMs and create co-pilots and generative AI applications quickly with API calls.
• Palantir held its AIPCon meetup and customers outlined how they delivered value quickly. The use cases ranged from supply chain to defense to logistics to smarter workflows among field workers. Palantir has been using its AI Platform (AIP) to land, generate value and then expand.
• C3 AI held its Transform event where Baker Hughes highlighted how they used C3 AI's platform to optimize sourcing and inventory along with value delivered to the US Department of Defense, Con Edison, GSK and others. C3 AI's formula rhymes with Palantir's approach.

Taken as a whole, the generative AI use cases today are delivering value, but won't set the technology world on its ear. Frankly, some of the use cases sit at the intersection of AI, process mining and data science and you'd be hard pressed to declare the implementations as solely artificial intelligence.

This post first appeared in the Constellation Insight newsletter, which features bespoke content weekly and is brought to you by Hitachi Vantara.

Jensen Huang's keynote highlighted where generative AI use cases are going to go. First, the sheer pull of Nvidia's ecosystem--AWS, Microsoft Azure, Google Cloud Platform, Oracle Cloud Infrastructure, data platforms such as Databricks and Snowflake and enterprise software vendors--will put NIMs on the map. Enterprise AI 5.0 will be ubiquitous.

And priced at $4,500 per GPU for AI Enterprise, there's a big market opportunity for Nvidia, but nothing that breaks the bank. The cash register for Nvidia is still the GPU. That said, the software math for Nvidia is compelling--especially if Nvidia has 1 million GPUs in the field attached to AI Enterprise.

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Simply put, Nvidia is flooding the zone for generative AI use cases. Speaking to industry analysts, Huang was asked about enterprise use cases. He said:

"We have two avenues to take AI into enterprise. One avenue is people build up applications in the IT department. We have business application developers writing applications for forecasting and supply chain management. We have to create these AI modules and AI libraries for them. Business applications are just AI applications. Somebody's going to go off and build.

The other avenue is through the enterprise IT platforms, and I think that they're all sitting on a goldmine. They created tools and you can now create AI copilots to go use those tools. You're gonna have SAP create copilots and they're gonna get better and better. Instead of instead of hiring 100 business application developers, you have 100 and another 500 that are APIs."

Platforms appear to be the primary vendor goal at the moment. Palantir said on its fourth quarter earnings conference call that the company has covered nearly 200 use cases coming from its AIP Bootcamps. Palantir CTO Shyam Sankar said AIP is enabling the company "to integrate so many types of new data, video conferences, incident response calls, Slack rooms, PDFs, images, video, audio, and exploit them through the power of LLMs and ontology."

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Sankar said the real data that defines a process is in the conversations than the enterprise system. "What's in the enterprise process system is a lousy latent representation of this reality," said Sankar. "With AI and LLMs, you can't think your way through it. You have to get your hands dirty and work in anger to get use cases into production. In AIP, we have built a platform to deliver proof, not just proofs of concept, to our customers."

C3 AI CEO Tom Siebel said during one of his Transform 2024 talks that if you fast forward three years, you'll find that the entire enterprise application stack will be transformed. AI applications will be predictive and prescriptive and save billions of dollars.

"Let's fast forward three years March 2027. No CEO in the world will be able to withstand a board meeting where he or she was standing up without reporting what customer churn was, what device failure was, and the level of fraud.  When the tools are in place to prevent the failures, prevent the customer churn and make sure you can deliver the products on time it's big," said Siebel.

C3 AI as of Transform 2024 has deployed more than 47 use cases in generative AI across multiple industries.

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The bet here is that we're going to see a lot more enterprise use cases soon, but the real business value will be at the intersection of generative AI, process transformation, automation, scale and speed. It's also worth noting that enterprises are planning to allocate money to generative AI even if they haven't scaled funding yet. Deloitte's first quarter CFO Signals survey found that 64% of North American CIOs are looking to adopt generative AI with a focus on IT, business operations, customer service, finance and sales and marketing.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Sixty-two percent of CFOs say their organizations are allocating less than 1% of corporate budgets to generative AI next year, according to Deloitte's CFO Signals survey for the first quarter. Another 37% of CFOs expect 1% to 10% of budgets to be allocated to generative AI.

The findings, based on 116 respondents, are notable because they highlight how actual enterprise movement on generative AI has trailed headlines and vendor proclamations. Consumer companies plan to allocate more than 5% of their budgets to generative AI. Another notable takeaway is that 58% of CFOs say their boards are somewhat or very much encouraging genAI adoption in the enterprise.

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Here's the breakdown from the report.

The budgets may move once the returns on generative AI become clearer. Seventy percent of CFOs expect a 1% to 10% increase in productivity from using genAI with 13% of CFOs seeing higher gains. Productivity is the return metric of choice among CFOs. CFOs from larger companies expect the biggest generative AI productivity gains.

For instance, 9% of CFOs from companies with more than $10 billion in revenue are expecting productivity gains of more than 20% from genAI. Five percent of CFOs surveyed expect productivity gains of more than 20%.

Going forward, CFOs are valuing generative AI on workforce productivity and cost savings. A big chunk of CFOs surveyed, 24%, are uncertain how to value generative AI or had no measurement.

Across the enterprise, CFOs say IT, business operations, customer service, finance and sales and marketing are the top functions ripe for generative AI transformation.

The generative AI data from the CFO Signals survey come amid other key themes.  Other takeaways include:

• 40% of CFOs say now is a good time to take greater risks and the remainder are risk averse.
• 65% of CFOs say they believe the US equity markets are overvalued.
• 42% of CFOs say they were more optimistic about their own companies' financial prospects.
• 59% of CFOs saw North American economic conditions as good or very good, but 12% of CFOs saw Europe that way. Just 3% of CFOs China economic conditions as good.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Enterprises are betting on generative AI and digital transformation at the expense of other IT projects, but scaling AI is difficult and more foundational work is needed, according to Accenture CEO Julie Sweet.

Sweet, speaking on the company's second quarter earnings call, said Accenture saw 39 clients with quarterly bookings topping $100 million. The company also had more than $600 million in generative AI bookings to reach $1.1 billion in generative AI sales for the first half.

That's the good news. The bad news is enterprises are prioritizing large transformation projects that convert to revenue more slowly. Sweet said:

"We see clients continuing to prioritize investing in large-scale transformations which convert to revenue more slowly, while further limiting discretionary spending particularly in smaller projects. We also saw continued delays in decision-making and a slower pace of spending.

Our clients are navigating an uncertain macro-environment due to economic, geopolitical, and industry-specific conditions. And in response, we are seeing them thoughtfully prioritize larger transformations, building out their digital core to partnering, to improve productivity, to free-up more investment capacity to focus on growth and other initiatives with near-term ROI."

Revenue in the quarter was flat for the second quarter even though Accenture saw mid-single digit growth or higher in six of its 13 industries.

Overall, Accenture reported second-quarter revenue of $15.8 billion, flat from a year ago, with earnings of $1.71 billion, or $2.63 a share.

Accenture's outlook for the third quarter fell short of expectations. The company projected third quarter revenue between $16.25 billion to $16.85 billion, below Wall Street estimates of $17 billion. Full year revenue will be between 1% and 3%. Analysts were looking for growth of 2% to 5%.

Sweet said enterprises are now "near universal recognition of the importance of AI," but "most clients are coming to grips with the investments needed to truly implement AI across the enterprise and nearly all are finding it difficult to scale, because the AI technology is a small part of what is needed."

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Indeed, Sweet said companies with strong data and digital cores are moving quickly. Laggards are investing in digital core and new processes. "We are working closely with our ecosystem partners to help our clients understand the right data and AI backbone that is needed and how to achieve tangible business value," said Sweet, who noted that 2024 budgets were just recently set and there's caution about the economy.

Here's a look at some of the enterprise technology spending takeaways from Accenture:

• Enterprises pulled back on spending for Accenture services and smaller projects at the beginning of the year.
• Accenture is focused on market share and meeting customers where they are.
• Cloud, data and AI are leading priorities.
• Companies are substituting projects instead of adding to budgets.
• Foundational data projects are necessary, and those transformation projects are heavier lifts.
• Companies are dialing back services because they are more discretionary. Large transformations are happening because the need to replatform is critical.

Sweet said:

"You can't just jump to the great data foundation. You need to be in the cloud. You have to have modern platforms. The clients during these higher bookings rate are making big transformations oftentimes to be ready to put in the data foundation. Only 40% of workloads are in the cloud and 20% of those roughly haven't been modernized. Many of our clients haven't put in the platform--if you don't have the major ERP platforms that are modern, you don't create a data foundation to fuel GenAI. You've got to build the digital core. And there's a lot more to go."