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Enterprise hits and misses - gen AI spending meets ROI pressure, AI readiness is a thing, and AR/VR has a vision problem

Jon Reed Profile picture for user jreed June 24, 2024
This week - gen AI gets the ROI scrutiny, but Accenture notches $2 billion in gen AI sales. Is AI readiness an economic force unto itself? AR/VR gets an upgrade, and I pay tribute to Neil Raden and his outspoken take on AI ethics.


Lead story - Accenture notches $2 billion in generative AI revenues, but AI readiness is the bigger story

Accenture has now notched $2 billion in generative AI sales in 2024, as Stuart notes in Accenture clocks up $2 billion in generative AI sales so far this year - with more to come, according to CEO Julie Sweet. Still, as Stuart points out, it's early days: 

Two important caveats here - that’s impressive growth, but gen AI revenues are still a drop in the ocean when placed against Accenture’s quarterly total of $16.47 billion. The other point to note in terms of shaping future growth expectations is that those numbers are coming from 'smaller projects as our clients primarily are in experimentation mode'.

Duly noted, but I think there is a bigger storyline here: AI readiness is a thing, and it is luring companies into tech (modernization) spending. That may end up boosting Accenture's numbers more than gen AI does. Stuart quotes Accenture CEO Julie Sweet: 

It is important to remember that while there is a near universal recognition now of the importance of AI, which is at the heart of re-invention, the ability to use gen AI at scale varies widely with clients on a continuum. With those which have strong digital cores genuinely seeking to move more quickly, while most clients are coming to the realization of the investments needed to truly implement AI across the enterprise, starting with a strong digital core from migrating applications and data to the cloud, building a new cognitive layer, implementing modern ERP and applications across the enterprise to a strong security layer. 

Enterprise hypothesis: AI can't fix what ails you (e.g. legacy landscape and 'messy' data bottlenecks), but applied properly, it can accelerate what you do well. Sweet continues: 

Nearly all clients are finding it difficult to scale gen AI projects because the AI technology is a small part of what is needed. To re-invent using technology, data, and AI, you must also change your processes and ways of working, rescale and upscale your people, and build new capabilities around responsible AI, all with a deep understanding of industry, function, and technology to unlock the value. And many clients need to first find more efficiencies to enable scaled investment in their digital cores and all these capabilities, particularly in data foundations.

Ergo, Accenture believes it is well-positioned for this 'AI readiness' work: 

In short, gen AI is acting as a catalyst for companies to more aggressively go after cost, build the digital core, and truly change the ways they work, which creates significant opportunity for us.

This is why, even if gen AI project adoption is not setting the world on fire, it is still impacting IT modernization (more on that in my picks below). Another key to gen AI progress? We need more industry use cases, such as the interesting health care scenarios articulated in Stuart's "Everything we have accomplished has led us to this moment today!" - AI rhetoric, sales pitches and use cases from HPE CEO Antonio Neri. Then there is the combination of other forms of AI, leading to new use cases, as George documents in ABBYY – how IDP is paving the way for gen AI success. This doesn't change the obstacles gen AI needs to overcome around trust, cost, pricing, and ROI - but it shows that evaluating gen AI means looking at how gen AI is sparking a modernization/data conversation.

Vendor analysis, diginomica style. The spring event coverage rolls on (and on, and on...).  

Derek was on the ground at Pure Storage Accelerate, documenting some of the use cases behind Pure Storage's earnings success: 

Chris filed more stories from a prolific trip to .conf24, Splunk's first user event since Cisco acquired them:  

A few more vendor picks:

Jon's grab bag - Mark Samuels posted another nifty Snowflake use case: How Eutelsat OneWeb uses real-time data to ensure its satellite network is up and ready. Nice to see someone finally address the AR/VR nausea problem. George is on the case: AR/VR has an image problem. Holographic light fields could be the answer

To wrap the diginomica week, I tipped my cap to an unwitting mentor: If we don't get real about AI ethics, that's on us - a tribute to Neil Raden. Neil was an outspoken advocate for a different kind of AI ethics; may the ideas (and the conversation around them) live on. 

Best of the enterprise web

Waiter suggesting a bottle of wine to a customer

My top six

  • In spite of hype, many companies are moving cautiously when it comes to generative AI - Over at TechCrunch, Ron Miller delvers into the gen AI adoption question: "Companies are extremely interested in generative AI as vendors push potential benefits, but turning that desire from a proof of concept into a working product is proving much more challenging: They’re running up against the technical complexity of implementation, whether that’s due to technical debt from an older technology stack or simply lacking the people with appropriate skills. In fact, a recent study by Gartner found that the top two barriers to implementing AI solutions were finding ways to estimate and demonstrate value at 49% and a lack of talent at 42%. These two elements could turn out to be key obstacles for companies." I don't believe this conflicts with the Accenture analysis above; Miller also alludes to obstacles such as technical debt that prevent easier AI deployments. Generative AI might be influencing spend, but scaling that inside organization is another story. 
  • Google Says AI Is Magic. Businesses Are Finding Out It Isn't. - Over at Bloomberg, Parmy Wilson issued an opinion piece that goes further. Olson argues that "To avoid a painful correction, tech companies must start managing expectations properly." Olson adds: "No, Google. AI isn’t “magic.” And to frame it as such — even in a tweet — is already leading to disappointment. Further down the value chain, away from the glow of Nvidia, lurk signs of discontent. Businesses have cut back on whizzy new AI tools out of concern for hallucinations, cost and data security. The proportion of global companies planning to increase spending on AI over the next 12 months has slipped to 63% from 93% a year earlier, according to a recent survey of 2,500 business leaders by software company Lucidworks Inc." 
  • Mitigating AI Risks: 5 Essential Steps for Secure Adoption - Freeform Dynamics hits on the AI risk factors any successful AI project must check off. 
  • Why AI solutions have just three months to prove themselves - A different set of stats via ZDNet's Joe McKendrick, but again - caution over AI spending carries the day - despite high expectations.
  • How to stop Perplexity and save the web from bad AI - Casey Newton looks at alternative web models, now that big tech companies seem determined to ingest the web and worry about the fallout later. 
  • Workshopping Gen AI enterprise creativity part 2 - parsing the BS with Brent Leary - the audio podcast replay of my video discussion with surprise guest Brent Leary on what he learned this spring about gen AI, and why "creativity" can be a limiting lens. 


Overworked businessman


I should call a whiff on myself for picking six AI-related stories, but I did spend a good chunk of time looking into good non-AI stories that mattered. If you spotted one I missed, post it in the comments; I came up dry this time. But we've got whiffs... That went well: 

So did that: 


If you find an #ensw piece that qualifies for hits and misses - in a good or bad way - let me know in the comments as Clive (almost) always does. Most Enterprise hits and misses articles are selected from my curated @jonerpnewsfeed.

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