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Enterprise hits and misses - generative AI gets enterprise scrutiny, Credit Suisse gets a lifeline, and digital transformation failures get dissected

Jon Reed Profile picture for user jreed March 20, 2023
Summary:
This week - tech vendors have a lot to say about generative AI - and so do I, thanks to new field reports. Credit Suisse avoids the abyss (and so do we), and tech spending gets a fresh look via the latest data. In the whiffs section, I debut my first generative AI animation.

Lead story - Generative AI in the enterprise - tech vendors stake their claims, but are we there yet?

All of sudden, enterprise vendors of all flavors have a game-changingly wonderful generative AI offering in the works, perfectly timed with... consumer fascination, and VC funding of a zillion startups. Go figure! Generative AI was certainly a hot topic at last week's Workday Al/ML Innovation Summit, where Workday briefed an analyst gathering on their push to lead the pack in AI for enterprise applications (Constellation's Holger Mueller filmed our review of this event over the weekend; I'll post the link here as soon as it's out).

Meanwhile, Stuart captured the broader view via a review of Morgan Stanley's Technology, Media & Telecom Conference (Chatting about ChatGPT - enterprise tech leaders set out their generative AI stalls). Stuart cites several pending use cases, including one we heard about at the Workday Innovation Summit. He quotes co-CEO Aneel Bhusri:

People hate writing performance reviews. The way that we're using generative AI is to look at taking an input, either a combination of, maybe, a review done over Zoom, all the data that you already track in Workday from a performance perspective, and then [generative AI] actually writing the performance report that nobody likes to write. In our early trials, it works really well.

How soon will these kinds of innovations hit? As Stuart reports, Bhusri "suggested the first wave of use cases from Workday will be seen in around six or seven months." Another speaker at Morgan Stanley's event, ServiceNow CEO Bill McDermott, is also bullish. Stuart quotes McDermott:

We're big on AI, we’re big on generative AI. We do think this is a transformational moment. I know the hype cycle is high right now, but it's absolutely a transformational moment. When you think about Netflix, as an example, getting its first million users in 3.5 years, and Twitter doing it in two years, and ChatGPT doing it in five days, you know something's going on out there.

Stuart concludes: "The ChatGPT hype is real, but so is the potential for enterprise application." Agreed, but as I said on DistruptTV last Friday, generative AI is a cultural phenomenon, but it will be an enterprise AI evolution, not revolution. That distinction is important. The enterprise is about mission critical applications running at scale. Generative AI's inherent limitations that produce some flawed output dictates two things:

  1. Avoid high risk use cases (something OpenAI itself warns users about), and
  2. Keep a human-in-the-loop to supervise/edit/curate output, which limits both the ROI and the revolutionary force of the technology.

Therefore, expect adoption to proceed, but not for swaths of jobs to disappear in some massive disruption. The use cases are intriguing, but they are also domain-specific.

And yet, as I also said Friday, I think the enterprise could take the lead on curbing the problems with generative AI I documented in Microsoft's Bing ChatGPT search bot is still looking for answers - but is AI for enterprise search worth a look?

Enterprise tech always seems to lag behind the consumer tech experience. But could generative AI change that? One of ChatGPT's weaknesses is the garbage-in, garbage-out problem of training on the open Internet. But generative AI for the enterprise could be focused on more controlled data sets, with better output guardrails added early, not after the fact. Enterprise users could help to further refine the models, via iterative reinforcement learning on the data set, something ChatGPT doesn't currently accommodate.

As Workday emphasized in its approach to enterprise AI, there could also be notable improvements in explainability, transparency of data sources, and helping users understand the "why" behind the recommendations in AI systems. You can pick up on some of this via Stuart's Bhusri quote:

Our data is absolutely clean and it's normalized. So [with] ChatGPT, you can ask good questions and you get some wacky answers at times, because the Internet is the Wild Wild West, so maybe it's actually getting wilder. But our data is absolutely constrained, normalized, it's clean. And as a result, we can produce pretty powerful results.

Generative AI is one more way that AI will impact the enterprise, but it's hardly the only one. Yes, the overhype gets me (predictably) riled up, but at least the emperor has some clothes on this time around. Unlike the Web 3/Metaverse/tokenized blockchain future-of-everything celebrated on wishful thinking slide decks, generative AI has actual user adoption on its side.

Diginomica picks - my top stories on diginomica this week

Vendor analysis, diginomica style. Here's my three top choices from our vendor coverage:

Earnings reports keep rolling in:

A couple more vendor picks, without the quotables:

Jon's grab bag - George examines the real world considerations of digital twins in How memory management is key to scaling digital twins in the cloud. Shadow IT never dies, and neither do spreadsheets. But some things have changed - Neil looks back (and forward) in Shadow IT never dies - why spreadsheets are still running your business. Chris takes another angle on generative AI in the enterprise in ThoughtSpot gives GPT an enterprise analytics focus.

Finally, it was time for me to update the satire with more real world tips: Are we ever going to get small events right? Lessons from the ups and downs of vendor analyst days. "The constituents are demanding; the temptation to pile on forklifts of product information is ever-present." Ah, but there is hope yet...

Best of the enterprise web

Waiter suggesting a bottle of wine to a customer

My top seven

  • UBS buys Credit Suisse for $3.2 billion as regulators look to shore up the global banking system - This marks the second time the financial markets have stared into the abyss in just a few weeks. Let's hope we've learned a lesson in risk planning; that is one steep cliff.
  • The pandemic-induced tech binge is over and the cloud champagne has gone flat - Phil Fersht with a notable post on the state of tech spending, via HfS research data: "Against this backdrop, the data from the survey tells us that discretionary spend is disappearing fast. This is neither the time for fancy innovation projects nor lavish client entertainment. However, spend for managing the core operations of organizations is holding up. We haven’t come across indicators that would suggest a softening in sourcing activities beyond service providers privately complaining deal cycles are lengthening and harder to close."
  • Why Do Digital Transformations Fail? - Eric Kimberling with a detailed post on ten ways transformations go awry. If forced to pick one, I'd go with the (over)"dependence on technical implementors": "Another challenge and risk that digital transformation teams often face is an over dependence on the technical implementers. Whether it be the value-added reseller, the software vendor themselves, or a system integrator, deferring too much to them and assuming that they're going to run the project for you means you're missing out on a lot of things."
  • Work-From-Home Regulations Are Coming. Companies Aren’t Ready - Interesting data via MIT's Sloan Management Review: "Allowing large numbers of employees to work from anywhere is starting to get a lot more complicated." Yes, but this isn't a new revelation. These issues have been building for a while. Remote work policies are in a fascinating place: cost savings, office culture, talent, economic pressures - where will this land?
  • Analysis: Huawei's Journey of Digital Transformation and Sustainability - Constellation's Dion Hinchcliffe with a deep analysis of a major tech player we typically don't get enough analysis of.
  • 7 Predictions for SAP Customers in 2023 - UpperEdge's Len Riley issues fresh advisory for SAP customers. Based on my recent talks with SAP leadership, I can confirm the 2027 ECC standard maintenance deadline will not be extended.
  • The role of great technology in the run on Silicon Valley Bank - Vijay Vijayasankar makes a key point bank regulators and managers will need to better account for: "Frictionless transactions have always been a good thing. Managing the risk of advanced tech and social media (which is also about great technology that advanced fast) fueling a Bank run will be a new risk management muscle to exercise for all parties – but one I am sure they will start thinking about seriously and real quick!"

Overworked businessman

Whiffs

Yours truly got in on the generative AI action this weekend:

Another fabulous use case... Oh, and isn't it a drag when you find out that being an influencer means being accountable for the BS you endorse without any grasp?

And when it comes to security, things change fast, and not always for the better:

See you next time... 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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