Enterprise hits and misses - AI hype vs real world use cases, enterprise earnings face headwinds, and buyers get a new BS detector

Lead story - AI use cases, lawsuits, and ethics - where will we land?

On DisruptTV last week, I said that AI defied Gartner. Technology supposedly rides Gartner's hype cycle into the "plateau of productivity," but AI broke those rules, jumping right into what I coined the "mountain of malevolent magnificence" (no word yet if Gartner is going to add that one to their hype).

The week in diginomica shows the contrast: on the one hand, we have maturing AI use cases. Barb assessed that in Viva la revolution! AI use cases in B2C commerce – it’s time to move forward. Barb notes that up until this point, the primary AI use cases for e-commerce and marketing are about AI for data normalization, via customer data platforms (I'd argue that pricing and recommendation engines are also mature AI-for-commerce use cases - see: Amazon's e-commerce domination). But as Barb writes, the use cases are changing:

Now that you have that complete and accurate view of your customers, what’s next? How do you act on it and make it a routine part of your business process?

Judging by the extravagant hype festival, ChatGPT and generative AI might seem like what's next:

We’re on the cusp of (another) ‘revolution’ where AI will impact marketing and commerce. That’s the revolution everyone is talking about today. You can’t read three posts on LinkedIn without someone talking about AI-generated content and images(ChatGPT, Dall-E). AI-generated content isn’t exactly new - many solutions are available that help generate subject lines and ad copy using the power of AI and ML - but this new breed of technology is pushing us further, faster.

But Barb says another step comes first: acting on that "normalized" data:

Brian Walker of Bloomreach believes companies aren’t using AI effectively in their marketing channels - not search, merchandising, or digital experience content. That, he said, is the low-hanging fruit for most companies today.

I interpret that to mean: not everyone is good at what Amazon does. However, ChatGPT has forced the issue: marketers must take positions on how to use generative AI tools: how they help, and when they are not ready - or even inappropriate. This is where the infatuated tech media is of no use - implying that generative AI can pretty much write all your marketing copy right now (it can't, unless you want you copy to be enthusiastically mediocre).

But now we're in the ethical/legal quagmire: most generative AI tools trained on "publicly available" data sets, and creators have different definitions of opt-in than AI enterpreneurs. Chris takes that on in Generative AI - will it be summer for humanity? Or a legal winter for vendors?

If an artist is alive and/or their work is in copyright (and therefore not in the public domain), then it stands to reason that a ChatGPT simulation must be a derivative work.

Indeed, it can ONLY be derivative. That much would be implicit in the instruction ‘Write a Nick Cave song…’ In other words, generative tools are not AI at all, but are really little more than derivative work engines. And derivative works are covered by copyright law.

No surprise: enterprises are taking a more deliberate approach than the ChatGPT carnival barkers, or Microsoft for that matter, which can afford to spend oodles trying to redefine a search game (Bing) it has been unable to win. Chris again:

A handful of organizations have rushed into early adoption, but the majority have yet to be convinced that ‘plaything’ equals ‘useful tool’.

My view on generative AI is different. It's already here. As for preparing, we're probably behind. See also, Chris' Capital idea? The vital importance – and dangers – of ethical AI investment.

Vendor analysis, diginomica style. Time to make sense of another batch of enterprise earnings:

• ServiceNow eyes $8.5 billion revenue in 2023 - delivers solid quarter - start with the brightest news, via Derek: a comparatively solid earning report from ServiceNow. Oh, and CEO Bill McDermott already said there are no layoff plans for 2023 (not many of the biggest enterprise software companies can say that). McDermott was, predictably, glowing about his company's appeal: "C-level buyers don't want long-term road maps to clean up a siloed mess of point solutions. They want integration, speed, automation, great experiences and business impact."
• Optimize the positive! Microsoft CEO Satya Nadella makes his pitch as profits slump and layoffs confirmed - A bit more sobering over at Microsoft. As Stuart notes: "The PC business performed in line with company expectations, but fell 19% year-on-year, with “execution challenges” related to the Surface business cited. Ad spending decline more than expected, which hit the LinkedIn Marketing Solutions arm. There was “moderated consumption growth” for Azure, while spending on Windows products was down 39%. The brighter spot was Microsoft Cloud revenue, which was up 22% year-on-year."
• SAP announces 3,000 layoffs following "one of the most important years in our history" - A bit rockier at SAP, which joined the lengthly list of software vendors with significant layoffs. Stuart parses a complicated docket of earnings news, including SAP confirming plans to divest its share in Qualtrics, and a 47% year-on-year drop in profits for Q4. Still, CEO Christian Klein expressed confidece in SAP's overall cloud and subscription momentum.
• Qualtrics experiences 36% full year growth as SAP mulls selling its remaining stake - Meanwhile, it's sunny side up at Qualtrics. Stuart quotes CEO Zig Serafin: " We believe that we're faring better than others because of the value of the platform, particularly right now, where companies are honing in on technology and solutions that affect the way that they can drive performance in their own companies."

What do all these reports have in common?

• An acknowledgement of a tougher buying environment (aka "headwinds").
• Yet tech spending persists, despite downgraded forecasts.
• Consumer tech exposure is a surefire earnings drag; I believe vendors more dependent on bigger, multi-year projects are more exposed also.
• Enterprises are still serious about tech modernization, but in a prioritized manner than insists on quicker wins - with a longer term platform of choice in mind.

It's an unsparing environment for startups, immature technologies and unnecessary headgear, but as these earnings reports show, achieving results amidst the fray is clearly possible.

More top picks from our vendor coverage:

Jon's grab bag - This week, Phil revealed How mmhmm cut its quarterly board meetings from 4 hours to 90 minutes. Oh, and "Why VR business meetings are a stupid idea." (Preach!) Speaking of tech hype that got set on the back burner, Chris revisits Quantum in The quantum tipping point - where are we today? (Answer: not close, but Quantum use case development is a slow burn).

Finally, in The next best thing since the last best thing! Some tech hype models to live by, Brian gave enterprise buyers a spiffy polish of their BS filters, just in time for a flurry of ChatGPT sales calls:

The next best thing since the last best thing! Some tech hype models to live by. Parroting and/or amplifying founder claims without verification is, at a minimum, ethically challenged and intellectually weak. And yet, bloggers, influencers and traditional media aid and abet the continued hyping of these new tech products simply because the articles on these new technologies attract readers and are easy to produce (especially when the founder/investor spoon feeds them copy and soundbites).

Looks like Brian has plenty of Sriracha on hand...

Best of the enterprise web

My top seven

• A CEO’s guide to the Metaverse - a tad too much Metaverse handwaving for me, but still, a useful piece if your company is trying to figure out where the action is. I'll be interested to see if there is more in play than high-end sales and digital twins, which I don't personally consider a Metaverse use case.
• What’s Unique About NFTs? - Constellation's Steve Wilson has a way of writing about NFTs and blockchain in a balanced way, without losing the sharp edge. He hits on the concept I've hone on: decentralization is a key factor in these technologies. Once we understand that, the enterprise use cases clarify, but also decrease: "The key is to understand precisely where decentralization surfaces with these technologies. Decentralization is part of the digital zeitgeist but historically it’s unusual in business and society. The vast majority of us live and work within myriad authority structures. We deal on a daily basis with intermediaries and institutions that have evolved over decades (or even centuries) as the most efficient ways of organising complex communities. It is rare that these structures can be decentralized, and ever rarer that the benefit of decentralization is worth the cost. Bitcoin, NFTs and DIDs all do something that is truly unique, but it is very specialised."
• Executives to tech teams: Reinvent us, and make it quick - Joe McKendrick digs into Accenture data and finds, no surprise, "total enterprise re-invention" is not a piece of sponge cake. "Most companies aren't quite there yet. Only 8 percent could be considered to be "reinvented" along these lines, the survey of 1,516 enterprises shows."
• As activist investors target Salesforce, what’s next for the CRM giant?  - Ron Miller's interesting post explains why working with activist investors isn't necessarily about playing defense.
• Pivot to ChatGPT? BuzzFeed preps for AI-written content while CNET fumbles - I think Buzzfeed is overreaching here, but hey, investors like it - and that buys runway. The article implies that Open AI will essentially take the expertise of a particular author and run with it. Good luck with that...
• Replacing workers with AI? Don’t forget the retraining fees - The Register raises some interesting points about the unanticipated costs of keeping training data sets current, and how this could potentially exclude many companies from duplicating whatever the deep pockets do with generative AI. I'm not so sure, but understanding the costs (and logistics) of updating training data does need more attention.
• Netflix’s New Chapter - No one is better at analyzing the business model crossroads of the big tech giants than Ben Thompson. This deep dive into Netflix, and the case for why new content is now the differentiator, is a good "cup of coffee" type of read.

Whiffs

I almost never say a bad word about grabbing a refreshing nap, but...

Smart homes are only so smart as the devices that are actually connected at any given time:

Do the US credit agencies actually try to protect consumer data, or are they busy with something else?

By the way, the worst tech fails of 2022 lists are all out by now... MIT put out a particularly hard hitting one... 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.