Enterprise hits and misses - Enterprise AI finds its niche(s), sustainability gets a mixed review, and B2B buying goes omnichannel

Lead story - What's next for enterprise AI? Focus on specialization and regulation

Enterprise AI is in dire need of precision - what is it good for, and where does it fall short? What are the concerns - and where is the ROI?

Yes, you'd think these are straightfoward questions, but judging from the AI hype festival in my daily inbox, they are evidently not. Chris makes headway though, via AI - Clari CEO says niche is good when it comes to enterprise GPT. As Chris explains, Clari has skin in this particular game:

Earlier this month, the 10-year-old company launched RevGPT. The new GPT-powered tool queries data stored in Clari’s RevDB database, so users can ask critical revenue questions – in areas such as risk, sales goals, and forecasts – then act accordingly.

One advantage of generative AI in the enterprise - smaller/niche data sets, honed (made "smarter"?) via iterative user feedback:

Unlike the ChatGPT instances that have been trained on, and refer to, data scraped from the pre-2021 Web, RevGPT and its enterprise ilk solely access companies’ own, trusted data. Over time, their employees will experience a “flywheel effect” of increasingly accurate answers, actions, and outcomes from it, according to Clari.

Granted, this doesn't solve the overall concerns on AI's broad impact. But it's that restraint/focus that makes this use case happen:

What’s powerful for us is that we're bounded in our use cases. RevGPT is not this general-purpose ‘ask anything of anything in the world’ application. And if you combine that with the proprietary data we have, we're able to make powerful predictions and suggestions in every ‘revenue moment’ for every revenue-critical employee.

We'll see different flavors of GPT use cases for other departments and roles. I will point out, however, that accurate advisory/forecasts is not a done deal, even if your AI is well-trained. Data quality/depth/variety factors heavily - including external trend data. As for the broader concern, Derek revisits that topic in AI regulation is a priority, but so is awareness and education - let’s not repeat the same mistakes:

Government, the private sector and research institutions should be working together to think through some national campaign messages that could help citizens and users better prepare for how AI might present itself online, as well as some best practice approaches for how it could be used.

I've spent time geeking out with data scientists lately - and I disagree with the sentiment that generative AI will be performing human-level intelligent tasks before long. This technology has limits in how "intelligent" it can become; adult supervision will be required. That's precisely why the enterprise will make progress - by actually limiting the data sets, and focusing the AI where it can excel.

So with that in mind, do I find Derek's call to action overly-strident? No - because as I've said before, generative AI's handful of killer apps include deep fakes and disinformation at scale. That's within AI's current capabilities - and we need to act.

Diginomica picks - my top stories on diginomica this week

• It's not easy being green...but it could mean a bump in tech spend for enterprise vendors, according to new IBM data - Stuart examines fresh IBM sustainability data from IBM, and finds a mixed bag at best: "As noted above, the impact of the macro-economic crisis on the gap between public intent and public practice when it comes to sustainability initiatives is still unclear. All the right noises are being made, but how much of that ‘greenwashing’ is going on remains to be seen."
• The role of CIO at Shell and changing the energy system from molecules to electrons - Mark looks at how Shell is changing, and the technology in play: "Cockriel says Shell is analyzing how EV charging algorithms, combined with artificial intelligence (AI) will help the firm generate energy to meet peaks in demand, and data is becoming increasingly important."

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

• FinancialForce talks up the need for joined-up processes in services organizations - Phil weighs in on FinancialForce's quarterly release update: "What's interesting however is that most of the service organizations FinancialForce has spoken to about its customer success product are not yet seeing this function as one that works hand-in-hand with the service delivery teams."
• The state of SAP - DSAG explains its position on hybrid cloud and S/4HANA feature parity - It's a pivotal time for SAP customers, and DSAG, the German-speaking SAP user group, has a lot to say. Has DSAG's position on cloud changed? And why is on-premise feature parity their hottest topic? My latest interview with DSAG digs in.
• Dassault creates Big Context with generative AI - George has another enterprise angle on generative AI, this one including IoT: "Things are a bit fuzzier when it comes to data from physical things, supply chain partners, and third-party data providers. In these cases, AI-powered data translators could go a long way toward contextualizing information appropriately for the problem at hand."

More vendor picks, without the quotables:

• Coupa Inspire - giving CFOs more visibility and control over spend - Phil
• Contentstack integrates ChatGPT into headless CMS editing environment - Barb
• Cornerstone refocuses skills development on the individual with its Opportunity Marketplace - Phil

Jon's grab bag - Gary penned an inspiring use case, How StepUp.One offers global online opportunities to its refugee-only recruits. Phil shares a highlight from the Trailhead event in Salesforce CMO Sarah Franklin and the Trailblazers building new careers in tech. Finally, Derek covers a potential expansion of AI oversight in UK’s data protection regulator calls on government to widen scope of ‘AI fairness’ principle to include development of AI, not just use.

Best of the enterprise web

My top seven

• Workday AI and ML Innovation Summit: Chasing the Eye of the AI Storm –  a thorough review from Amalgam Insight's Hyoun Park, including insights on generative AI that apply well beyond Workday.
• The “Sell to Business” or “Sell to IT” Debate Is For a Bygone Era  - Gartner's Hank Barnes getting it done: "The new reality is that in savvy organizations, that see technology as a source of advantage, business and IT work together for better business outcomes vs. the old days of fighting for power and control.   This is not the majority, but for providers, the “avoid IT” strategy is likely to deliver more and more diminishing returns."
• Why Independent ERP Software Consultants are a Must-Have for Successful Digital Transformations - Yeah, Eric Kimberling snuck in a plug for Third Stage Consulting in here, but the fact remains: project bias is real, and independent advisors matter: "Project bias continues to be a problem even after selecting software... During implementation, project bias can undermine the process by hiding project risks and problems to preserve revenue streams and software footprints."
• The multiplier effect: How B2B winners grow - The consumerization of B2B is certainly kicking in: "About 70 percent of decision makers are prepared to spend up to $500,000 on a single e-commerce transaction."
• The Great Flowering: Why OpenAI is the new AWS and the New Kingmakers still matter - I was waiting to hear James Governor's take on the impact of OpenAI on developers, and here we have it.
• ‘Overemployed’ Hustlers Exploit ChatGPT To Take On Even More Full-Time Jobs -  Illuminating, interesting, at times disturbing. Important. Though I will point out: these strike me as highly skilled individuals who are able to do more - but ChatGPT can't actually handle the entire job for them, not even close. Still, the gruntwork they've reduced is an early indicator on how jobs will shift.
• ChatGPT is making up fake Guardian articles. Here’s how we’re responding - Another interesting angle on ChatGPT gone wrong. But I found The Guardian's statement about their own potential future of use of generative AI even more interesting - though it's early days.

Whiffs

So, Netflix had an unflattering debut into live streaming, prompting all kinds of Love is Blind punnery and social media satire. My fave came from Blockbuster:

You know you've had a rough round when Blockbuster gets a right hook to land... Here's one generative AI use case I'm not a fan of:

Just what the world needs, more mediocre, content-free press releases. Fortune cookies on the other hand? Spot on:

Though perhaps the revenue potential is a tad limited... 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.