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Enterprise hits and misses - the AI circus faces off against user trust, transformation meets reality, and speculations on Google and HubSpot clog social media

Jon Reed Profile picture for user jreed April 8, 2024
Summary:
This week - new data underscores gen AI's trust, data and cost issues, but industry use cases also gain stream. Transformations can fail but are we extracting the right lessons? Google and HubSpot speculation runs rampant, and I get a PR pitch for... mermaids?

professor-questioning

Lead story - The AI circus versus trust and harsh realities

The potential and problems of gen AI are tough to unravel from each other. But in the enterprise, trust and data quality are paramount to adoption - and results.

Rebecca gets the ball rolling with some eye opening stats in: Roll up, roll up! The AI circus is open for business with trust center stage in the ring:

In Valoir’s recent report, Language Matters – AI User Perceptions, we found that the AI trust battle is just getting started.

In our recent study, we found that 84% of workers say they have experimented with generative AI, either on their own or at work. However, although there has been plenty of press about the current and expected benefits for users of AI, potential users are skeptical. In fact, 17% of workers believe AI is about as useful as a screen door on a submarine at work, and only 15% thought that AI could help them jumpstart a writing task.

Some of these dismal percentages are likely the result of using early versions of these tools; now part of the AI trust problem is luring users back for another go. The trust issues don't stop there:

Privacy violations top the list of worries, with 51% of workers expressing fears about potential privacy violations by AI systems. Additionally, apprehensions about AI acting autonomously without human intervention (45%) and the perceived threat of AI replacing human roles (38%) add layers of complexity to the trust equation.

Valoir found that "trust" is also tied to AI brand credibility, but that's a complicated picture too, with some vendors (e.g. Google and Microsoft) winding up on both lists (trust and don't trust). So how do we get to a different place? Rebecca concludes:

So, in the immortal words of PeeWee Herman, what is all of this supposed to mean? It means that trust will be key for both buyers and potential users of AI, the battle for trust is just beginning, and it’s anyone’s to win – or lose.

In the enterprise, much of this will play out on a per-industry basis, as Stuart documented in Retailers excited about gen AI; retailers don't have the data foundations in place for gen AI - Salesforce study exposes some harsh realities. Though retailers are optimistic about use cases like personalization, pesky data issues cloud the AI picture:

That’s the positive. The negative bring us back to that damn data issue. Drilling down from the headline conclusions, the underlying detail doesn’t get any more comforting. The study finds that only 17% of respondents reckon to have a complete, single view of their customers. Nearly half (49%) are still in the preliminary stages of building or even considering the creation of a complete customer data profile.

AI trust is best examined by use case - with the degree of human oversight a factor as well. It is easier to imagine trustworthy AI for personalization than for autonomously approving mortgages. Medical use cases are more appealing when AI is used to assist in diagnostics, not for determining care eligibility.  Not trusting AI for the latter feels like more like a virtue than an error. As for retailers, Stuart notes the upside as well:

Personalization and customer service use cases are cited as target areas for generative AI adoption by retailers. The study finds that just over a third of retail employees are using gen AI today, with that expected to rise to 45% by the end of next year. Of that percentage today, some 93% say they are already using generative AI around personalization, in the form of email copy or product recommendations to customers.

Personalization can certainly drive sales; no arguing with the data there. But as I hashed out with Thomas Wieberneit last Friday, AI-based hyperpersonalization is no simple thing to achieve.

Diginomica picks - my top stories on diginomica this week

  • Revolut Bank CIO invests in change - Mark Chillingworth profiles a CIO with a transformation agenda: "Product-based teams that include technologists working with product owners, UX designers and business line operations are becoming popular, but as Peñacoba says, in traditional banks, a new way of working and new types of teams are hard to embed with people who have worked a certain way for 30 years. So, what is the role of the CIO?"
  • Why Amex GBT embraces AI with executive oversight - George examines an aggressive AI play, albeit with plenty of adult supervision: "Amex GBT’s example of appointing AI leads across engineering, finance, customer, and employee sides is a smart move. It also has a greater chance of balancing the incredible amount of AI hype against practical opportunities to generate business value safely."
  • HR, meet AI - everything changes...perhaps - HR is one of the most promising - but also problematic - areas for HR. Cath digs in: The upshot is that HR headcount is unlikely to fall much. Instead, AI will likely be used to automate transactional activities, leading to the creation of 'augmented roles.'"

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

Oracle event coverage and use cases - post CloudWorld London, Oracle stories kept coming: 'Build once, use many times' - UK Crown Representative Nick Griffin on how firms like Oracle can help improve government service delivery (Stuart), and, fresh back from an Oracle NetSuite event in NYC, Brian filed SuiteConnect NYC - helping customers scale with Oracle NetSuite.

A few more vendor picks, without the quotables:

Jon's grab bag - I have not heard of the Milk Shed buyer predicament. From how Martin describes it, I'd prefer not to get milked anytime soon: The 'Milk Shed' model - how enterprise buyers can avoid being semi-skimmed.

The long tail of established users committed to an old technology and content to be `milked’ for years is fading fast: the pace of change in IT means the long tails are getting rapidly docked.

Indeed. Finally, I took another crack at Avasant Research's gen AI project report in "Your generative AI is your new brand" - so get it right! Lessons from Avasant Research's report on real world gen AI: I asked the authors: "How do you think gen AI costs will impact adoption? Will vendors effectively bundle these costs into existing licenses or affordable add-ons? Or will cost be a factor that limits implementation of gen AI solutions until cost/operating expenses are reduced?" Their answers were revealing - though gen AI costs are going to be a moving target for a while...

Best of the enterprise web

Waiter suggesting a bottle of wine to a customer

My top seven

Overworked businessman

Whiffs

On the more serious side of whiffery:

But we could fill this column with that type of thing most weeks. I can't say the same for this pitch, which Brian Sommer thoughtfully forwarded to me:

PR pitch for mermaid experts
(via Brian Sommer)

Thanks for throwing my hat in the ring, Brian! I don't think I can top that one, so let's quit while I'm behind...

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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