It's not too late - You and your team might not be as far behind the AI adoption curve as you think.

Nate Buchanan Director, Pathfindr

In this week’s edition of The Path, we’re going to take a page out of the Gen Z playbook and do something that doesn’t come naturally to me at all - “react” to content someone else has posted.

Specifically, we’re going to unpack a particular finding in The State of Generative AI in the Enterprise, a report based on data gathered in 2023 and published by Menlo Ventures. Over 450 enterprise executives were surveyed to get their thoughts on how Gen AI adoption has been going at their companies. It’s particularly relevant for us at Pathfindr because one of our core capabilities is helping enterprises experiment with (and scale) AI applications, so we like to keep tabs on what the data says about how - and how often - companies use AI.

I found the results of this report very surprising. Based on all the hype surrounding Gen AI last year, I fully expected that many, if not most, companies would have significantly increased their budgets for AI experimentation and adoption. Instead, the survey found that across most enterprises, investment in Gen AI at the end of 2023 represents less than one percent of total cloud spend. It also found that the number of enterprises using AI increased by only 7% between 2022 and 2023, which seems like a paltry number given the intense interest in AI during that time period and the growth in its capabilities.

Think about that. Most people in the know agree that the Gen AI represents a technological shift on par with the advent of the internet (or at least the release of Pokemon Go). Yet the world’s biggest companies are soft-pedaling their investment in it. Why?

There are probably a wide range of reasons for this, some of which we’ve addressed tangentially in previous editions of this newsletter. One reason could be that decision-makers just don’t think AI is worth the spend. Another might be that budgets are being slashed everywhere, so placing a big bet on an as-yet unproven technology would be unwise. It could also be as simple as analysis paralysis: having so many options for AI tools that they don’t know where to start.

None of those seem particularly plausible. Most large companies have a stack sitting on one or more of the big hyperscalers, all of whom offer their own inbuilt AI platforms (which should make the decision to purchase one of them an easy one). And while it might appear like IT spending should have decreased with all the economic headwinds everyone’s facing, a Splunk report published in November last year forecasted total 2023 cloud expenditure to be $100M higher than it was in 2022. It’s also unlikely that any IT leader worth their salt would truly believe that AI isn’t “worth it” in this day and age. So what gives?

I think the most likely explanation is that:

1. AI is new and poorly understood
2. It’s hard to quantify the value from it (even if you’re convinced the value is there)
3. Proving the value requires skills and processes that are difficult to find and build

Perhaps IT and business leaders want to spend more on AI, but they don’t because they’re not sure how they would maximize their investment. Three weeks ago this newsletter covered five safe bets that would help them address that concern in the short term. But for long term comfort, decision-makers should think about three additional considerations that will help them build the kind of rapid experimentation engine they’ll need to set their teams up for AI success in the future:

• Train your developers - many AI capabilities today can be built unlocked with a combination of three skill sets: APIs, prompt engineering, and data science. For Gen AI specifically, those first two are all you need. Most large IT shops have people who know APIs and data - fortunately, prompt engineering is the easiest of the three to learn (albeit also the most unpredictable). All you need to do is find the members of your development team who want to upskill in this red-hot field (if they haven’t already) and give them the opportunity to put what they’ve learned to good use.
• Be open-minded - the AI revolution is going to require all of us to think very differently about how we do everything work-related. The usual model of single-threading through one software ecosystem (i.e Microsoft, Google, Salesforce etc) is becoming increasingly limited in terms of the capabilities we are leaving on the table. There is a plethora of low-code startups and bespoke AI tools that are addressing highly specific niche use cases in the legal, health care, payments, banking, sales, and other sectors. Leaders would be wise to consider other options when deciding where to direct their AI spending if they want to get the most out of it.
• Get the business involved - if your goal is to prove value with AI, there’s no better place to start than with the business: where the money is made. They’re the ones who may be less likely to understand how AI can help, and who might benefit the most. If you are able to involve leaders from each vertical or line of business in helping you identify the use cases they care about and (most importantly) in providing feedback on the prototypes your engineers build, your results will improve exponentially in terms of adoption and value creation.

So what does this mean for the average tech exec as they consider the current state of their AI strategy? If they haven’t started yet, they might feel as though they’re on the outside looking in. Yet the data show that at-scale implementations are exceedingly rare, and if they haven’t started yet, they’re actually in good company. The better you can identify and iterate on use cases, the more impactful your adoption will be - a lesson that many IT leaders are already taking to heart.