American humorist Mark Twain once said, “History never repeats itself, but it does often rhyme.” Today we worry this phenomenon is playing out with enterprise adoption of generative AI.
On their digital transformation journeys over the last decade, many enterprises spent time mired in “pilot purgatory,” taking as long as years to move from concept to executing on use cases.
Generative AI (genAI) arrived on the scene with use cases such as “support chatbots” or “talk to your documentation apps” that were so obviously useful that many companies are well on their way to taking them into production.
But in many organizations, we’re seeing a gap emerge between those activities and a lack of comparably swift and intentional work to nail down the full potential value of genAI—and then take action, at scale, to make it real.
Stuck in ‘use case limbo’ with genAI
We call this state of ambiguity “use case limbo” because it leaves big important questions that hang in the air unanswered.
Because genAI can (among other things) be used to summarize, bootstrap, analyze, and automate, in concept, it could save time and toil in just about any job.
Because GenAI can manifest human-like cognitive behavior in roles ranging from a direct report to a co-pilot to a coach (or a confidante, and more), in concept, it could play a part in every aspect of your customer value proposition that involves people.
Because the value at stake with genAI is believed to be in the trillions of dollars, in concept, disruptors (or first movers) could seize a chunk of it by doing something no one has thought of yet.
Now imagine employees asking each other, “What does genAI mean to our jobs?” Or a functional and line-of-business executive asking, “What does genAI mean to how we create value?” Or the CEO and the board discussing, “What does genAI mean to our future opportunities and threats?”
With no disrespect to the late great Douglas Adams, a plan for four, 14, or even 42 GenAI use cases begs these questions. And with every passing day, “your guess is as good as mine” will become an increasingly uncomfortable answer.
Break out with leverage, knowledge, and lighthouse strategies
This is in part because we do see some companies signaling big moves beyond “use case limbo.” The biotech company Moderna, for example, is aiming to use ChatGPT Enterprise to “automate nearly every business process” in order to “outpace its plan to roll out 15 new products within the next five years.” Verizon’s CEO is organizing genAI strategy using three buckets: “optimizing processes, product experiences, and revenue growth.”
We think these companies are heading in the right direction by turning “in concept” potential into real outcomes, and that the past provides some useful lessons for doing the same.
A 2023 World Economic Forum diagnosis of digital transformation efforts noted that “without clear direction, the breadth of possibilities and the variety of use cases and technologies threaten to mire organizations in pilot purgatory”—so it was essential to build a clear strategy.
This applies in spades to genAI and “use case limbo.” The good news is that we see a way to break the daunting breadth of possibilities of genAI down into three actionable areas of responsibility that we call “leverage,” “knowledge,” and “lighthouse.”
Your leverage strategy sponsor takes accountability for answering: “How can we use genAI to save time and toil?”
Your knowledge strategy sponsor takes accountability for answering: n”How can we put genAI to good use improving the business we’re already in?”
And your lighthouse strategy sponsor takes accountability for aanswering: “How can we proactively explore what genAI means for evolving or expanding our business model?”
The table below is a “genAI strategy sponsor” cheat sheet for bootstrapping vision, crafting metrics, and driving progress.
DataStax
“Firing on all cylinders” starts conversations that don’t begin with lists of generic use cases but rather your unique employee, customer, and market context. It offers a way to get people enrolled and engaged in creatively driving the dimension of value that suits their skills and passion (or, for that matter, addresses their pain points).
No one today looks back fondly on the time their organization spent in “pilot purgatory.” Nip the seeds of future regret in the bud and put use case limbo behind you
Artificial Intelligence, Machine Learning
Read More from This Article: 3 ways to break out of AI ‘pilot purgatory’
Source: News
