Executive Summary
I’m bullish on GenAI – and I’ve got the scars to prove it. The promise is clear: natural-language intelligence applied to your data and processes can drive growth, deeper customer relationships, better margins, and real productivity. But it’s not plug-and-play – it demands data readiness, end-to-end orchestration, retraining builders, and responsible AI with real governance.
The pragmatic play – start internally, deliver frequent “reasons to believe,” measure with evaluation-driven development, and close gaps (data, literacy, change management, security). Hype or disruptor? Both—for now. The agentic future will be earned through disciplined execution and plenty of grunt work. Winners balance ambition with operational readiness – in days and weeks, not years.
There’s a Lot Hype Flying Around about GenAI
That’s why when I hear leaders in organizations that have poor Data Maturity, complex, error-prone business processes, and low AI Literacy talk about how Agentic AI is going to transform their business in the next year, I tend to take the “under” on that bet… It’s also why I tend towards pragmatic voices in the AI community, like John Michelsen at Krista.ai and Kevin Dewalt at Prolego. Both are vocal advocates for GenAI’s promise, while being thoroughly grounded in the foundational capabilities required to successfully scale it.
GenAI’s Promise is Undeniable
It’s critical to cut through the hype and hyperbole, and continually refocus on GenAI core Value Proposition, which is to bring bespoke, natural language interactivity and intelligence to your business data and processes. At its simplest, GenAI helps companies:
- Accelerate their growth
- Build deeper, more profitable relationships with their customers
- Deliver higher operating margins
- Enable significantly increased workforce productivity
It’s rare for a single technology to offer “top-line growth”, margin improvement, and bottom-line profitability benefits – which is why everyone’s so excited about GenAI.
Though the Promise is Clear, the Path to Getting There Isn’t Simple…
In many ways, OpenAI did those of us in the industry a major disservice by making GenAI look awfully simple… Open a browser, ask a question on nearly any topic you can think of, get a customized answer that you can explore iteratively in multi-turn interactions. Pretty cool – until you tried it on data that didn’t go through a full neural network training cycle… It turns out for organizations to leverage GenAI in a business context, they need to:
- Address deeply rooted Data Quality and Data Readiness issues we’ve all known about but rarely had a burning platform to fix
- Enable programmatic orchestration across large parts of our business, in real-time, without human intervention
- Retrain a large % of their application developers and data scientists to think, act, and solution differently
Those are just a few examples… The reality is that GenAI is like all previous waves of disruptive technologies that came before it. They all required significant alignment, focus, and commitment to adopt and embed into the way we work.
To “Win” You’ll Need to Deliver Clear Value, While Closing Capability Gaps…
We need to manage expectations when it comes to adopting and scaling GenAI. Until you address GenAI Data Readiness, you’re going to be stuck playing small-ball or being surrounded by a bunch of PoC Zombies. Until you solve for detailed diagnostics – i.e. Evaluation Driven Development – you’ll play an endless game of “Whack-a-Mole” across your GenAI solutions. Until we approach GenAI adoption and scaling as a highly complex, interdependent business model evolution, we’ll continue reading discouragingly low stats on GenAI impact.
To “Win” with GenAI, organizations need to prioritize delivering frequent “reasons to believe” to their team members and eventually their customers. Start internally, where the price of getting it wrong is more manageable, then methodically introduce more substantive solutions over time. In parallel, prioritize identifying and rapidly closing organizational GenAI capability gaps. Topics like Secure & Responsible AI, Technical Change Management, and Integrated Governance aren’t generally what you lead with in a Town Hall, but they will safeguard your organization from unwanted, large font articles in the Wall Street Journal…
So – Hype or Disrupter?
GenAI is clearly a bit of both today… For those selling dreams of fully autonomous Agents solving complex business problems – while you grab coffee – a bit of pragmatic grounding is likely in order. Agentic AI will transform the way businesses work, are structured, and how they scale – but there’s a lot of “grunt work” to be completed in the meantime… For the nay sayers that observe the messiness of early stage GenAI solutioning I’d recommend a review of Innovation history. The early stages of technical solutions were typically pretty “janky”, but provided a clear glimpse of what’s to come. In GenAI’s case, the future is compelling, Agentic, and disruptive, and you want to be on the successful side of the adoption fence.
