Every day there is a new story about AI, and—unlike most tech coverage—it gets picked up by all the traditional media. AI feels omnipresent. Reality, however, is different: adoption at the enterprise level is far lower than headlines suggest, and the AI toolbox is far wider than just limelight‑hogging generative models.
That gap fuels a classic case of FOMO. Board members and C‑suite executives push their teams to "do AI" without a clear idea of what it means, how it works, or what it costs. Their staff, understandably, are reluctant to leap into unfamiliar territory—especially one rumoured to make humans redundant.
The result? A proliferation of pilot projects that:
aren’t aligned with business objectives,
are poorly prioritised, and
lack proper governance.
Unsurprisingly many pilots deliver little value—or simply fail. Yet relentless vendor hype still convinces leaders they need AI. They’re right—applied correctly, every organisation can benefit—but success demands moving from hype‑driven FOMO to focused strategy.
Aligning AI and Business Strategies
AI adoption is uneven. Some firms have used AI for decades (think predictive maintenance in manufacturing), while others tap it unknowingly via email auto‑suggest or smartphone autocorrect.
AI is a tool. In the right context it is transformative, but it is still only a tool.
No board asks for a “wrench strategy”, yet many now demand an AI strategy. A plan to infuse AI across the enterprise must begin with—and iterate alongside—the overall business strategy. It is a two‑way street:
AI can enable entirely new business models, not just accelerate existing ones.
Conversely, business objectives determine where AI creates the most value.
E‑commerce is a precedent: companies that merely bolted a website onto an old model lost out to those that re‑imagined their business around the internet. The same opportunity—and risk—exists with AI.
So “alignment” means deeply exploring how AI and business strategy evolve together, and mapping the organisational change required for an AI‑enabled future.
Building Focused AI Leadership
Pilot projects are a good first step—like high‑school science experiments, they build core skills. But they’re insufficient for enterprise transformation. To scale, organisations must examine the very DNA of the business:
Key questions include:
Objectives – Is enhanced product innovation or cost reduction more critical?
Automation – Which processes, previously too costly to execute manually, can now be automated (e.g., continuous analysis of every customer interaction)?
Portfolio impact – Which existing applications gain most from an AI infusion? Traditional portfolio‑management tools overlook AI specifics; new prioritisation frameworks are required.
Testing & Deployment – How do we validate models—especially generative ones that intentionally produce varied outputs? Monitoring must detect performance drift and quality issues.
Risk & Governance – As applied AI becomes ubiquitous, so must practices for legal compliance, bias mitigation, and accuracy assurance.
Applied AI Success as a Subscription
At Data Kinetic we’re building a proposition we call Applied‑AI success as a subscription:
Right‑sized technology that runs in your environment.
Experience‑based advisory and tailored workshops—no overpriced slideware.
On‑demand access to skilled technical consultants.
Our AI‑focused workshops address everyone from board directors to project leaders, producing the next generation of AI Leaders with ongoing advisory support. The outcome: clients progress from exploring “the art of the possible” to delivering measurable business results.
Ready to move from AI FOMO to AI focus? Contact us to begin your journey toward pragmatic, AI‑enabled success.