You're watching your AI investments scatter like shrapnel across a hundred use cases. Your teams are chasing the biggest models, convinced more parameters equal more business value.
They're wrong.
After deploying AI systems across Fortune 500s and scaling platforms that handle billions in throughput, the pattern is clear. More doesn't mean better. For most organizations, throwing everything at AI dilutes value instead of creating it.
Think of it as triage on a battlefield. You need to identify which AI initiatives have a real chance of survival and success. The rest? Cut and run before they drain resources from winners.
The Momentum Method That Actually Works
Viability in the AI age comes down to quick results. Pick one or two strong, low-hanging fruit use cases. Prove the technology and concept. Use that momentum to tackle more complex, theoretical applications.
Your competitors are doing the opposite. They're spreading efforts across dozens of initiatives, never giving any single use case a viable shot at success.
That's your competitive advantage.
The data supports this focused approach. Multimodal AI represents a $1.6 billion market growing at 32.7% annually, yet only 1% of enterprises currently use it effectively. The 40% adoption projected by 2027 will belong to organizations that master the art of strategic focus.
Multimodal AI Changes Everything
Processing multiple data types simultaneously will accelerate your quick wins. But only if you're willing to make hard decisions about what stays and what goes.
Most organizations struggle with the "cut and run" decision. They want to keep every initiative alive, hoping something eventually works. Strategic leaders understand this balance. They measure success incrementally during development, not just at the end.
You need checkpoints every 30-60 days. If the value isn't materializing fast enough to justify continued investment, stop. Redirect resources to initiatives showing real progress.
The Regulatory Reality Check
While you're focused on proving quick wins, regulatory frameworks are reshaping the entire game. The EU AI Act reveals that 18% of AI applications are high-risk, with 40% having unclear classification. Violations carry fines up to €35 million or 7% of global annual turnover.
Smart move: Build compliance in from the start. Include your legal and compliance teams in the big ideation stages. Identify the major risks upfront so executives can make informed decisions. Then move them to the side and execute rapidly.
The tension is real. Legal teams slow things down, but AI requires speed. The solution? Small checkpoints along the way, especially when your product deviates from the original plan.
Small Models Beat Big Models
Here's what the industry won't tell you: smaller, specialized models often deliver faster value than foundation models. Companies can now train GPT-3-equivalent models for $20,000, enabling 10-20 targeted use cases instead of one massive application.
But avoid the trap of deploying many small models doing their own thing. The art of design becomes critical here. You need to balance specialized models for specific functions with larger models that handle multiple enterprise-wide tasks.
Ask yourself: What are you ultimately trying to accomplish as an organization? Some areas need specialized models. Others require enterprise-wide solutions that solve your biggest value drivers.
The Strategic Architecture Decision
Your biggest mistake? Thinking too myopically. You're focused on what's directly in front of you instead of what's coming down the road.
Most executives want to think strategically about AI's future. They lack access to advisors who understand both where they are today and where they need to be tomorrow. That's where the real business risk lives.
The cost of doing nothing means losing market share to competitors who are moving faster and more strategically. Meanwhile, AI sits on top of your huge lake of data underneath. Most issues arise from inadequate controls on that data foundation, not the AI itself.
Get The Right Team Around You
Everything comes back to having strategic advisors who can bridge your current reality with your future needs. You need a team that understands both the present constraints and future possibilities.
That's true digital leadership.
The organizations that will dominate AI in 2025 aren't chasing the biggest models or the most use cases. They're building focused strategies with the right advisors, proving quick wins, and using that momentum to tackle bigger challenges.
While others scatter their efforts and dilute their value, you can concentrate your resources where they'll generate measurable results. The choice is yours.
#SignalNext #AIStrategy #DigitalTransformation #BusinessValue #TechLeadership
What's your organization's biggest AI challenge right now - proving quick wins or scaling successful initiatives?
