The success pattern: Think big, start small
How should you approach your company’s AI transformation? With the same mindset as startups and the venture capitalists who fund them: think big, start small, build a great team, get traction, and then scale.
An organization’s AI transformation has the same potential weakness as any other major corporate innovation activity: a lack of commitment. Major changes pose big risks. They require strong leadership, culture changes, new team members, and a vision. Few large organizations make the necessary commitment until it’s too late.
I suggest a three-phase approach for thinking big but starting small with your AI transformation.
Phase 1: A small, strong foundation
The most effective companies begin by standing up a parallel AI organization of dedicated, qualified teams. Give your talent the space and mandate to reinvent the core business with AI. Align them with the business leaders and customers who share this vision.
Do a few things well:
- Build an MLOps foundation.
- Hire a few qualified engineers and data scientists.
- Deploy solutions that demonstrate clear business impact.
- Engage the rest of the company by using consistent, top-down messaging through stories.
This is the time when a company like Prolego can help you avoid common mistakes and get your program off to a good start.
Phase 2: Traction
As the AI organization makes progress, slowly add new team members and initiatives. Begin promoting your successes to get more resources. Bring on additional business partners and track your progress by using metrics.
Phase 3: Scale and transition
As you build the organization you need, slowly ramp down legacy initiatives such as traditional rules-based business applications and paper-driven business processes. The AI organization expands and begins to take on more and more resources.
Of course the legacy teams and infrastructure might continue operating for years, just as your company still uses fax machines and paper forms in some cases. But the AI organization will be the growth engine of the future.
You have time, but you need to act now
Innovation in big organizations is incredibly hard, even if you’re following the success pattern. Incumbent teams will resist change and often undermine the new AI organization. Building infrastructure and teams and getting traction takes years. Waiting gets you nothing but a lost opportunity.
As I explain in AI Abundance, exponential technologies like AI start slow. But then they reach an inflection point that precipitates rapid change and abundance. The only winners are those organizations that begin investing early, when the technology first demonstrates value.
That time is now.
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