If AI strategy is not unfolding according to plan, it’s usually down to missing one of these key ingredients.
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As enterprises become smitten with the potential of artificial intelligence (AI), the reality on the ground can often be sobering. A recent study indicated a striking gap in confidence among executives regarding their organization’s prowess in AI implementation. Furthermore, a comprehensive poll of 1,400 executives revealed a widespread sense of dissatisfaction with the pace of progress, particularly in the realm of Generative AI—the ability of AI to generate new information based on input.
These insights spotlight the lack of expertise and a sufficient approach to AI deployment. The following five key principles address these deficiencies and provide a framework for successful AI deployment. As an expert who has done the work, I can offer these key principles to serve as a roadmap that will bridge the gap between aspiration and reality.
1. Start with the “why.” This first principle emphasizes the core purpose behind adopting AI. Is it to improve efficiency? Drive innovation? Unlock new value streams? Stay ahead of competition? Without a clear purpose, AI projects are at risk of becoming solutions searching for problems, which can lead to misaligned expectations and wasted resources. Starting with a compelling “why” ensures that AI initiatives are grounded in the organization’s strategic objectives, providing a North Star for the project and a benchmark for measuring success.
2. Develop a strategy that matches the organizational maturity. A sophisticated AI solution might dazzle in a demo but could overwhelm an enterprise lacking the necessary infrastructure or data management capabilities. A successful strategy will take stock of the company’s current state, including data readiness, technological sophistication, and employee skill sets. It must set realistic milestones that reflect organizational capabilities and outline a clear path for growth and development in AI competency.
3. Take the approach of “think big, start small, scale quickly.” The vision for AI within an enterprise should be ambitious, aiming to transform or significantly enhance key areas of operation. Yet, the path to that vision should begin with small, manageable projects that deliver quick wins and valuable lessons. This iterative approach allows for adjustments and refinements, reducing risk and building organizational confidence in AI. As these smaller projects prove their value, they set the stage for rapid scaling, expanding AI’s benefits across the enterprise.
4. Choose your technology partner wisely. The market is full of AI vendors and partners claiming expertise. However, you must first vet this out. What have they done that’s gone into production and remains there? And are they a collaborator who understands your industry and is committed to your success? They should have a proven track record, robust support structures, and a clear understanding of your strategic goals. A wise technology partner is also one who aligns with your “why” and your strategy and is invested in scaling with you as you grow.
5. Pace yourself. It’s essential that you pace yourself while embarking on an AI deployment. Expect the journey to be resource intensive. It requires time, investment, and patience. The technology needs to be integrated thoughtfully into existing systems — bending the model where necessary without breaking it. Rushing can lead to burnout, resistance from employees, and deployment that is fragile and unsustainable. By pacing the deployment, leaders can ensure that the organization absorbs the new technology effectively, integrates it with human processes, and scales it to an advantageous and transformative solution.
The lack of faith in organizational expertise and dissatisfaction with AI progress can often be traced back to a deviation from these fundamental principles. By committing to a well-paced, strategic, and purpose-driven approach to AI deployment, and by selecting partners who truly complement your vision and capabilities, organizations can navigate the common pitfalls that impede AI initiatives.
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