Three Stops on the Path to AI Transformation

As companies grow increasingly interested in leveraging artificial intelligence, it’s become clear that a structured approach to AI transformation is crucial for success.

Whether your organization is in the early stages of exploring AI or advancing from piloting use cases to scaling them, understanding what you’re striving for is the key to finding impactful, long-term outcomes.

In a recent piece by Wharton professor and AI expert Ethan Mollick, he highlighted the importance of empowering employees to experiment with AI openly and without fear.

His research shows that such encouragement, paired with a clear strategic direction, can significantly accelerate an organization’s AI transformation journey—and lead to gains they didn’t know were possible.

I agree with Mollick that empowering people to experiment—safely and within the boundaries of your AI governance structure—is the right approach.

Because one of the fundamental truths of AI transformation is that no one can do the work for you, or find the use cases that will really move the needle for your organization.

It’s up to you to do the work. By which I mean: It’s through the doing that you’ll unearth the real potential.

With that in mind, here are three stops to expect on the path to AI transformation.

1. Table Stakes—Building a Solid AI Foundation

The initial phase of AI transformation revolves around laying the groundwork—what we call the “Table Stakes.” These foundational steps help companies establish a baseline for effectively engaging with AI.

The key activities in this phase include:

• Recruiting an Executive Sponsor. Securing leadership buy-in is critical to ensuring that AI initiatives align with overall business strategy and receive adequate support.
• Establishing an AI Council. Creating a cross-functional AI Council helps govern AI-related projects and align them with organizational goals.
• Creating an AI Charter. An AI Charter sets the vision, guiding principles, and priorities for AI adoption, making sure everyone understands the organization’s AI agenda.
• Drafting an AI Policy. This ensures that AI implementation remains ethical, transparent, and aligned with regulations.
• Assembling Use Cases: Identifying high-value opportunities for AI is the stepping stone to success. Knowing how to benchmark use-case performance is equally important.
• Launching Pilot Projects: Testing concepts in controlled, safe environments helps determine viability before larger investments are made.

2. Leading Policies—From Pilots to Sustainable Practices

Once the foundational phase is established, companies need to move towards implementing practices that encourage the innovative and advanced use of AI.

This phase focuses on embedding AI within the company culture and scaling pilot projects.

Key actions in this phase of AI transformation include:

• ROO to ROI Mapping: Combining Return on Operations (ROO) with measurable Return on Investment (ROI) allows organizations to capture the best of both worlds. The real value lies in seeing both operational efficiency and financial returns, rather than focusing solely on one. Most companies get stuck seeking ROI alone, but the sweet spot is when both operational and financial benefits are realized, demonstrating the comprehensive impact of AI initiatives. This dual focus enables companies to validate value comprehensively, balancing efficiency with financial returns for a more holistic understanding of AI’s contribution.
• Scaling AI Projects: Moving from piloting AI projects to organization-wide implementation allows AI to become a meaningful driver of value. This transition requires a strategic approach to managing risks, optimizing resource allocation, and ensuring that scalability leads to sustainable benefits.
• Developing AI Literacy & Training Programs: AI training for all employees helps foster an environment that is ready for AI innovation. This ensures that everyone, regardless of their role, understands how AI impacts their work and how they can contribute to its effective use. Ethan Mollick refers to the importance of empowering “Secret Cyborgs”—employees who experiment with AI on their own. By promoting open experimentation and reducing fear, companies can create a culture where AI thrives at every level.
• Facilitating Cross-Departmental Collaboration: Encouraging departments to work together is crucial, as AI’s benefits often depend on combined datasets and inter-departmental insights. Breaking down silos helps create a culture of shared knowledge and innovation, maximizing AI’s potential across the organization. Mollick also highlights the idea of leveraging both ‘Crowd’ and ‘Lab’ efforts—where individual experimentation (‘Crowd’) and centralized innovation teams (‘Lab’) work in tandem to accelerate progress and enhance AI adoption throughout the organization.

3. Next Horizon Practices—Pioneering the Future of AI

Finally, as organizations mature in their AI journey, they need to adopt next-horizon practices that position them as leaders in AI implementation.

This phase of AI transformation is about forward-thinking policies and initiatives that set the stage for the future:

• Implementing Learning Journeys Companywide: Continuous, role-specific learning journeys ensure that the workforce remains equipped to handle AI-driven changes.
• Specific AI Talent Recruitment: Attracting AI-specific talent, such as data scientists or machine learning engineers, ensures the company has the skills needed for advanced AI projects.
• Company-Sponsored AI Innovation Incentives: Incentivizing innovation through rewards encourages employees to come up with AI-driven solutions for business challenges. Mollick suggests aligning incentives with productivity gains and recognizing internal experimentation, helping companies harness the collective creativity of their workforce to drive AI-led innovations.
• Annual AI Transparency Reports: Providing transparency reports on AI use helps maintain trust and demonstrates the responsible use of AI.

Moving Forward

One thing is clear: the path to AI transformation is anything but linear.

However, understanding where your organization stands today can help you develop a realistic roadmap for the future.

By following this structured approach—from building a solid foundation, developing leading policies, to embracing future-focused practices—your company can unlock the transformative power of AI and stay ahead in the competitive landscape.

Where is your organization on this journey, and what steps can you take next to mature in your AI adoption?

Reach out. I’d love to talk about it.