The Friction in Every AI Transformation
How I Turn Resistance into Momentum for Rapid Growth
As an AI solution expert, I can share my insights from applying AI tech to remove the “friction” that is the equivalent of a handbrake on making rapid progress.
AI is not just a distant future promise; it’s a reality transforming everything from customer service to coding and marketing. Our AI tools, ChatGPT, Claude, and GitHub Copilot, are a testament to this. They are empowering employees across industries, boosting productivity, and leading to a significant increase in output. It’s just the beginning of AI’s rapid growth and transformation potential.
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“Yet here’s the paradox: while individuals thrive with AI, organisations struggle to translate these successes into meaningful, scalable change. That friction — the gap between what’s possible and what’s happening — is what I want to explore today.” — Greg Twemlow, Nov 2024
A recent Boston Consulting Group survey of over 1,400 C-suite executives revealed something fascinating. Nearly 90% of them ranked AI as a top-three priority for 2024. The potential is clear — McKinsey and PwC predict AI could contribute up to $15 trillion to the global economy by 2030. But here’s the catch: only 6% of these executives have taken concrete action to upskill their employees. That gap between awareness and action is where the natural friction lies.
This struggle isn’t new. I’ve seen it before with the adoption of open talent models — using digital platforms to access a global, freelance workforce. It’s a powerful way to work, but managers hesitated to embrace it until they saw clear, scalable benefits. The same dynamic is at play with AI. The friction is undeniable, but friction isn’t inherently a problem. It can be the force that transforms organisations — if approached with the proper framework.
Let me share the lessons I’ve learned from navigating similar challenges. These principles will help companies develop their in-house processes for the powerful integration of AI.
Establish Communities of Practice
Organisations truly thrive when collaboration becomes the default mode of operation. The best ideas often emerge from the edges, where experimentation is alive and well. The challenge is to foster a culture where these innovative ideas don’t stay at the edges but are shared and celebrated across the entire organisation.
All workplaces have ‘secret cyborgs’ — employees quietly using AI tools like ChatGPT to get their jobs done. These employees, often unnoticed, are innovating but in isolation. Without a system to share those insights, the organisation misses out. Managers bypassed outdated hiring processes to get work done on platforms like Andela and Torc, but their successes were personal, not organisational.
The solution? Build internal communities of practice. These virtual spaces allow employees to share tools, tactics, and lessons learned. I’ve seen this work in AI hackathons and prompt-sharing sessions, where employees solve real problems collaboratively. These events do more than break down silos; they create a sense of shared purpose and turn friction into fuel for innovation.
Centres of Excellence
If communities of practice decentralise innovation, a Centre of Excellence in AI acts as the counterbalance. It provides a centralized hub for governance, experimentation, and alignment with strategic goals in the context of AI. In my work, I’ve seen how a Centre of Excellence can reduce the friction of ‘trial-and-error chaos’ by giving employees a clear framework for experimenting safely with AI.
But the key is customisation. Off-the-shelf solutions rarely work. Instead, organisations need tailored benchmarks that align with their unique needs. When companies I’ve worked with embraced this approach for open talent, they moved from fragmented efforts to scalable solutions. The same principle applies to AI: benchmark its value through small experiments and expand what works.
Critically, a Centre of Excellence cannot be elitist. To succeed, it must represent every functional area of the business, ensuring that innovation reflects the diverse needs and expertise of the entire organisation. Moreover, the most forward-thinking CsoE go further by incorporating perspectives from outside the organisation. Loyal customers, key suppliers, investors, and government agencies can provide invaluable insights that help align AI strategies with market needs, regulatory expectations, and broader societal goals.
Engaging external stakeholders transforms the CoE into a collaborative ecosystem. For example:
- Loyal customers can test AI-driven solutions and provide real-world feedback on usability and impact.
- Suppliers can co-develop more efficient workflows or integrate AI into shared processes.
- Investors bring a strategic lens, ensuring alignment with long-term goals and market dynamics.
- Government agencies can guide compliance, ethics, and best practices, ensuring the organization stays ahead of regulatory changes.
Sometimes, this innovation needs to happen outside the traditional organizational structure. I’ve seen how bureaucracy can crush nascent experiments. A Centre of Excellence can act as a shield, protecting new ideas while they gain traction. But when it also includes external collaborators, it becomes a dynamic hub that accelerates internal innovation and aligns it with external realities, amplifying its impact.
Embrace the Process
If there’s one insight I’ve gained from working on transformational projects, it’s this: change is a process, not an event. It’s a journey that requires deliberate steps: assess, learn, experiment, build, and scale. Understanding and embracing this process is vital to successful AI integration and transformation.
- Assess: Start by understanding how AI is already used in your organisation. I’ve found that many “secret cyborgs” are eager to share their insights if given the right platform. Conducting a transparent assessment brings these efforts into the light.
- Learn: Share what you’ve discovered. Knowledge-sharing platforms and internal communities are critical here. Encourage employees to collaborate across teams and regions. The cultural shift this creates is just as important as the practical knowledge.
- Experiment: Don’t wait for perfection. AI is evolving too fast for that. Start small, experiment in different parts of the business, and embrace the unexpected insights from these trials. Some of the most creative uses of AI I’ve seen were born from small, scrappy experiments.
- Build: Once you have a proof of concept, formalise it. Integrating it into workflows, partnering with innovative vendors, and creating the infrastructure needed to scale is how friction turns into momentum.
- Scale: At this stage, it’s embedded into the company’s DNA, and you can amplify what works across the organisation.
Most organisations are stuck in the “assess” phase right now. That’s okay, but it’s also a warning. The pace of change is ongoing, and the window to act is narrowing.
Friction as a Catalyst for Transformation
Adopting a new technology at scale will always involve friction. It’s the nature of the beast. But I believe that friction isn’t a barrier — it’s the spark that ignites transformation. It forces us to rethink how we innovate, collaborate, and grow.
The companies that lean into this process and embrace the tension between experimentation and structure will be the ones that thrive. The rest? They’ll get left behind.
So here’s my challenge to you:
- Don’t fear the friction.
- Use it for leverage.
- Create an environment where employees feel safe to experiment, share their insights, and collaborate openly.
- Empower them to turn friction into momentum and momentum into transformation.
AI is more than just another tool; it’s the foundation for what comes next.
About the author: Greg Twemlow, Founder of XperientialAI©.
Greg Twemlow: Sharing what I’ve learned from my career of 35 years as a citizen of the world, parent, corporate executive, entrepreneur, and CEO of XperientialAI, focused on experiential learning for maximum impact with AI. Contact Greg: greg@xperiential.ai
