Turn Your AI Hub Into a Customer Impact Engine — Not Another Innovation Graveyard

The corporate world is witnessing a familiar pattern. Just as companies rushed to establish innovation labs a decade ago, today's organizations are rapidly launching AI hubs with similar enthusiasm and expectations. However, the sobering reality of innovation lab closures should serve as a critical warning for AI hub leaders. By understanding why innovation labs failed and applying customer-focused methodologies like Jobs-to-be-done, AI hubs can avoid the same fate and deliver genuine business value.

The AI Hub Gold Rush: History Repeating Itself?

Today's AI hub phenomenon mirrors the innovation lab boom of the 2010s with striking similarity. Companies are establishing dedicated AI centers, hiring specialized teams, and investing heavily in cutting-edge technology, driven by the same "AI or die" mentality that once fueled innovation labs “Innovate or die”.

The parallels are unmistakable:

• Technology-first approach: Organizations focus on AI capabilities and technology rather than customer problems

• Separate organizational units: AI hubs operate independently from core business functions

• Innovation for innovation's sake: Projects are initiated because they showcase advanced technology, not because they solve real customer needs

• Backlog without priorities: Ideas are piling up in some backlog spreadsheet without priorities or next steps

• Abundant funding: AI-related projects are easy to fund while traditional initiatives face scrutiny 

Rightly so artificial intelligence has become a major search field for innovations. The potential is huge and we are already seeing first indications of success. However, companies face the same challenge that plagued early innovation labs: thousands of possibilities for AI implementation with unclear prioritization criteria and a danger of disconnect from the core.

The Innovation Lab Graveyard: Lessons from Failure

The innovation lab experiment has largely failed, with many labs being closed down due to fundamental disconnects from core business value. The failures weren't due to lack of creativity or technical capability, but rather systemic issues that AI hubs are now replicating:

Disconnect from Core Business

Innovation labs operated as isolated entities with little connection to the core business . This separation, initially seen as necessary for creative freedom, ultimately led to:

• Projects that couldn't be integrated into existing operations

• Solutions that didn't align with business strategy

• Innovations that remained perpetually in "pilot" phase and simply got forgotten

Frank Mattes speaks of the disconnect between the Now and the New or the gap between the red (ocean) shirts and the blue (ocean) shirts, i.e. the innovation people and the core in his excellent new book “Now and New”. If you’re interested in how to bridge this gap we strongly recommend his thought leadership on the topic.

Low Success Rates and Idea Overload

The innovation lab approach resulted in overwhelming idea collections with low success rates. Labs generated hundreds of concepts but struggled to identify which ones would create real value. Innovation became a problem of idea selection rather than idea production. No company has a lack of ideas.

Lack of Customer-Centric Focus

Most critically, innovation labs focused on technology solutions without clearly understanding customer problems alongside. They were putting the cart before the horse by developing impressive technologies and concepts that failed to address genuine customer needs.

The Ocean of Possibilities Problem

AI hubs face the same "ocean of possibilities" challenge that sank innovation labs . With AI's vast potential applications across internal processes, customer interactions, and product development, organizations struggle to determine where to begin and focus. What are the real opportunities that we should be focusing on? Technology will not give you the answer.

The Customer-Focused Solution: Jobs-to-be-Done and CFI

The key to AI hub success lies in adopting a customer-focused approach that innovation labs largely ignored. Jobs-to-be-done and Customer-Focused Innovation (CFI) provide the framework to ensure AI initiatives create genuine customer value rather than impressive but irrelevant technology demonstrations.

Understanding the Customer Job First

The fundamental principle is simple yet powerful: customers don't want technology for its own sake . Instead, they hire products and services to accomplish specific jobs. As Theodore Levitt famously stated, "People don't want a drill. They want a hole in the wall". This perspective shift is crucial for AI hubs. Rather than asking "Where can we apply AI?" the question becomes "What customer jobs are currently not well served and how can AI help them do it better?"

The CFI Process: From Customer Needs to AI Solutions

The Customer-Focused Innovation process provides a systematic approach to identify and validate customer needs to focus and sharpen AI solutions to have a lasting customer impact. The four-step framework ensures AI initiatives are grounded in real customer problems:

1. Frame: Define the right scope

2. Discover: Conduct solution-free customer research to identify unmet needs and pain points

3. Spin: Align AI initiatives with measured customer pain points and create compelling value propositions

4. Action: Develop and implement AI solutions that address validated customer needs

Measuring Customer Pain Points

CFI's strength lies in its ability to quantitatively measure customer pain points . This measurement creates a Customer Value Map that shows which needs are important but unfulfilled - the prime targets for AI innovation .

Conclusion: A Customer-Centric Path Forward

The innovation lab failures offer a clear warning: technology-driven initiatives without customer focus are destined to fail. AI hubs have a unique opportunity to learn from these mistakes and build sustainable, value-creating organizations. To avoid innovation lab failures, AI hubs must:

1. Anchor in Customer Needs: Jobs-to-be-done provides stability by focusing on customer needs rather than rapidly changing technologies .

2. Measure needs while building: Use quantitative customer research to identify which AI applications will create the most value before investing too much in development.

3. Connect to Core Business: Ensure AI initiatives directly support business strategy and can be integrated into existing operations.

4. Focus on Outcomes: Prioritize AI projects that address measured customer pain points rather than showcasing technical capabilities .

   
   

By adopting Jobs-to-be-done thinking and the CFI methodology, AI hubs can:

• Focus resources on customer problems rather than technical possibilities

• Reduce innovation risk through validated customer insights

• Increase success rates by addressing proven market needs

• Create lasting business value that justifies continued investment

The choice is clear: AI hubs can either repeat the innovation lab mistakes and face eventual closure or dissolution into existing teams or embrace customer-focused methodologies and become genuine drivers of business growth and have impact. The customer perspective must be incorporated into the decision-making process from day one.

The future belongs to AI hubs that understand a fundamental truth: artificial intelligence is not an end in itself, but a powerful tool in service of customers. Those that embrace this customer-centric approach will not only survive but thrive in the AI-driven future.