Beyond Excellence: For A.I., Let's Do Centers of "Experimentation"

Centers of Excellence (CoE) and Innovation Labs are too often launched with hype and shiny toys, but left to decay in vacant tributes to the tech du jour. Furthermore, they are often a "Silo of Excellence" with a particularly constrained focus and limited reach. However, a shift in mindset and investment will help companies yearning to learn about, embrace, and apply the power of A.I.

In the ever-evolving landscape of corporate strategy, the notion of a Center of Excellence (CoE) has long been a cornerstone.

These entities have traditionally been perceived as bastions of preconceived wisdom, grounded in specific technologies or functions, like supply chains or mixed reality. However, there exists a compelling opportunity to revitalize these institutional pillars into something far more dynamic: call it a Center of Experimentation.

Drifting away from the confines of assumed "excellence," Centers of Experimentation represent a paradigm shift where curiosity reigns supreme. Rather than tethering themselves to a singular technology or functional domain, these centers serve as crucibles for challenging mindsets and methodologies. As Mark Twain aptly observed,

"The mind, once expanded, never regains its original dimension."

Twain's comment is a favorite of ours at Megafauna , where we aim to expand minds. This ethos underscores the essence of experimentation, where the pursuit of knowledge transcends the boundaries of familiarity.

Not being precious is critical: Centers of Experimentation foster inclusivity by inviting—perhaps even expecting—participation from all corners of the organization. Every individual is encouraged to embark on a journey of exploration, probing new frontiers and discerning whether/how they are relevant to the business at large. The potential for co-creation with end-users and the broader community adds another layer of richness to this collaborative pursuit.

Flexibility is a hallmark of Centers of Experimentation.

While GenAI may serve as the inaugural focal point, the trajectory of exploration inevitably splinters into diverse avenues tailored to various facets of the business. Whether it's Tech XYZ or Method ABC, the center's mission and evolution must be deliberately designed to accommodate shifting landscapes. Rotations of new employees ensure a continuous influx of fresh perspectives, enabling the cross-pollination of insights throughout the organization.

Gone are the days of cloistered expertise within siloed domains.

Instead, Centers of Experimentation herald a new era of adaptive capability-building. By perpetuating a culture of innovation and evolution, these centers become vital conduits for organizational rejuvenation. In a "meta" twist, they also serve as living laboratories for refining change management practices and cultivating functional leadership...as they are changing themselves.

A journey this transformative can't proceed without a compass, or a leaders, of course. So a core team, untethered to any specific technology, needs to spearhead the center's voyage, establishing boundaries and crafting experiential frameworks. And while it might be tempting to keep a team in place to staff the Center over time, keeping the same team in place results in professional experimenters who likely aren't connected to the front lines of the business. So, rotating teams through the Center ensures that the learnings are embedded across the company, and that learning remains a constant.

In essence, Centers of Experimentation bridge the gap between conventional R&D functions and commercialization processes.

They epitomize the ethos of embracing failure as a conduit for valuable innovation—an ethos often lauded but seldom realized. As the corporate landscape continues to evolve, these centers can stand as beacons of agility and resilience, propelling organizations towards a future defined by perpetual exploration and innovation.

Centers of Experimentation must exhibit four principles in order to succeed:

1. A Clear Vision and Cross-Functional Support: At its core, the center starts with a meticulously crafted vision that serves as a guiding light for all its endeavors. This vision is not only articulated clearly but also garners support from multiple functions within the organization. By securing buy-in from various departments, the center ensures alignment of goals and resources, fostering a cohesive approach towards leveraging AI capabilities to drive organizational objectives. While the study was focused specifically on cost-optimization, Gartner research revealed that "70% of companies whose executive leaders consistently hit their [key] targets have cross-functional [internal] teams, compared with 46% of companies who do not." (It's often difficult to quantify the benefits of cross-pollination, but cost-optimization is a well-defined place to begin.)
2. The Cultivation of a Collaborative Culture: Central to the idea is the cultivation of a culture that values collaboration and embraces failure as a catalyst for learning and innovation. Rather than stigmatizing missteps, the center must encourage an environment where failures are dissected, analyzed, and transformed into invaluable learning experiences. This approach not only fosters resilience but also nurtures a mindset of continuous improvement and experimentation, essential for harnessing the full potential of AI technologies.
3. Continuous Reinvention and Evolution: Recognizing the rapid pace of technological advancement, particularly with A.I., a Center of Experimentation must be designed to be agile and adaptive, continually reinventing itself to stay ahead. It must remain open to incorporating new technologies, methodologies, and best practices, ensuring its relevance and efficacy in an ever-changing landscape. Moreover, a periodic infusion of new team members will bring fresh perspectives and insights, enriching the Center's collective knowledge and driving innovation forward. Through this process, it's easy to see that a Center would learn from how new team members are using A.I. already in their daily work, as well, given the wide-open exploration currently going on in that field.
4. Alignment of Success Metrics with Organizational Goals: Success for the Center shouldn't merely be measured by the deployment of AI solutions but rather intricately tied to metrics that reflect broader organizational objectives. For example, the Center's definition of success must be tied to metrics that underline learning, employee experience, new product and service design, and operational improvements. By aligning success metrics with organizational goals, the center ensures that its efforts are not only impactful but also contribute meaningfully to the overall success and growth of the organization.

Centers of Excellence aren't dead, nor should they be put out to pasture.

But Centers of Experimentation combine the creativity of innovation labs with the refinement of best practices often seen in a traditional CoE. The result is an evolved, dynamic, vibrant crucible in which the promise of A.I. (and other new tech, methods, and mindsets) can be both explored and applied. Through the four principles above, these new Centers can escape the fate of so many other dusty, abandoned centers and labs that served a temporary function but didn't have long-term relevance and staying power.

UPDATE: Hey ChatGPT, what questions might a someone have after reading this article?

1. How do companies ensure that the rotating teams maintain continuity and coherence in their experimentation efforts as they move through the Center of Experimentation?

Maintaining continuity and coherence within rotational teams as they move through the Center of Experimentation requires strategic planning and effective knowledge transfer mechanisms. Companies can establish standardized processes for documenting learnings and insights gained during each team's tenure, ensuring that incoming teams have access to valuable information and can build upon previous experiments. Additionally, clear communication channels between rotating teams and core leadership can help maintain alignment with the center's overarching goals and vision. Regular debriefings and knowledge-sharing sessions can further facilitate the transfer of expertise and ensure that the momentum of experimentation is sustained.

1. While the article highlights the importance of fostering a culture of experimentation and collaboration, how do organizations balance this with the need for accountability and measurable outcomes?

Balancing a culture of experimentation and collaboration with the need for accountability and measurable outcomes is crucial for the success of the Center of Experimentation. Companies can achieve this balance by establishing clear performance metrics and key performance indicators (KPIs) aligned with organizational goals. While encouraging a culture where failure is embraced as a learning opportunity, organizations must also emphasize the importance of accountability and learning from mistakes. Regular evaluations of experimentation initiatives against predefined KPIs can provide valuable insights into their effectiveness, allowing teams to pivot or refine their approaches as needed while still maintaining a culture of innovation and continuous improvement.

1. Given the emphasis on continuous reinvention and evolution, what strategies or mechanisms can companies implement to effectively incorporate new technologies and methodologies into the Center of Experimentation without causing disruption or losing focus?

To effectively incorporate new technologies and methodologies into the Center of Experimentation without causing disruption or losing focus, companies can adopt a phased approach to implementation. This approach involves conducting thorough research and experimentation with emerging technologies in controlled environments before scaling them across the organization. Additionally, establishing cross-functional teams comprising experts from different departments can facilitate the integration of new technologies while ensuring that diverse perspectives are considered. Regular feedback loops and iteration cycles enable teams to adapt and refine their approaches based on real-world feedback, ensuring that the center remains agile and responsive to evolving technological landscapes.