Accelerate AI Maturity by Focusing on Operational Excellence

7 minute read

Jul 8

Vice President of Operations

Sand Technologies

There’s no denying enterprise and municipal excitement about the potential of AI . Yet that enthusiasm hasn’t yet been matched by adoption. Reports show that while over 72% of global companies have integrated AI in their business, many are still in the experimentation phase.

To truly unlock AI’s potential and accelerate their AI maturity, organizations need to transition from viewing this technology as novel to perceiving AI as a strategic driver of enterprise-wide transformation. The first step is building operational excellence, setting the correct foundations and processes to embrace and scale their AI initiatives. Achieving this requires some key levers to be in place to ensure operational excellence.

What is Operational Excellence, and Why Does it Matter?

What do we mean by “operational excellence”? Business operations are a key component, but it’s about more than streamlining processes. At its core, it’s about reshaping how people think and work. Successful AI integration is as much of a “people” issue as it is a technological one. An AI-ready management philosophy must place an emphasis on continuous improvement across all aspects of the business. When companies empower both management and employees to make changes that lead to greater efficiency, agility and effectiveness, those same people will quickly identify ways in which AI can improve their jobs, not replace them, and accelerate overall enterprise success.

Operational excellence lays the groundwork for accelerating AI maturity. Companies that excel in harnessing AI’s full potential have advanced data and technology capabilities, as well as a clear strategic vision, a skilled workforce and a culture that embraces innovation. Here’s how operational excellence drives AI success:
  • Solid Infrastructure: AI’s ability to make accurate decisions hinges on the quality of data available. By focusing on infrastructural operations, companies can develop robust and reliable systems that enable the smooth operation of AI systems and data pipelines, and ultimately better AI-driven outcomes.
  • Strong Process Foundation: Scaling AI across an organization requires repeatable and measurable processes. Emphasizing the identification, documentation and streamlining of processes creates a strong foundation for AI initiatives to have org-wide impact.
  • Innovative Environment: Successfully implementing AI and ensuring it brings value requires great innovation and exploration. Excellent operations cultivate a culture of learning, experimentation, adaptation and continuous improvement, all of which empower employees to actively seek ways to maximize AI’s potential.

By focusing on operational excellence, companies can build a launchpad from which to accelerate their transformation from an early-stage “paper powerhouse” to an advanced “AI vanguard.” By continuously moving to the next level in AI maturity, companies can begin to drive faster growth, greater ROI and build or widen their competitive edge. To find out where your company or city exists on the AI-readiness spectrum, take our quick assessment.

Key Operational Excellence Enablers

There are various frameworks and methodologies to help achieve operational excellence. In a conversation exploring the subject, McKinsey leaders Ted Iverson, Ferran Rujol and Joris Wijpkema argue that operational excellence is about “uniting an organization around a common purpose, process and systems.” So how do you create an environment that facilitates this?

We’ve spent years developing a unique framework to drive operational excellence by focusing on four key ingredients: distributed leadership, rhythms and routines, technology and partnerships.

Distributed Leadership

AI adoption demands a new type of leadership with new skill sets and mindsets, especially in today’s dynamic landscape of hybrid and distributed work. As such, companies that rise to become AI Vanguards have embraced new leadership models that fuel radical innovation and unconventional ways of working at hyper scale. One such model, adopted by Uber and others, is distributed leadership. This collaborative and empowering approach moves away from the traditional “heroic leader” model and relies on a network of formal and informal leaders across an organization. Through decentralizing leadership, managers and frontline staff are empowered to make decisions without constant approval from senior management. Ultimately, this frees up senior leaders to focus on developing innovative long term AI strategies. It also fosters a culture of ownership throughout the organization leading to faster responses, improved problem-solving and a more engaged workforce. Finally, distributed leadership ensures that an organization’s impact and exposure to the market is increased.

To truly unlock AI’s potential and accelerate their AI maturity, organizations need to transition from viewing this technology as novel to perceiving AI as a strategic driver of enterprise-wide transformation.

Rhythms and Routines

While many organizations have begun experimenting with AI, the real challenge is scaling those efforts across the organization. Quite often, the integration of new technologies impacts not just the quality and delivery of services and products, but also employee productivity and customer service. AI is no different.

Yet scaling AI requires a particular focus on building a solid operational foundation. In his book ‘Mastering the Rockefeller Habits,’ author Verne Harnish argues that high-growth companies often cultivate certain habits that enable them to maintain high performance and consistent delivery even as they scale. One key habit is establishing robust rhythms and routines that streamline information flow, promote alignment across teams, and empower rapid decision-making.

The rhythms and routines should address performance tracking (tracked goals and tracking cadence) and reporting structures (who reports, what information is reported and reporting formats). By addressing these aspects, these rhythms and routines become a bedrock for successful AI scaling, enabling swift decision-making based on real-time insights.


The key to AI maturity lies not just in building advanced customer-facing solutions, but in the internal digital transformation that fuels them. Many organizations struggle to recognize the critical role their technology infrastructure plays in fuelling success. Yet its impact is not limited to the engineering team; technology and data can revolutionize how every department operates and unlock new levels of efficiency and innovation. By investing in available technology platforms or bespoke solutions, companies can unlock a future-proof foundation that empowers them to:
  • Reduce risk with greater reliance on systems
  • Enable data-driven continuous improvement through collecting rich data insights
  • Reduce costs with optimized workflows and automated processes
  • Scale with confidence while boosting customer and staff satisfaction


Finally, it’s important to acknowledge that the road toward AI maturity does not need to be tackled in solitude. As the AI global market continues to grow, seeking partnerships with established AI players or complementary technology firms can significantly accelerate an organization’s progress and unlock several key advantages.

Firstly, partnerships can grant access to cutting-edge expertise and resources that allow companies to leverage pre-built solutions and best practices beyond their internal capabilities. Such collaborations can also foster knowledge sharing and innovation, which then further accelerates internal AI learning curves.

Partnerships can also open doors to new talent pools and specialized skills, filling critical gaps in an organization’s AI development team. By working together, companies can overcome internal limitations and reach their AI maturity goals faster and more effectively.

AI presents a powerful opportunity for organizations to transform their operations and achieve significant competitive advantages. However, achieving this AI endgame requires a deliberate shift from experimentation to a strategic, organization-wide approach rooted in day-to-day operations. By embracing operational excellence and its key enablers, and routinely evaluating where they exist on the AI readiness spectrum, companies can build a solid foundation for accelerating their AI maturity.


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