Digital Economy Dispatch #208 -- What is AI Strategy?

Digital Economy Dispatch #208 -- What is AI Strategy?
3rd November 2024

As the rolling thunder of new AI technologies and tools reshapes the business landscape, organizations worldwide are grappling with how to put together their AI plans. In my discussions with a variety of teams, digital leaders, and executives across the public and private sector, I am finding a dominant theme to these efforts is how to develop comprehensive frameworks to guide them in deploying AI at scale.

With increasing pressure to deliver better services and productivity gains, many organizations are shifting gears from experimenting with AI to broader, more substantial rollouts. As part of their accelerated AI plans, they aim to design schemes that will ease this transition. However, the more I talk with these organizations, the more I realize that current approaches to AI strategy often reveal fundamental misconceptions about what should and shouldn’t  be part of an AI strategy. Or, perhaps more fundamentally, whether they actually need an explicit and separate AI strategy outside of their current plans for driving growth through digital transformation.

Examining different efforts, what I see in practice are two predominant approaches to AI strategy formulation, both of which, in my opinion, fall short in addressing the complexities of systematic AI-at-Scale adoption.

The first approach manifests itself as a collection of abstract ideals primarily focused on highlighting how AI can bring many benefits but must be handled with care to avoid some of the many downsides that this intrusive technology can bring. This is an important message. But typically goes no further than pointing out the obvious: Care must be taken when adopting technologies such as AI. However, in isolation, pointing out the need for a balanced approach does little to guide the organization in the practical choices being made on a daily basis.

The second typically emerges as an elaborate wishlist of AI capabilities describing a wide collection of AI capabilities that are currently being explored or that may soon be available in some unspecified future. Again, this can be helpful to indicate where the organization is placing focus. However, its impact is hugely limited without addressing a range of fundamental concerns about the organizational changes required for success.

So, if this is the case, what is a more useful way to construct an AI strategy, and what key elements should it contain?

The Core Elements of an Effective AI Strategy

Over the years I have worked with many organizations on a wide variety of strategy documents. Across public and private institutions in several countries, I’ve had the chance to be part of some extraordinary efforts. While my experiences have varied considerably, most recently their primary focus has been on digital technology adoption and how to drive digital transformation across large, complex organizations. In that time, I have found that these statements of strategy are more effective if they place focus on three fundamental characteristics that galvanize disparate communities, place emphasis on key objectives, and guide individuals in their everyday actions. I call these factors Aspirational, Inspirational, and Transformational.

Aspiration

First, they must be aspirational, creating a vivid vision of a future state that serves as a unified destination for all stakeholders. Details on these aspirations may not be needed. However, they also cannot be too vague. For example, common statement supporting "AI for Good" and “democratizing AI access” are all well and good. Yet, to ensure they don’t backfire, they must be amplified with more specific examples and illustrations that paint a picture of how these ideals are interpreted in contexts that are meaningful to this community.

Inspiration

Second, successful strategies must be inspirational, generating genuine enthusiasm and commitment to the considerable effort required for moving forward. Any meaningful change is disruptive, disconcerting, and exhausting. Sustaining change requires a clear purpose and explicit outcomes that will make a difference to those bearing the brunt of the effort. Not just “fine words”. They must ensure individuals feel able to contribute to a cause that matters.

Transformation

Third, they must be transformational, precisely identifying key areas for change and establishing clear priorities. Again, at this level it may not be necessary to provide formal dates and detailed numerical goals. However, it should, for example, highlight where improvement in operational efficiency and service delivery improvements will occur and the benefits this brings in the near and longer term through staged AI deployment.

Building a Comprehensive AI Strategy

These three characteristics can form the basis for a meaningful AI strategy. However, on their own they too often float above the reality of most people’s daily experiences, disconnected from the actions and decisions they make. Successful AI strategies incorporate several critical components that work in concert to drive implementation. At their foundation lies a set of fundamental principles guiding AI adoption. This might include, for example, details of how the stated AI principles guide project decisions, providing a practical framework for development and delivery teams. But more is needed.

Organizational values form another crucial component, providing the ethical framework necessary for navigating the challenging decisions that inevitably arise during AI transformation. These values become particularly relevant when addressing sensitive matters such as AI-driven automation initiatives, workforce evolution, and skill development programs.

However, these practical considerations will only be relevant if they acknowledge the organization’s current status and context. A thorough (and honest!) contextual analysis represents another vital element of effective AI strategy. This analysis must encompass a comprehensive evaluation of the organization's current position, including its capabilities, limitations, and market context. In today's dynamic technological landscape, this assessment proves invaluable in understanding how the organization's strengths and weaknesses align with evolving customer needs, competitive offerings, and on-going potential market disruptions.

Based on this understanding, two further elements can now be defined as immediate next steps. First, strategic roadmapping emerges as a critical component that transforms abstract objectives into concrete plans. This roadmap should clearly delineate the major milestones and events that stakeholders can anticipate in both the near and long term. The most effective roadmaps I've encountered provide clarity without sacrificing flexibility, allowing organizations to adapt to emerging opportunities and challenges while maintaining strategic direction.

Second, a performance measurement framework constitutes the final essential component of comprehensive AI strategies. Based on organizational values and objectives, these frameworks should explicitly define the metrics by which progress will be evaluated and interpreted. This transparent approach to measurement ensures all stakeholders understand how success will be defined and assessed throughout the transformation journey.

The Strategic Imperative

Looked at in this way, the key question facing organizations today is not whether they require an AI strategy, but rather what form that strategy should take to best serve their specific context and objectives. Whether explicitly and formally declared to be “THE AI Strategy” or more modestly promoted in terms of impacts on existing ways of working and operating depends on the local culture and politics. Often, for change to take place there has to be a change owner and a “flag to wave”. In other cases, bringing in AI capabilities by adapting on-going activities without great fanfare is the way to go.

Regardless of the approach, introducing these key elements is essential in transitioning from experimenting with AI to delivering AI-at-Scale. A path is required that supports your organization in receiving the benefits of AI while managing its risks. As AI continues to reshape society and business models, a well-crafted understanding of these AI impacts becomes increasingly crucial for maintaining competitive advantage and ensuring sustainable growth.

However, it's essential to recognize that AI strategy initiatives should not exist in isolation. The most successful organizations I've worked with integrate their AI initiatives seamlessly into their broader digital transformation efforts and overall business strategy. This integrated approach ensures that AI initiatives align with and support the organization's broader objectives and operational framework.

Looking Ahead

All organizations considering AI adoption must determine whether an AI strategy is necessary and define its key elements. This strategy should focus on realizing AI's potential while mitigating associated risks. This is necessary, but on its own it is insufficient. Organizations should not only consider the broad concepts of an AI strategy but should also relate this to specific components and implementation approaches. The strategy developed should reflect the organization's unique context, capabilities, and aspirations while providing clear direction for the transformation journey ahead.

In my experience, the most effective AI strategies are collaborative efforts that function as living documents, evolving in response to technological advances and changing organizational needs. The key lies in creating a strategic framework that provides clear direction while maintaining sufficient flexibility to adapt to emerging opportunities and challenges. This balance between robustness and adaptability ensures the strategy remains relevant and effective throughout the organization's journey to delivering AI-at-Scale.