How to Develop a Strategy for Artificial Intelligence

 

by David Wilsey

Leaders I talk to are expressing a common concern that keeps them up at night: what to do about artificial intelligence (AI). Since the launch of ChatGPT, Google Bard, AI-supported Bing, and other competitors, it has become clear that the strategic environment for most of us is about to be upended by this new technology. A common problem these leaders face is forming a coherent strategy for AI; compounding the problem – they don’t know where to start.

The good news is that formulating a strategy for harnessing the potential of AI and avoiding being left behind is very similar to formulating strategy for any other area of strategic inquiry. Like any other planning effort, the balanced scorecard and the first six steps of the Nine Steps for Success™ can provide a disciplined framework for organizing your team’s efforts. This blog highlights the most important elements to consider. For a more comprehensive understanding of the Nine Steps, I’d recommend reading The Institute Way or taking our Balanced Scorecard Professional Certification class.

Program Launch

Any Nine Steps effort begins with a program launch, where we plan the planning effort itself. The program is launched by a project champion(s) and key stakeholders. Any existing material related to this effort is examined, a gap analysis is completed, key stakeholders are interviewed, and other assessment activities are completed.

Stakeholder Engagement

Part of the program launch is determining who should take part in the strategy formulation. If you want your teams to buy into the strategy, they need to be a part of the process. For an AI effort, this could include executives, department heads, data scientists, IT personnel, legal and compliance teams, and end-users. The goal of any outreach and inclusion is to solicit their input, address concerns, and build strategic alignment.

Step 1: Assessment

Like any other strategy development effort, strategic thinking starts with an assessment of our current situation. Beyond the typical internal and external environmental analysis, the organization will want to do an AI Readiness Assessment.

AI Readiness Assessment

Any strategy formulation effort will require an assessment of organizational readiness. This is even more critical with disruptions like AI. Most organizations will want to evaluate existing technology infrastructure, IT capabilities, and skills gaps. This might include a data assessment to determine the gaps and limitations in data collection, storage, and quality as well as the related data infrastructure, governance, and security. This is when you’ll start the conversation around the rationale for adopting an AI strategy. Depending on the strategy selected later, there might be a need for additional resources, tools, or partnerships to successfully implement AI initiatives. Some superficial training will likely be in order, as everyone will come into the conversation with a different understanding of what AI is and what the possibilities include.

Picture of the Future

Instead of the standard organizational Vision and Mission statement development that would typically happen during Step One, AI strategy will require clearly articulating the organization’s picture for future AI adoption success. Why is the organization going down this path? Is it simply because it is trendy, or is it because of a clearly perceived desired benefit, such as improved efficiency, enhanced decision-making, cost savings, or competitive advantage?

Environmental Analysis

The strategic environment for AI is then assessed, where the team discusses all the known internal and external factors influencing AI adoption. The team should consider technological readiness, data availability and quality, regulatory and ethical considerations, market trends, potential risks/challenges, and competitive landscape.

Step 2: Strategy

Building on the assessment, organizations formulate/clarify strategy in the Strategy step. In an organizational strategy effort this would be about creating high level Strategic Themes, Results, and Perspectives. If we are more narrowly defining an AI strategy, this is where we articulate how AI is influencing our current strategy or define one or more new themes. The strategic themes and results selected will provide high level context for the specific objectives that will be selected and mapped in the next steps related to AI.

Step 3: Strategic Objectives

High level themes are broken down into continuous improvement activities in Step 3. This is where we outline specific objectives related to AI, such as increased revenue growth, improved customer satisfaction, increased operational efficiency, and/or improved innovation. Again, the specifics will depend on the exact AI strategy. Are we incorporating machine learning into our product offerings or are we simply using AI internally to create operational efficiencies?

Step 4: Strategy Map

The objectives identified in Step 3 should work together to tell a coherent cause-effect story. This story is illustrated with a strategy map, which is a graphic that shows the cause-and-effect relationships of objectives across the four perspectives, telling a story of how the organization will achieve the results desired.

Step 5: Performance Measures and Targets

No strategy implementation is effective if you don’t have a way to measure success. In Step 5, metrics/KPIs are defined to measure the effectiveness and impact of the AI initiative. The emphasis in this step is on helping you develop the critical leading and lagging measures needed to manage strategy execution. As measures are implemented, progress is monitored, outcomes are evaluated, and adjustments are made as necessary, using feedback loops to learn from pilot projects and continuously improve AI capabilities.

Step 6: Strategic Initiatives

In Step 6, the projects that are critical to success of the AI strategy are developed, prioritized, and implemented. Initiatives help close performance gaps in performance to hit targets. It is important to focus the organization on the execution of the most prioritized strategic projects versus creating a long list of potential actions and projects. In AI implementations, pilot projects are often developed and implemented to validate AI use cases.

Program Rollout and Strategy Execution Considerations

Once Step 6 is complete, the AI strategy is ready to be rolled out. The goal of this part of the process is to create more internal fans and build a coalition of employees that understand and support the new AI strategy. Once the strategy is ready for implementation, care must be made to manage common strategy execution challenges, such as:

• Implementation: Create a detailed roadmap for AI implementation. Define the sequence of initiatives, resource allocation, timelines, dependencies, and milestones. Consider an iterative and agile approach to accommodate evolving technologies and organizational needs.
• Change Management and Training: Develop a comprehensive change management plan to address the organizational and cultural aspects of AI adoption. Identify training needs for employees to acquire AI-related skills and competencies. Foster a culture of continuous learning and innovation.
• Governance and Ethics: Establish a governance framework for AI to ensure responsible and ethical use. Define guidelines for data privacy, bias mitigation, transparency, and compliance with relevant regulations. Establish mechanisms for ongoing monitoring, evaluation, and accountability.
• Risk Management: Identify potential risks associated with AI implementation, such as cybersecurity threats, algorithmic biases, legal and regulatory compliance, and job displacement. Develop strategies to mitigate these risks and establish contingency plans.
• Collaboration and Partnerships: Explore opportunities for collaboration with external partners, such as AI vendors, research institutions, and industry experts. Leverage their expertise, resources, and best practices to accelerate AI adoption and stay updated with the latest advancements.
• Budget and Resource Allocation: Develop a detailed budget for AI initiatives, including infrastructure, software, talent acquisition, training, and ongoing maintenance. Ensure sufficient resources are allocated to support the implementation and scaling of AI projects.
• Ongoing Strategy Review: Establish a regular strategy review cycle to impose discipline to the execution process, improve performance, create accountability, and adapt strategy to ongoing new developments.

Conclusion

A comprehensive AI strategy is essential for organizations to harness the potential of AI, drive innovation, improve efficiency, and maintain a competitive advantage in today’s rapidly changing business environment. While a more coherent strategic plan might not prevent oncoming AI disruptions, a more disciplined approach to AI planning will communicate the organization’s commitment to continuous improvement, innovation, and responsible AI adoption.

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