AI Strategic Plan

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Introduction

Do you feel unsure about how to cross the critical innovation barrier with AI? Our AI Strategic Plan guides you through the frameworks and tools that form a well-considered blueprint for future strategy development. The presentation is ordered into sections for current state assessment, market analysis, strategy design, strategy implementation, resource allocation, and performance monitoring. It breaks down the formidable scope and implications of the AI journey for both technical and nontechnical personnel. The added clarity on how these AI initiatives play into broader business outlooks can better align team members and upper management with a shared understanding and common outlook. Ultimately, the strategic plan serves as a roadmap to encourage the pursuit of ambitious goals balanced with reasonable precaution.

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Unsurprisingly, effective strategic planning helps organizations secure an advantageous position among competitors. But a good AI strategic plan can also turn the innovation hype cycle into real, sustainable growth in the long run. So while your teams embrace future-forward advancements, the organization's foundational mission and core values continue to serve as the guiding principles.

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Current State Assessment

A strategic plan should be supported by contextual and factual reasonings. It's not just what the strategic goals are, but also the motivation and potential business impact of these goals. With that in mind, we begin the presentation with an analysis of the organization's current state of strategic health.

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The first order of business is to evaluate how the latest strategic plan has performed so far. List your previous strategic goals, the initiatives that support each goal, if they've been achieved or not, and most importantly, if they should remain as part of the business's strategy design going forward, or modified, or simply discontinued altogether.

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Competency, Capabilities, Competitive Advantages

Success factors and barriers highlight both the strengths that can be harnessed and the challenges that may impede progress. This allows teams to quickly see which assets can immediately contribute to the organization's AI goals. At the same time, it reveals the barriers that might undermine these efforts. By framing these issues side by side, decision-makers gain a practical sense of what is pushing the organization forward and what is holding it back, leading to more informed discussions on how to prioritize resources and plan mitigation strategies.

Questions and answers

How can understanding success factors and barriers contribute to more informed discussions on resource prioritization and mitigation strategies?

Understanding success factors and barriers can contribute to more informed discussions on resource prioritization and mitigation strategies by providing a clear picture of the organization's strengths and challenges. This knowledge allows teams to identify which assets can immediately contribute to the organization's goals and which barriers might undermine these efforts. By comparing these factors, decision-makers can gain a practical understanding of what is propelling the organization forward and what is holding it back. This leads to more informed decisions on how to allocate resources and plan strategies to overcome obstacles.

What are some potential barriers that could impede the progress of an organization's AI goals?

Some potential barriers that could impede the progress of an organization's AI goals include lack of understanding of AI and its potential benefits, lack of skilled personnel, data privacy issues, and lack of clear strategy and vision. Other barriers could be resistance to change, lack of funding, and technological constraints. It's important for organizations to identify these barriers and develop strategies to overcome them.

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But not every capability area shares the same level of strategic importance. The VRIO analysis determines which AI capability can provide actual competitive advantage, based on the capability's value, rarity, imitability, and organizational leverage. This way, your AI strategy design isn't just based on what's innovative and exciting, but also what's defensible and sustainable in the long run.

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Organizational Synergy

McKinsey's 7S framework – Strategy, Structure, Systems, Style, Staff, Skills, and Shared Values – assesses how well each interdependent element aligns with the organization's AI objectives. By compiling these insights into a single view, the organization gains a clear roadmap for making structural or cultural changes that are essential to the success of AI projects. Ultimately, the 7S approach ensures that AI does not remain a siloed initiative but rather becomes integrated across the organization's core pillars. When each "S" is calibrated to support AI adoption, the organization can move beyond mere experimentation and into a phase where AI is embedded into daily operations, strategic planning, and ongoing innovation efforts.

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How does McKinsey's 7S framework support the integration of AI into an organization's core pillars?

McKinsey's 7S framework, which includes Strategy, Structure, Systems, Style, Staff, Skills, and Shared Values, supports the integration of AI into an organization's core pillars by assessing how well each interdependent element aligns with the organization's AI objectives. By compiling these insights into a single view, the organization gains a clear roadmap for making structural or cultural changes that are essential to the success of AI projects. This approach ensures that AI does not remain a siloed initiative but rather becomes integrated across the organization's core pillars. When each 'S' is calibrated to support AI adoption, the organization can move beyond mere experimentation and into a phase where AI is embedded into daily operations, strategic planning, and ongoing innovation efforts.

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AI readiness assessment underscores the importance of not only having a grand vision for AI but also that the necessary foundations — skills, systems, and support — are in place to make that vision a reality. By assigning scores or ratings to areas such as leadership vision, cross-functional collaboration, and resource availability, it becomes easier to see where momentum is building and where potential roadblocks may exist.

Questions and answers

Why is cross-functional collaboration important in implementing an AI strategic plan?

Cross-functional collaboration is crucial in implementing an AI strategic plan because it ensures that all necessary foundations are in place to make the vision a reality. It allows different departments to work together, leveraging their unique skills and resources, to overcome potential roadblocks. This collaboration fosters a shared understanding and commitment to the AI strategy, which can accelerate its implementation and increase its chances of success.

How does an AI readiness assessment help in identifying potential roadblocks?

An AI readiness assessment helps in identifying potential roadblocks by evaluating various aspects of an organization's preparedness for AI implementation. It assigns scores or ratings to areas such as leadership vision, cross-functional collaboration, and resource availability. This allows for a clear view of where momentum is building and where potential roadblocks may exist. It ensures that the necessary foundations — skills, systems, and support — are in place to make the AI vision a reality.

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Market Analysis

So we've been looking inside the organization. Now let's turn our attention to the outside. The strategy value trajectory graph forecasts the amount of business value that AI developments can bring over time. It segments industry players into "observers", "front-runners", and "innovators" based on the maturity and advancements of their AI strategies. It shows where each competitor falls on the trajectory and your company's relative position. And makes a strong case for the necessity of AI-oriented strategies by showing how much additional value they can bring compared to conventional strategies.

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Macroenvironment

Although AI is usually seen as a technological development, AI strategies have much broader implications. This environment scan uses the PEST analysis to create a 360-degree assessment of every political, economic, social, and technological development that can become either a threat or an opportunity (or in some more ambiguous cases, both) to the company's strategic direction.

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Alternatively, an impact analysis can be used to rate the impact level of relevant market factors, which are categorized into drivers, restraints, and opportunities.

Competitive Intelligence

For those who need to do a more granular deep dive on the individual players, or competitors, in the industry, use the strategic grouping map to position each company based on its AI adoption level and its market scope. From here, you can easily visualize which companies are behind the innovation barrier and need to play catchup, and which companies have already crossed that innovation barrier into more disruptive territory.

Questions and answers

What is the role of AI Strategic Plan in aligning team members and upper management with a shared understanding and common outlook?

The AI Strategic Plan plays a crucial role in aligning team members and upper management with a shared understanding and common outlook. It provides a clear roadmap for AI adoption and innovation, outlining the steps needed to cross the innovation barrier. This shared blueprint ensures everyone is on the same page regarding the company's AI strategy, fostering unity and collaboration. It also helps identify where the company stands in relation to competitors, which can inform strategic decisions and drive progress.

How does the strategic grouping map help in understanding the AI adoption level of different companies?

The strategic grouping map is a tool that positions each company based on its AI adoption level and its market scope. It allows for a more granular deep dive on the individual players or competitors in the industry. By visualizing the map, one can easily identify which companies are behind the innovation barrier and need to play catchup, and which companies have already crossed that innovation barrier into more disruptive territory.

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Similarly, the competitive audit can also be conducted based on multiple comparison areas. Use industry benchmarking to share how your company and its top competitors position next to each other on multiple performance metrics, and how you position relative to the industry average.

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Strategy Design

Now, time to get into all the details of your AI strategic plan for the future. A single-slide overview can be used to summarize all the key components of the strategic plan in a logical sequence, including mission, vision, values, strategic goals, outcomes for goals, corresponding initiatives, and implementation tactics. So even if your audience doesn't look at any other slide in this presentation, this particular slide should still give them a pretty good idea of the key points.

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Outcome and ROI

An indispensable part of any strategic plan is its quantitative impact. The AI strategy ROI analysis starts with defining the short-term, mid-term, and long-term strategic goals and each goal's main success metric. Then, each goal branches out into more detailed AI initiatives. For each initiative, document the amount of investment it requires, and the projected return on investment. Alternatively, metric-based outcomes can also be presented by following the evergreen components of a Balanced Scorecard.

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The strategic planning cone provides a simpler, high-level view of your AI vision. The main point here is to use the spirit of a scenario analysis to present different possible outcomes of your AI strategies.

Triaging

So by now, you probably have a handful of AI initiatives that can be pursued. Based on McKinsey's Portfolio of Initiatives, this nine-grid matrix organizes and prioritizes your AI initiatives based on risks and timeframe.

As risk management is such a critical part of AI deployment, your strategic plan needs to have the foresight to prevent and mitigate those risks. This risk analysis maps each AI strategy risk based on probability and impact. Document each risk's severity category, its risk type (which can be reputational, financial, operational, competitive, and governance), and its priority level.

Questions and answers

What are the different types of risks that can be encountered in AI strategy?

The different types of risks that can be encountered in AI strategy include reputational, financial, operational, competitive, and governance risks. Reputational risks involve damage to the company's reputation due to AI failures or misuse. Financial risks are related to the costs of implementing and maintaining AI systems. Operational risks involve the potential for AI systems to fail or not perform as expected. Competitive risks are associated with competitors potentially gaining an advantage through more effective use of AI. Governance risks involve the challenges of managing and overseeing AI use within a company.

How does risk management play a crucial role in AI deployment?

Risk management plays a crucial role in AI deployment by identifying, assessing, and prioritizing potential risks associated with the use of AI. These risks can be reputational, financial, operational, competitive, or governance-related. By mapping each risk based on its probability and impact, organizations can develop strategies to prevent or mitigate these risks. This proactive approach helps to ensure the successful implementation and operation of AI systems, while also protecting the organization from potential negative consequences.

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Strategy Implementation

After deciding the goals and outcomes of your strategic plan, discuss how the plan will be implemented. The AI strategy acceleration timeline shares a blueprint of the foundational changes that the organization has adopted and conquered so far. Not surprisingly, with successful integration of AI strategies, the company will be more likely to achieve exponential, instead of slow linear, growth.

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How does the AI strategy acceleration timeline contribute to the successful integration of AI strategies in a business?

The AI strategy acceleration timeline plays a crucial role in the successful integration of AI strategies in a business. It provides a blueprint of the foundational changes that the organization has adopted and conquered so far. This timeline helps in understanding the pace at which the organization is able to adapt and implement AI strategies. It also serves as a roadmap for future strategy development, enabling the organization to anticipate challenges and prepare for them in advance. This proactive approach can lead to exponential growth, as opposed to slow linear growth.

What are the key elements in implementing an AI strategic plan for exponential growth?

The key elements in implementing an AI strategic plan for exponential growth include setting clear goals and outcomes, creating a detailed implementation plan, and establishing a timeline for strategy acceleration. It's also crucial to make foundational changes within the organization to successfully integrate AI strategies. With these elements in place, a company is more likely to achieve exponential growth.

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And from that high-level roadmap, we transition to a more detailed roadmap to show the organization's strategic focus areas in this AI journey, the relevant context that was analyzed to come up with these focus areas, the specific efforts that matter the most in each focus area, and an array of positive value outcomes of your AI strategies.

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Roadmap and Timeline

If your AI strategy plan includes a longer timeframe, the implementation roadmap can be divided into multiple phases. The graph shows the resource needs for multiple implementation scenarios, and the objective and initiatives that need to be fulfilled for each phase.

For more on-the-ground activities and operations that are needed to implement the strategic plan, the Hoshin Kanri table plots how each initiative connects to the broader company mission and strategic goals. Plus, it assigns ownership to team members who are responsible for each part of the strategy implementation.

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While the Hoshin Kanri Matrix can be used for any implementation plan regardless of the strategic focus, an AI strategic plan may require more implementation details from a technical angle. The AI strategy implementation cycle details the data modeling and engineering requirements that are needed, so that the AI strategy can be properly deployed and securely integrated into the projects and workflows that support the strategy.

Questions and answers

How can the AI strategy be securely integrated into projects and workflows that support the strategy?

The AI strategy can be securely integrated into projects and workflows by following a detailed implementation plan. This plan should include data modeling and engineering requirements. It's crucial to ensure that the AI strategy aligns with the overall business objectives and that all stakeholders understand and support it. Additionally, security measures should be in place to protect sensitive data and algorithms. Regular audits and updates should also be part of the strategy to ensure its effectiveness and security.

What are the technical requirements for deploying an AI strategy according to the AI strategy implementation cycle?

The technical requirements for deploying an AI strategy according to the AI strategy implementation cycle include data modeling and engineering requirements. These are necessary to ensure that the AI strategy can be properly deployed and securely integrated into the projects and workflows that support the strategy. It's also important to consider the technical infrastructure, such as servers, storage, and network capabilities, as well as the software tools and platforms used for AI development and deployment.

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