It’s been a few years now since AI first captured the interests of many around the world – and it’s safe to say it’s here to stay. Models and their capabilities have come a long way, offering us multiple benefits, particularly when it comes to productivity and efficiency.
With organizations across the globe now rapidly adopting AI, and integrating it into business processes, AI now needs its own strategy. However, those that are the most successful don’t focus only on AI. They focus on how AI can improve human decision-making, keeping their “true north” at the heart of every initiative.
When it comes to strategy, there are still some that are struggling to embrace the technology and unlock additional capability – limiting the success of organizational strategy. The opportunity lies in how AI is used by organizations to support planning, execution, and strategy management, rather than relying solely on the tech to provide the answers.
What are you trying to achieve or resolve?
Before you begin thinking about using AI within your strategy, you need to get to the root of the business problem you’re trying to solve. Many organizations start trying to fix problems or issues without finding or explicitly defining the cause. Ask yourself:
When you focus on the “why” that’s driving your organizational strategy, the objectives (and associated projects) will become clear. Strategy is complex, comprising multiple moving parts. It is easy to fall into the trap of “when” will everything happen, rather than focusing on why it should happen. This “when” mindset can lead to poorly informed decisions, especially when looking at tooling to enhance processes and outcomes.
Once you have this deeper understanding of what’s driving your strategy, you will be able to identify areas in which AI can support you. This will also include mapping out your current strategic processes, typical timeframes, who is involved, and how well they serve your organization. Only then will you be able to begin introducing AI to your strategy – otherwise you risk making a large investment only to see minimal returns (but more on that later).
You will also need to look at your existing IT infrastructure and various tech stacks to work out where AI will fit in, along with its specific use cases. Try to determine a rough percentage of how much AI can contribute to supporting your strategy, and solving the business problem at hand. This will help in highlighting your ideal use cases. Prioritize scalability over specific applications, as introducing AI without these considerations can cause unwanted disruption.
Planning your strategy can take a lot of time and dedication to source all the relevant data, opinions, and expertise that will shape future activities. AI will not, and should not, define your strategy. Instead, AI should be used to support the planning process by helping strategy leaders (who have contextual expertise to fall back on) to understand their options ahead of committing to one idea only. Approaching strategy planning with an iterative mindset will help you in defining the best course of action for your organization.
AI is a great tool for research, whether that’s looking at data from outside the organization (such as competitor analysis, world or industry-related news) or sourcing data from within. Models can now quickly scour the internet or internal databases to find relevant information – market conditions, operational performance, potential risks – that will help you make informed decisions. As these searches take much less time than manual efforts, you’re able to perform more regular analysis and ensure your data is current. This is particularly valuable if your organization works across multiple geographies, sites, or business units.
Analyzing various data sources to derive insight can be rather time consuming, especially if you typically work with various models and frameworks to organize your thoughts. You might even need to compare potential outcomes to challenge assumptions, or simulate potential scenarios for deeper understanding. Using AI as part of the analysis process makes this more efficient – especially if working with large volumes of data or using a model that’s purpose-built around strategic frameworks. AI models are excellent at pattern recognition, and can identify trends that might have been otherwise missed by a human.
The execution phase of strategy is usually where organizations start to struggle, particularly when it comes to turning written plans into activities (and outcomes). You might find that strategic and operational priorities compete for attention, efforts aren’t connected to objectives, or there is a lack of buy-in from teams. While AI can’t currently directly execute a strategy (but maybe in the future as the technology advances), it can help with removing the barriers that prevent successful execution. It’s important to consider that, especially if you’re not as familiar with AI, that implementations can take up to eight weeks before you begin to realize clear results.
Strategy creates the most value when it is executed consistently and is definitively aligned to objectives. Keep changing plans, or deliberating over ideas, will only impede progress towards outcomes. The strategic vision might be simple to those leading the strategy, but if this isn’t organized into achievable goals and activities across various departments or teams, then execution will suffer. AI can help with this by cascading objectives across the organization to the right people – ensuring they only focus on their role in the strategy. It can also help with translating the high-level goals into lower-level objectives and defining KPIs to determine success.
The most successful strategies are those where teams actively collaborate, using a wide variety of expertise and lived experiences. Introducing AI to strategy isn’t about replacing the people involved, but rather elevating their capabilities. Agentic AI specifically removes data silos in a business by centralizing all relevant data and activities that would usually be kept in isolated information systems – improving overall alignment and coordination between teams. When everyone involved is able to access the same up-to-date information, strategy can be performed from anywhere regardless of physical location. Strategy leaders also benefit as they will be able to quickly understand how the strategy is progressing and make more informed decisions.
Additionally, this clear oversight into strategy – at any point in planning, execution, or management – helps leaders to identify performance levels, such as team output and skills, and which activities can be supported by AI in the (near) future. If you’re looking to increase your AI adoption, this will also aid in future strategy discussions as you will have a better understanding of which particular activities are best suited to people, and which to a model.
Instead of viewing AI as an independent tool, it should be viewed as a complementary partner to existing skillsets. Improvements in models to introduce reasoning capabilities helps to strengthen human judgment, execution, and autonomy over strategic responsibilities – extending an individual’s capacity to deliver.
Organizations should be careful not to view efficiency as the end goal. Yet an undeniable benefit of using AI is efficiency, and its ability to automate tasks that otherwise require a lot of manual input. Instead of thinking about efficiency, it’s worth seeing these gains as something slightly different: productivity. The trick to realize productivity, especially through AI tooling, is how you then use this additional capacity.
The main aim of strategy is to create value. Growth is a demonstration of value. Productivity becomes growth through the reassignment of additional capacity. Without allocating that additional capacity into higher priority activities, efforts become wasted – limiting the return on your (AI) investment.
Throughout the lifecycle of your strategy, you’re going to need to ensure it is well-managed. What’s working well? What not so well? Do your priorities need to shift? Are there any adjustments you can make? All questions that are answered with regular reviews and retrospective meetings.
However, like with a lot of strategy, this is largely a manual process gathering information that represents a point in time, and not always the entire picture. Using AI within strategy management helps organizations to build a continuously updated view of strategy execution – leading to more informed decisions.
The overall time period from planning to management means a lot can change. Collected data from the planning phase could be different or even no longer relevant, or the situation and surrounding factors have evolved. Effective strategy management is how you respond to these changes (i.e., your strategic adaptability and agility) without losing sight of your core objective.
AI allows organizations to move to an ‘always-on’ approach, constantly seeking new sources of information that could impact the strategy – whether that’s internal or external data. Strategy leaders no longer need to wait until a set checkpoint to understand what’s changed or what the next steps will be. Instead, smaller iterative adjustments can be made as required to keep things heading in the right direction.
The ‘always-on’ ability means decisions made by those leading the strategy will be more informed, as data will be more current and representative of the wider landscape. Agentic AI in particular can also assist with data analysis for decisions, interpreting data as they find or receive it. This includes looking at data flows within the organization and highlighting sudden changes or anomalies, and placing these changes into the wider business context. Rather than only looking at a single data point, the model connects the information from across the organization to provide deeper insight for decision-making – and ensures leaders don’t miss out on crucial information that can impact strategy.
Strategy works best when following an iterative approach with feedback loops incorporated. You are consistently trying different ideas and optimizing the method when required, based on feedback from initiatives. Normally this is through retrospective meetings or by using specific methodology, such as the OODA loop.
AI models support creating feedback loops through their continuous monitoring of data sources to highlight new trends, risks, and current performance levels. In time, this feedback loop becomes a continuous embedded cycle, rather than being treated as a standalone activity. Regular feedback and strategy optimization increases your strategic agility, improving the ability to adapt when conditions change.
Despite the technological advances AI models have experienced, human oversight is still necessary. Whether you’re using your own on-premise instance or an off-the-shelf solution, you will still need to involve an individual who can enforce boundaries and ensure the outputs are accurate. The individual is responsible for challenging the recommendations – so priorities aren’t competing and focus is correctly applied – using their own expertise and contextual knowledge of the organization.
When looking at how you can use AI in your business to support strategy efforts, it’s important you understand where the potential risks are ahead of deployment. If you work in a regulated industry, you will need additional governance to meet requirements. Once the risks have been identified, you will be able to design your AI strategy and associated governance measures. Try to consider the scalability of these efforts to give your AI strategy additional longevity.
Not all AI applications need the same level of oversight. For example, if you are using models for high-level meeting summaries, this will fall lower on the governance priority list. If you are conducting financial analysis to allocate resources for your strategy, there is a higher level of associated risk as the output will have a greater influence on decisions, and therefore stronger controls must be in place.
The prevalence of AI, coupled with its future potential, means it will only become more embedded into our working (and daily) lives. When using AI for strategy, the most successful organizations will be those that approach AI with a clear plan of how it will be used, and how it will create value.
Its purpose isn’t to replace your existing employees, but rather empower them to achieve their goals. Whether that’s finding efficiency within strategy planning or gaining deeper insight to make more informed decisions, your AI instance should be treated as a tool that enables individuals to contribute to strategic outcomes. There is a balance to be found between human expertise and automation, and once you get this right you will be better positioned to execute your strategy with confidence, adapt to change, and deliver real value for your organization.