The Pillars of a Successful Artificial Intelligence Strategy 16 September 2025 - ID G00805859 - 17 min read By Pieter den Hamer, Raghvender Bhati TOPICS The enormous potential business value of AI is not going to materialize spontaneously. AI leaders should guide their organization toward an era in which AI is not only creating tangible business value, but goes beyond to become a critical competitive differentiator and industry disruptor. Shareable Summary View & download slides Overview Key Findings Despite the outlandish AI hype, turning the promise of AI into reality is not a given: 49% of leaders highly involved in AI report that their organizations struggle to estimate and demonstrate the value of AI. Many find it challenging to go beyond the piloting of AI to scale it across the organization and achieve higher strategic impact, identifying value opportunities that are most relevant to their business strategy and markets. Successful value creation with AI requires more than technology. A lack of the right investments in data, change management, AI literacy, risk mitigation, trust and governance represents a significant obstacle to AI success and value realization. AI is only just getting started and will continue to evolve rapidly. With AI being both a catalyst and a disruptor of business and markets, companies are challenged to frequently update their AI and business strategies. Recommendations Create an AI strategy revolving around a vision that is fully aligned with business goals, market conditions and competitive pressure regarding the use of AI. In addition, the AI strategy should identify key value opportunities as well as risks. Make the AI strategy executable by setting priorities for a portfolio of concrete business-related AI initiatives, and by setting planning goals to build and mature an AI operating model. Keep the AI strategy up to date by frequently aligning or realigning it with the business strategy and vice versa while continuously seeking synergies with complementary strategies such as digital/IT, and data and analytics (D&A) strategies. Introduction The Pillars of a Successful Artificial Intelligence Strategy Playback Icon AI has quickly become pervasive and its potential is on the agenda of every organization. AI is the No. 1 technology that CEOs believe will most significantly impact their industry in the next three years (see 2023 CEO Survey — The Pause and Pivot Year).The question is not if but where, when and how to adopt and apply AI. Many organizations have initiated or expanded their AI (including generative AI) activities, and even more have announced very significant new or further investments. As for generative AI, a recent survey revealed that 18% of business leaders are piloting, implementing or have implemented it for their functions, while 47% will do so in the coming 12 months.1 However, many current AI initiatives are still experimental and exist as isolated projects. This fragmentation leads to difficulties in scaling, managing risks and realizing business value. Although experimental siloed approaches may be useful for building skills and learning what AI can and cannot do, they are not enough to create sustainable business value. Organizations that have successfully created value with AI have managed to go beyond the phase of experimenting with AI and piloting it. Invariably, they have built an AI strategy that is well aligned with their business strategy. In other words, scaling AI to create real value and to transform the organization requires an AI strategy. First and foremost, a vision about the strategic impact of AI on the organization needs to be developed. In addition, the AI strategy sets the priorities for a portfolio of AI initiatives, through which the actual business value is realized. To enable these initiatives, the AI strategy also sets planning goals for a roadmap to develop an AI operating model, including key AI capabilities in terms of technology, data, organization, literacy, engineering and governance. See Figure 1 for an overview of these elements. Figure 1: The AI Strategy in Context Elements within and related to the AI strategy include business strategy, AI portfolio and AI operating model. AI strategy is not a stand-alone concept and can only be effective if it is fully and frequently aligned with business strategy. This research is focused on the AI strategy. But as emphasized by the figure above, the AI strategy does not stand alone. To be effective, it must be fully and frequently aligned with the business strategy, as well as other enabling technology strategies, most notably those for IT and D&A. Moreover, the AI strategy only becomes a reality when it goes hand in hand with an AI portfolio and adequate AI operating model. This research, as well as the AI strategy template attached below, provides guidance to AI leaders responsible for developing and maintaining an AI strategy. View and Download Presentation Slides Poster Analysis The Foundation of an AI Strategy The foundation of an AI strategy is about vision, drivers and risks, focused on the essence of what an organization wants to achieve with AI, fully aligned with its business strategy. Preferably, this emerges from (repeated) discussions that the AI leader has with relevant stakeholders. In the case of the AI strategy, this involves senior, C-level managers. Their business priorities, opportunities, challenges and concerns related to AI should find common ground in the AI strategy (see Figure 2). Figure 2: Core Elements of an AI Strategy The figure shows the close alignment between AI strategy and business strategy, as well as foundational elements within the AI strategy, namely, vision, drivers and risks. The foundation of an AI strategy derives from repeated discussions between the AI leaders and relevant stakeholders. To develop an AI strategy, AI leaders can use the following activities and questions as guidance (for further details, see the attached AI strategy template): Formulate a vision. Together with C-level stakeholders, the AI leader should identify and formulate a vision that answers the question about the importance of AI to the organization, given its business goals, current market circumstances and competitor activities (see Note 1). Is this a key priority for your organization now? How critical is AI to the future of the organization, and how close is that future? To which business units and stakeholders is the AI strategy most relevant and what is their involvement? Determine drivers. What are the key business and technology trends that are driving the priority and planning goals set by the AI strategy? In terms of business trends, how will AI impact society or specific markets that are relevant to the organization? How are competitors and new entrants leveraging AI as a catalyst and differentiator for their business? In what areas is the organization vulnerable? What are the organization’s competitive moats? To strengthen these competitive moats, which unique data, expertise or capabilities should be utilized? In terms of technology trends, what is the current and future impact of AI? What are the key AI trends that are most relevant to the organization? Identify strategic risks. What is the organization’s vision for the responsible, ethical and secure use of AI? What are the main risks related to the use of AI in the organization? In addition to compliance, ethics, security and reputation, what other areas will the use of AI pose risks to? What are the main action plans to mitigate those risks? Who or which council is mandated to make decisions regarding the use of AI? From Strategy to Execution An AI strategy is about goal setting. Obviously, it, itself, is not enough to achieve value creation with AI. The AI strategy must also be executed. This is why any AI strategy must address value creation through an AI portfolio and adoption of the right AI operating model (see Figure 3). Figure 3: Relationships Among an AI Strategy, an AI Portfolio and an AI Operating Model The figure highlights value and adoption within the AI strategy, and how these relate to the AI portfolio and AI operating model, respectively. The portfolio helps leaders in value identification and realization, while adoption enables the planning of an AI operating model roadmap and maturity. The realization of AI initiatives takes place within the AI portfolio. The value section of the AI strategy sets overall priorities, ambition and investment levels for the portfolio. To make this more practical, the AI strategy may also incorporate some exemplary AI use cases. However, it is not the AI strategy itself but the AI portfolio which incorporates and maintains a full and current overview of AI initiatives and use cases. For the AI operating model, the adoption section of the AI strategy identifies the key capabilities (such as governance, literacy and data — see Figure 3) and the planning of their required maturity to enable timely execution on the strategic goals. This strategic planning sets the goals for a more detailed roadmap that is driving the maturing of the AI operating model. However, the more detailed roadmap is not a part of the AI strategy itself. Rather, based on the gap between current and required readiness of AI capabilities, the roadmap is an intrinsic part of the continuous development of the AI operating model. To develop the value and adoption sections of the AI strategy, AI leaders can use the following activities and questions as guidance (for further details, see the attached AI strategy template): Identify strategic value priorities for the AI portfolio. What are the levels of ambition with respect to applying AI? Will AI be used mostly to improve existing business, or to extend or even disrupt business? See Gartner AI Opportunity Radar: Set Your Enterprise’s AI Ambition for more information. How much funding should go to each of these ambition levels and in which business areas? How do these priorities relate to business objectives? For example, if the business objective is to cut costs, then AI use cases that impact costs take priority. If it is to improve customer engagement, then AI use cases that support customer engagement take priority. In other words, in which business areas are there the most important opportunities for AI to create real value? In each of these areas, what business goals is the use of AI related to? In which areas can AI be a catalyst for new or currently unaddressed business opportunities or challenges? Which KPIs will be impacted for which stakeholders? What are the key metrics for measuring the value that AI creates? What are some practical examples of AI use cases? Which business objectives and metrics are these examples related to? What is the art of the possible from within and outside the organization’s sector or industry? Set adoption planning goals for the AI operating model. Given the AI strategy and portfolio, what are the key capabilities required, and when do they need to be at which level of maturity? Together, these capabilities — governance, organization, literacy, engineering, data and technology — make up the AI operating model. In this section of the AI strategy, the planning goals for the development of the AI operating model and its capabilities should be summarized in terms of adoption phase or maturity level (see Become an AI-First Organization: 5 Critical AI Adoption Phases). In addition, for each planning goal, the AI strategy should identify KPIs to monitor progress. The setting of AI portfolio priorities and the AI operating model planning goals should be well coordinated. This is why these two are addressed together here. This strong coordination is needed because value creation through portfolio initiatives cannot happen without adequate maturity of the operating model. Not all AI initiatives require the same level of maturity in terms of capabilities. For example, some initiatives may utilize existing tools and require only limited capabilities for implementation whereas other initiatives may be highly complex in terms of technology or change management and require very advanced capabilities. However, such riskier initiatives and the required maturity of capabilities may still be prioritized to fulfill a more ambitious AI vision. In other words, a higher level of ambition, for example, using AI for disrupting an industry, must go hand in hand with a higher risk tolerance. There is a risk that an organization creates a roadmap for its AI operating model that is completely disconnected from the AI portfolio, creating a high probability that business value won’t be achieved. A balance is required between tangible value that business stakeholders can envision (via a portfolio of use cases) and the foundational capabilities required to deliver this portfolio. Frequent Alignment With Other Strategies An AI strategy should not be developed and then frozen. AI is evolving rapidly and any AI strategy needs to be frequently revisited. Moreover, the AI strategy is not to be seen or executed in isolation. In practice, it should be fully aligned with the business strategy and with strategies for highly complementary technology areas such as IT and D&A. Both business and technology are frequently disrupted by new developments, positive or negative, triggering the necessity of frequent strategic recalibrations. Figure 4 depicts this continuous strategic alignment process. Figure 4: Continuous Mutual Alignment Between the AI Strategy, the Business Strategy and Other Neighboring Strategies This figure highlights the alignment section within the AI strategy, and the alignment between the AI strategy and other strategies, namely, business strategy, IT strategy, D&A strategy, R&D strategy, or possibly other strategies. Alignment between AI and other strategies should be bidirectional. The alignment between the AI strategy and other strategies, in particular the business strategy, should be bidirectional. After all, AI is not just a technology to improve existing business only. In addition, it is increasingly applied to catalyze new business opportunities or disrupt existing business models and even entire markets. Either way, any changes in the business strategy, perhaps triggered by new competitive activity or changing market conditions, should be reflected in an updated AI strategy. This should in turn result in a reprioritized AI portfolio and updated planning goals for the AI operating model. But AI is too powerful to be used as just a reactive enabler of business needs. So the alignment should also happen the other way around. If new AI trends materialize, which open the door to new business opportunities, then this should be reflected in an updated business strategy, especially in the case of higher levels of AI ambition, given their more profound impact on the organization. Business shapes AI. AI shapes business. Playing it safe has never been riskier, as competitors or new entrants may overtake incumbents, leveraging the growing power of AI. AI’s growing force is a catalyst for new business opportunities as well as a potential disruptor of existing business models and markets. Business continuity and competitiveness depend on the active alignment and frequent adaptation of both AI and business strategies. To develop the alignment section of the AI strategy, AI leaders can use the following activities and questions as guidance (for further details, see the attached AI strategy template): Frequently align or realign the AI strategy with the business strategy. Are there market circumstances, competitor activities, business challenges and opportunities, changes in customer demand or sentiment, or other reasons to recalibrate or even overhaul the business strategy, and how does that impact the AI strategy? Conversely, are there advances or trends in AI that are significant enough to have the business strategy updated? Frequently align or realign the AI strategy with other adjacent strategies, most notably the digital/IT strategy and the D&A strategy (see Executive Essentials: Create and Execute an IT Strategy That Contributes to Enterprise Success and Creating a Modern, Actionable Data and Analytics Strategy That Delivers Business Outcomes). Other possible strategies include R&D/innovation strategies or security and risk management strategies. This alignment should clarify how these business-enabling strategies work together, how their scopes overlap, where shared capabilities can be leveraged and where synergies can be achieved. The AI strategy is highly complementary to at least the D&A strategy and the IT strategy, and vice versa in the following ways: D&A capabilities in data management, D&A governance, analytics, organization, roles and data literacy can be leveraged for AI. But only after they have been adapted and extended are they ready for use in AI initiatives. For example, D&A’s data management and governance capabilities can be instrumental in providing AI-ready data. Conversely, for example, D&A can greatly benefit from using AI to generate code or scripts for development and testing, to enable data fabrics or augmented data integration, to generate synthetic data, or to enable user interaction in natural language. IT can offer software engineering practices that may be leveraged for AI engineering. Many AI use cases require embedding or integration with existing (enterprise) applications such as ERP and CRM systems, or apps in various customer interaction channels, for example. Conversely, for instance, IT can greatly benefit from using AI to generate code or scripts for development and testing, to enable user interaction in natural language or to support IT operations. In the case of collaboration between D&A, IT and AI teams, practices and ways of working should reflect the need for flexibility and experimentation that are often more prominent in AI initiatives. Moreover, this collaboration should foster the streamlining of a shared “pipeline” of artifacts ranging from data to models to applications, thus shortening the time to value. In terms of AI strategy development and maintenance, the process of frequent strategic alignment requires that this should be a periodic item on the agenda of C-level meetings to monitor strategic execution and initiate realignment or other corrective actions when deemed necessary. AI leaders should then initiate meetings with all relevant stakeholders to id