Navigating the path from artificial intelligence potential to business value The artificial intelligence field, nearly seven decades old, has experienced numerous cycles of excitement and disappointment. Now we find ourselves in another summer season, propelled by the emergence of generative AI. The past three years have been remarkable, with barely a week passing without major technical advances, extraordinary investment rounds, or sweeping predictions about solving humanity’s greatest challenges.
Yet conversations with board members and senior executives reveal a different story. When discussing what these developments mean for their enterprises, responses frequently reflect uncertainty. Leaders generally recognize both opportunities and risks. Many have listened to consultants and analysts explaining the transformative potential of generative AI, often accompanied by dire warnings that inaction means being left behind by competitors.
But what concrete steps should they take? Should organizations act aggressively, investing heavily in AI to proactively shape their future? Or is it wiser to wait until business benefits become more clearly defined and proven?
The emerging field of generative AI presents organizations with substantial uncertainties requiring careful navigation. These uncertainties span technical, economic, competitive, regulatory, ethical, and capability-related dimensions. This situation isn’t unprecedented in strategy development. Business leaders shouldn’t attempt to eliminate uncertainty entirely, an impossible task, but rather make thoughtful, informed decisions about investment priorities and approaches. Addressing these strategic decisions benefits from a structured framework. The AI Navigator framework connects business benefit arenas with the various AI model types necessary for successful execution, answering two fundamental questions: Where should we play? And how do we capture value?
Where Can GenAI Create Value?
Three distinct application areas exist where generative AI can deliver significant organizational value: individual effectiveness, organizational effectiveness, and core innovation. How organizations build their AI portfolios depends heavily on their current level of AI maturity.
Individual Effectiveness: Augmenting Employee Capabilities
Individual effectiveness means deploying generative AI to boost productivity and enhance employee capabilities. Impact occurs at the task level, automating frequently repetitive, clearly defined activities. Examples include summarizing information, generating marketing copy options, or producing software code components. GenAI tools supporting employee effectiveness encompass powerful capabilities like copilots, search engines built on large language models, and intelligent assistants.
Business benefits can prove substantial if organizations properly measure gains and capture effectiveness improvements through increased productivity or redirection to different work tasks. At Unilever, customer service employees regularly use GenAI tools to draft responses to common customer inquiries. Through partnership with Accenture, Unilever deployed over 500 GenAI applications, including Accenture’s GenWizard platform. One application, an AI-powered customer connectivity model, executes more than 13 billion daily computations. During a pilot with Walmart Mexico, this approach achieved product availability reaching 98% with approximately 12% sales uplift, driven by GenAI-enhanced forecasting and replenishment capabilities.
Individual effectiveness initiatives typically create localized impact, sometimes proving difficult to scale beyond knowledge sharing between employees. Organizations at early stages in their AI maturity should generally start here as a rule.
Organizational Effectiveness: Redesigning Workflows and Processes
Organizational effectiveness represents a second-order impact extending beyond individual tasks. Here, collections of tasks become automated and embedded into the organization through redesigned workflows, processes, and even entire functions like customer service. Business value can be substantial, but capturing benefits requires high levels of organizational adaptation.
Success demands redesigning technology-enabled processes across organizational silos, securely embedding them into existing enterprise systems, and most critically, ensuring employee and team adoption of AI-driven workflows and new working methods. Organizations must consider scalability—can the new process be replicated across geographies, or can learnings extend AI enablement to other processes or functions within the organization? Over time, such implementations can profoundly transform how an organization operates.
Siemens provides an instructive example, having redesigned their entire industrial automation software development process by integrating AI coding assistants. Engineers complete programming tasks 30% faster, with junior developers showing even higher productivity gains. A broader study conducted by Microsoft and Accenture found a 26% average task completion gain, particularly among junior teams, without workforce reductions, reinforcing the Siemens findings.
This represents a potentially significant value source for organizations, but demands top-level commitment to execution. We’re now in work redesign and change management territory, traditionally complex endeavors within large organizations.
Changing How the Game Is Played
Core innovation represents an even higher order of GenAI enablement. While the first two focus on optimizing tasks and workflows within existing operational boundaries, core innovation changes fundamental rules of engagement. It enables fundamental changes to core value chain components, organizational structures, value propositions, or even business models themselves.
Business benefits can include step changes in cost structure, revenue uplift, or GenAI-driven autonomous decision-making. Core innovation is more complex and risky, requiring enterprise-wide strategic initiative treatment for success. Australia-based Canva illustrates this approach, innovating beyond being simply a graphic design platform to offer Magic Studio, a GenAI-powered creative suite, in 2023. The business model shifted from template-based design tools to GenAI-assisted content creation, expanding market reach to millions of non-designer users with significant increases in content creation volume.
Here we enter high-risk, potentially high-reward territory best suited for disruptors and organizations with high AI maturity levels.
Building a Portfolio Across Benefit Areas
Are these benefit areas mutually exclusive? No. Over time, executives can build portfolios of AI initiatives spanning multiple benefit areas. However, organizations progress through learning curves in terms of AI maturity. The principle remains: walk before you run!
Which Technologies Enable Value Capture?
Today’s market offers a plethora of GenAI models covering wide application arrays. These range from well-known text and content creation tools like OpenAI’s ChatGPT, Anthropic’s Claude, or Google’s Gemini, to image and video creation tools such as DALL·E 3, Midjourney, or Synthesia, to code assistance platforms like GitHub Copilot, Amazon’s CodeWhisperer, or DeepSeek Coder, to audio and music generation tools including ElevenLabs, Suno, or Udio, and many others covering research, customer service, data analytics, and more.
The supply landscape is enormous, and getting lost is easy. To navigate this landscape successfully, think in terms of three broad AI model application categories:
Off-the-Shelf Solutions
General-purpose AI models are off-the-shelf solutions, such as many examples mentioned above. They’re designed to handle well-defined tasks with minimal customization beyond basic settings or prompting. General-purpose models can generate immediate value in areas like document classification, customer support chatbots, or basic data processing.
These models are widely available at reasonable operating costs and easy to implement, without requiring deep integration into a company’s unique business processes or systems. General-purpose models often form the backbone for targeting benefits at individual and organizational effectiveness levels.
Domain-Specific Solutions
Specialized AI models are specifically trained and fine-tuned for industry verticals, functions, or business domains, making them more powerful and relevant for narrower applications such as regulatory compliance in finance, medical diagnostics in healthcare, or supply chain optimization. Unlike general-purpose models, they require domain expertise and contextual adaptation for effectiveness, resulting in higher implementation costs.
Specialized AI models bridge the gap between automation and business transformation at the organizational effectiveness level. They provide deeper insights and process improvements tailored to organizational needs and specificities. For example, OpenAI’s Harvey is a specialized model for the legal profession, automating tasks like drafting, research, and contract analysis to streamline and enhance legal workflow efficiency.
Custom and Experimental Solutions
Pioneering AI models are typically custom-built or highly experimental, often leveraging advanced AI techniques like reinforcement learning, generative design, reasoning-based AI, or agentic applications designed to autonomously make decisions and act without human intervention. These are complex and costly to implement, requiring significant investment in research and development, data infrastructure, and talent.
Pioneering models target groundbreaking applications with novel solutions for domain-specific complex problems, such as drug discovery or autonomous decision-making in financial investment. Pioneering AI forms the foundation for companies aiming to push competitive advantage boundaries by creating new operational methods or business models.
For example, Ocado, the UK-based online grocer, developed Kinetic Storage, a pioneering AI model for the group’s automated warehouses. Using generative optimization algorithms to dynamically reconfigure the storage grid based on predicted order patterns, the system has reduced retrieval times by over 30% during peak periods.
The biotech company Moderna is building a pioneering AI model to design novel mRNA sequences tailored to specific therapeutic needs. These AI-powered tools are engineered to optimize key properties such as protein expression, molecular stability, and immune system activation, substantially accelerating treatment development for cancer and rare diseases. Notably, Moderna has partnered with IBM to apply advanced models like MoLFormer, aiming to improve mRNA-based medicine safety and efficacy through AI-driven design. Early results show promise in protein stability optimization, potentially shortening development timelines by months.
Individual effectiveness, organizational effectiveness, and core innovation represent key areas where senior executives should focus on the value they want to extract from AI implementations in their organizations. General-purpose, specialized, and pioneering AI models are the technology-enabled routes to value capture. Both frameworks are important for executives thinking through their strategy for AI. But as with any strategy, the critical next step is making choices by combining the right AI model with the right business benefit arena.
Where to Play and How to Win
By taking a holistic perspective of business objectives for AI, together with the technology-enabled models that can help realize these objectives, executives can frame strategic choices that best suit the impact they’re seeking for their organizations. The AI Navigator framework maps business value arenas with AI model implementation types, yielding seven choice archetypes spanning quick wins to bold bets.
Automating Routine Tasks
At the entry level, Efficiency Boost represents the intersection of individual effectiveness with general-purpose AI. In this model, humans oversee AI-assisted automation, with the primary business objective being automation of repetitive, well-defined tasks to enhance personal productivity. These solutions rely on pre-trained models for document processing, chatbots, or email filtering, ideal for businesses seeking fast, low-integration wins.
Benefits include reduced workload, fewer errors, and freeing up time for higher-value work. A typical use case appears in insurance, where claims processors use AI to sort and categorize documents, allowing staff to focus on more complex cases.
Enhancing Expert Decisions
When deeper domain knowledge is needed, Smart Augmentation pairs individual effectiveness with specialized AI. Here, humans collaborate closely with AI, aiming to enhance expert performance without redesigning workflows. Tools like AI copilots in legal or healthcare settings exemplify this model.
For example, radiologists use AI to highlight abnormalities in medical images, improving diagnostic accuracy while retaining human judgment. Benefits include improved decision speed and reduced cognitive load, with AI amplifying, rather than replacing, expert insight.
Scaling Automation Across Functions
Scaling up to organizational workflows, Process Accelerator sits at the intersection of organizational effectiveness and general-purpose AI. In this approach, humans supervise AI-driven processes to automate routine workflows across departments, often through tools like robotic process automation and intelligent document processing.
This strategy works well for firms aiming to streamline without deep structural change. For instance, a telecom provider automated ticket routing and account updates, eliminating bottlenecks and improving internal response times. Business benefits include enhanced throughput, reduced manual effort, and more consistent execution.
Redesigning Cross-Silo Processes
More mature organizations might pursue AI-Optimized Operations, combining organizational effectiveness with specialized AI. Here, humans guide AI-driven decision-making across silos and functions. The objective is to redesign processes using AI that’s deeply integrated into operations, such as supply chain optimization or regulatory compliance.
For instance, in the retail industry, a global apparel brand uses AI to align inventory, pricing, and demand forecasting across regions. This model breaks down silos, improves allocation, and enables real-time decisions, ultimately driving scalable efficiency gains.
Reinventing How Operations Work
For businesses aiming at deeper transformation, Operations AI Step Change emerges when organizational effectiveness meets pioneering AI. In this mode, humans oversee AI-led innovation, and the goal is to fundamentally reinvent how the organization operates. Techniques include AI-led forecasting, autonomous planning, and research and development support.
Consider an automotive manufacturer using AI to predict market trends, simulate designs, and optimize factory layouts. This strategy improves agility, accelerates innovation cycles, and builds long-term advantage by reimagining how the enterprise functions.
Enabling New Offerings
For organizations aiming to accelerate innovation cycles, AI-Powered Innovation blends core innovation with specialized AI. Humans set exploration boundaries, and the objective is to develop new business models or offerings without a full operational overhaul. Companies apply AI in drug discovery, product development, or design automation.
In consumer electronics, for example, one firm analyzes customer feedback using AI to generate and test new product concepts, reducing research and development time, accelerating product cycles, and enabling responsive design with lower overhead.
Driving Frontier Innovation
Finally, for disruptors or organizations wanting to extend innovation boundaries in their industry, Moonshot AI marks the boldest archetype, combining core innovation with pioneering AI models. In this approach, AI often operates with minimal human intervention, driving frontier innovation with agentic AI or autonomous AI applications. The aim is to create new value propositions or markets, often through autonomous discovery or decision-making.
In biotech, for instance, startups are using generative AI to design novel proteins, developing treatments unimaginable with traditional research and development. The benefits include redefined markets, new competitive moats, and the transformation of entire industries.
Using the AI Navigator to Sharpen Your Strategy
Business leaders must focus attention on where AI can meaningfully advance strategic ambitions, given their current maturity, risk appetite, and investment horizon. This starts with a strategic choice about which outcomes to pursue; only then should you consider which technologies to adopt.
The AI Navigator doesn’t propose a single path forward, rather, it reveals the full spectrum of what’s possible. It helps shift conversations from vague ambition to focused execution: Where can AI accelerate your current goals? Where might it reshape your longer-term operations? And where could it eventually reinvent your business model entirely?
The AI Navigator framework invites reflection, alignment, and dialogue among executive teams. Used well, it can support sharper decision-making and help organizations craft a strategy for AI that is as ambitious as it is grounded.
Your Strategy for AI Will Be Iterative
Five recommendations for senior leaders:
Deepen the Dialogue Within Your Team
The AI Navigator is intended to inspire focused, collaborative, and constructive conversations between business and technology leaders. It will sharpen your strategy for AI and align leaders around actionable directions.
Define the Scope of Your Ambitions
The AI Navigator presents pathways with different timeframes, complexity levels, risk profiles, and transformational opportunities. Your current level of AI maturity will determine where you can start your journey, but exploration of future new sources of business value should guide you as you build your strategic portfolio.
Think Beyond Technology
AI alone will not create value. To unlock the full organizational benefits of these tools, it’s important to take a people-centric approach; it’s a true transformation. It requires gaining detailed understanding of workflow redesign requirements, appreciating cultural implications, adapting organizational structures, and measuring business outcomes.
Build Your Team’s Tech Expertise
AI is a complex field, and there will invariably be gaps in capabilities. Think early about building the right ecosystem to maximize your chances of success.
Revisit Your Strategy Regularly
Your strategy for AI will be iterative. The field of AI changes extremely rapidly, and new opportunities will appear. By revisiting the AI Navigator regularly as you progress, you will foster organizational learning across your teams.
The challenge facing today’s business leaders isn’t whether AI will transform industries, it’s determining how and where their specific organizations can harness this transformation effectively. The gap between technological possibility and business reality remains wide for many companies, not due to lack of tools or talent, but because of insufficient strategic clarity.
The AI Navigator framework provides a structured approach to bridge this gap. By explicitly connecting business benefit arenas with appropriate AI model types, it moves organizations beyond the paralysis of overwhelming choice toward purposeful action. The seven strategic archetypes—from Efficiency Boost to Moonshot AI—aren’t prescriptive steps but rather a menu of options, each suited to different organizational contexts, capabilities, and ambitions.
Success in AI strategy requires balancing ambition with pragmatism. Organizations must honestly assess their current AI maturity while maintaining vision for where they want to go. They must recognize that technology deployment alone creates no value, organizational transformation, cultural adaptation, and capability building are equally critical. And they must accept that strategy in this space will necessarily be iterative, evolving as both the technology and their own capabilities mature.
The most successful organizations won’t be those that move fastest or invest most heavily. They’ll be those that make the most thoughtful choices about where AI can create meaningful competitive advantage, then execute with discipline and learning agility. The AI Navigator provides a framework for making those choices deliberately rather than reactively, grounded in business reality rather than technological hype.
The question for leaders isn’t about keeping pace with AI development, it’s about determining where and how their organizations can realistically deploy these powerful tools to achieve strategic objectives. With a clear framework for thinking through these choices, leaders can move from uncertainty to action, from experimentation to execution, and from AI strategy to strategy for AI.
