Will Your Gen AI Strategy Shape Your Future or Derail It?

As companies move from generative AI (gen AI) pilots to scaled efforts with measurable outcomes, many incumbents struggle to turn ambition into impact. Despite rising adoption, most gen AI initiatives fall short on ROI, highlighting the complexity and high-stakes nature of gen AI strategy.

Leaders face critical questions: Where should they focus gen AI to unlock the greatest strategic impact? Should they prioritize proprietary systems for long-term advantage or proven off-the-shelf tools for speed? How can they balance rapid deployment with stability, trust, and compliance? And how can they ensure their gen AI strategy aligns with execution levers to drive transformation with measurable impact?

These are not just technical issues, they are strategic choices that will determine which companies lead and which fall behind. The journey begins with two fundamental questions: What should our gen AI strategy be? and How can we execute it effectively?

Answering these requires more than experimentation. It demands clear prioritization and disciplined execution. Prioritization means navigating trade-offs across four dimensions: aligning use cases with strategic goals, choosing between build versus buy, calibrating risk exposure, and balancing speed with operational stability. Execution, in turn, depends on strong data foundations, scalable infrastructure, responsible governance, organizational readiness, and targeted capability building.

In 2024, we analyzed extensive archival sources, media coverage, and online reports to build a dataset of 100 gen AI use cases across industries such as logistics, finance, healthcare, energy, and retail. This analysis revealed key patterns of adoption, strategic positioning, deployment decisions, and the organizational models used to support execution.

We used these insights to develop a practical framework to help organizations shape and execute strategies that unlock real value, refined through executive classroom discussions and our collaboration with established companies on real-world gen AI implementation.

Shaping a Gen AI Strategy

Leaders at incumbent firms now face a new kind of portfolio challenge. Unlike prior digital transformations typically focused on IT modernization or process digitization, gen AI introduces a more complex, multidimensional puzzle. Effective prioritization requires balancing the following factors: expected benefits with cost implications and risk tolerance with desired pace of implementation.

Expected benefits and cost considerations

Align AI use cases with strategic goals. Smart organizations are using gen AI to solve high-impact problems with clear returns. Carrefour cuts spoilage and boosts margins through smarter inventory. Novartis speeds up clinical trial design. Zurich Insurance improves compliance and trust with plain-language underwriting, all delivering measurable gains in speed and productivity. Creative firms like Netflix and Warner Bros Discovery use gen AI to localize content, summarize scripts, and explore new ideas, aiming to accelerate production and innovation.

Whether targeting efficiency or innovation, the key is to define the value sought and choose use cases that directly support those goals.

You will have to build or buy AI solutions. Gen AI adoption often requires major investment. JPMorganChase, with more than 1500 AI experts, builds proprietary models for fraud, compliance, and customer service, betting on long-term gains in control and competitive edge. Smaller or cost-sensitive firms tend to buy and integrate off-the-shelf tools. Klarna uses an OpenAI-powered assistant for most customer chats. Enel combines in-house gen AI with third-party APIs, while Maersk mixes commercial tools for port modelling with custom logistics systems.

The key question isn’t just how much to invest, but where. Competitive advantage often lies in building proprietary solutions around unique data contexts, while speed and efficiency can be gained by buying external tools for more commoditized capabilities like language generation.

Risk tolerance and implementation speed

Manage risk: Firms must align their gen AI risk posture with strategic goals, compliance needs, and risk tolerance. For instance, JPMorgan launched its secure ChatGPT version, LLM Suite, almost a year after it restricted employees from using ChatGPT due to data privacy concerns. The bank prioritized protecting proprietary data while enabling adoption across key divisions like asset and wealth management. In contrast, Duolingo used GPT-4 for features like “Explain My Answer” and “Roleplay,” gaining an edge in edtech, an industry with arguably lower risk management concerns than finance or healthcare.

The right level of risk varies by industry, use case, and regulatory environment. What works for a tech-forward social media company may be unacceptable for a highly regulated financial institution or pharmaceutical company.

Balance speed and stability: The speed of gen AI rollouts can offer first-mover advantages but also carry risks. For instance, General Motors takes a slower approach, testing gen AI for software, training, and legal tasks, with a focus on security, alignment, and minimizing business disruption before full deployment. In contrast, Snap Inc. rapidly deploys gen AI features like AI Lenses, My AI, and AI Snaps to enhance the Snapchat experience. The social media industry’s focus on rapid feature rollouts enabled Snap to adopt these tools swiftly, often with lighter concerns about disrupting operational stability.

Many firms opt for a hybrid path. Adobe built Firefly in-house and released it gradually, starting with beta users before full Creative Cloud integration.

Four Strategic Archetypes

The strategic trade-offs above influence how organizations set gen AI priorities. To navigate them, we outline four strategic archetypes, each representing a distinct approach. These archetypes help companies align their gen AI strategy with overall business goals by clarifying their stance on key trade-offs.

• Bold Innovators: These firms seek to reshape their markets with gen AI, embracing risk to stand out. Heidelberg Materials, for example, is using gen AI to simulate carbon-reducing chemistry for sustainable cement, an ambitious play for IP strength and climate leadership.
• Disciplined Integrators: These firms focus on trust, control, and compliance, ensuring innovation supports operational stability. Roche, for instance, uses gen AI in clinical trial monitoring under tight regulatory oversight, carefully protecting patient data.
• Fast Followers: These firms target quick wins, using gen AI for low-cost, high-impact solutions. CarMax, for example, deployed a gen AI engine to summarize used car reviews, speeding up e-commerce engagement without major infrastructure changes.
• Strategic Builders: These firms take a long-term view, developing gen AI to own IP and drive sustained advantage. Allianz exemplifies this approach, building its own gen AI stack for claims, fraud, and underwriting across global markets to strengthen its future position.

An organization’s gen AI archetype is often fluid, evolving with changes in capability maturity, risk appetite, and business priorities. Some transitions are strategic, others reactive. Zurich Insurance, for example, began as a Disciplined Integrator by applying gen AI to underwriting under tight governance, later evolving into a Fast Follower through chatbot deployment in claims processing. Novartis shifted from Fast Follower to Strategic Builder by moving from off-the-shelf tools to proprietary models in clinical trials. Carrefour advanced from using gen AI to generate product descriptions to piloting autonomous stores, exemplifying a Bold Innovator. These shifts underscore the importance of aligning archetypes with evolving organizational needs rather than chasing a “superior” model.

Moreover, few organizations conform to a single archetype. Different functions within the same firm may adopt distinct approaches. For example, an R&D unit might operate as a Bold Innovator, while a customer-facing team functions as a Disciplined Integrator. The key is to assess your position across benefit, cost, risk, and speed, and choose a path aligned with strategic goals.

Executing with Discipline

Choosing the right archetype is only half the battle. Executing it demands a disciplined approach grounded in robust data foundations, scalable technology architecture, responsible governance, organizational readiness, and targeted capability building. We recommend the following five execution pillars.

1. Data readiness

High-performing gen AI needs high-quality, well-integrated data. Many firms underestimate the work involved in cleaning and aligning data across systems, leading to unreliable AI outputs. American Express tackled this by overhauling its data infrastructure and unifying data from transactions, customer service, and fraud monitoring, ensuring consistency, accuracy, and privacy. Roche, in healthcare, created a cross-functional governance model to manage diverse data types under GDPR, supporting gen AI tools in diagnostics and research. Data readiness also demands ethical oversight: sourcing, consent, anonymization, and explainability policies are essential, particularly in regulated or consumer-facing sectors.

2. Technology architecture

Scalable gen AI requires flexible, high-performing architecture. Companies must choose between cloud, on-premises, or hybrid setups, ensure interoperability, and build modular systems that evolve with tech and regulations. Netflix uses a tailored cloud system that powers global AI-driven recommendations and integrates microservices for seamless feature rollout. CarMax partnered with Microsoft Azure to create a cloud-native gen AI engine that summarizes customer reviews, enabling quick e-commerce integration with minimal infrastructure changes. CVS Health blends cloud and on-prem systems to balance speed and control. Its claims review gen AI tools run in secure, HIPAA-compliant modular environments. Smart architecture fits current IT, supports fast deployment, and scales with business growth.

3. Governance

Gen AI governance is essential as models grow more complex. Companies must manage compliance, risk, and ethics with frameworks for decision rights, model monitoring, and accountability. Microsoft implemented a Responsible AI Standard, requiring teams to document use cases, explain models, and run fairness checks, supported by its Office of Responsible AI. Salesforce formed an AI Ethics team to guide product development through risk assessments. Even smaller firms like Hugging Face contribute, offering tools like “Model Cards” and “Data Statements” for transparency. Most organizations begin with audits, advisory boards, and bias testing, then expand governance as usage grows.

4. Organizational readiness

Cultural and structural inertia can slow gen AI adoption, even with strong tools and skills. Organizational readiness means aligning processes, metrics, and leadership to support transformation. Airbnb created cross-functional AI “tiger teams” to rapidly identify and implement use cases, bypassing traditional bottlenecks. ING embedded AI into its agile squad model, helping teams move smoothly from idea to production. Change management is key. Leaders must explain why gen AI is being used, how it impacts employees, and what support is available. Resistance is natural but manageable when communication is clear and inclusive.

5. Capability building

Gen AI enhances human expertise, making AI literacy essential across all roles. PwC is investing over $1 billion to upskill consultants in using gen AI for tasks like document analysis and risk modelling, making it a core skill. Unilever trains marketing, finance, and HR teams via prompt engineering workshops and sandbox environments where employees can safely experiment with AI-powered tools. The most effective upskilling programs blend education with empowerment: they teach people how to work with gen AI, while also redesigning roles and workflows so gen AI becomes part of everyday business processes.

Linking Archetypes to Execution Pillars

Gen AI isn’t a simple plug-and-play solution. To unlock value, leaders must choose where to focus and how to execute. Our framework combines the four strategic archetypes with the five execution pillars to guide this process and ensure lasting impact.

Each archetype (Bold Innovators, Disciplined Integrators, Fast Followers, and Strategic Builders) needs a tailored setup across the five pillars: Data Readiness, Technology Architecture, Governance, Organizational Readiness, and Capability Building. These pillars work as a system and aligning them with each archetype’s goals ensures strategic fit. We detail each approach below.

Bold Innovators

These organizations chase breakthroughs, prioritizing high benefits and accepting higher risks for first-mover advantage. They should leverage a hybrid of proprietary and third-party models to power cutting-edge applications.

• Data readiness: Prioritize rapid data integration from varied sources while ensuring ethical sourcing and consent. Key for use cases like supply chain optimization.
• Technology architecture: Build flexible, cloud-based systems combining APIs and custom models to support quick prototyping and scaling.
• Governance: Use lightweight governance with real-time bias checks and ethical reviews to maintain trust without stifling innovation.
• Organizational readiness: Create cross-functional innovation labs that sidestep hierarchy to accelerate experimentation.
• Capability building: Develop advanced skills like prompt engineering and model tuning through hands-on workshops and sandbox environments.

Disciplined Integrators

These firms prioritize control and compliance, often in regulated industries, with low risk tolerance and slower rollouts. Partnering with niche vendors for custom models ensures robust governance.

• Data readiness: Create structured, privacy-compliant datasets with strong consent and explainability, essential for regulated industries like healthcare and finance.
• Technology architecture: Use hybrid cloud-on-prem setups that emphasize security and interoperability with current systems.
• Governance: Implement robust frameworks with audits, fairness checks, and ethics boards to manage risk and ensure compliance.
• Organizational readiness: Integrate AI into existing workflows with clear processes and stakeholder alignment to preserve operational stability.
• Capability building: Combine technical training with governance-focused upskilling tailored to roles for disciplined, compliant execution.

Fast Followers

These organizations focus on quick wins with moderate budgets, seeking rapid execution using off-the-shelf solutions. Gen AI APIs for tasks like summarization or customer support deliver measurable impact.

• Data readiness: Focus on quick data cleaning and use readily available datasets to enable fast rollout of API-based tools.
• Technology architecture: Rely on cloud APIs and pre-built platforms to cut infrastructure costs and speed up integration.
• Governance: Use vendor-provided governance templates with basic bias checks and transparency measures to build trust without delays.
• Organizational readiness: Restructure teams around agile workflows to embed gen AI into key areas like customer service with minimal disruption.
• Capability building: Offer hands-on training in API use and basic prompt design to help teams quickly leverage off-the-shelf tools.

Strategic Builders

These firms play the long game, prioritizing innovation, IP ownership, and customization. Investing in foundation models with secure infrastructure ensures competitive differentiation.

• Data readiness: Develop centralized, high-quality data lakes with strong anonymization and ethical sourcing to fuel proprietary model creation.
• Technology architecture: Design scalable, custom architectures, typically on secure cloud platforms, to support tailored models and long-term adaptability.
• Governance: Implement advanced systems with automated monitoring, explainability, and IP protection to secure strategic AI investments.
• Organizational readiness: Integrate gen AI deeply by reworking processes and KPIs to embed AI-driven innovation across the business.
• Capability building: Commit to broad upskilling, from data science to AI strategy, to sustain a proprietary, enterprise-grade AI ecosystem.

. . .

Will your gen AI strategy shape your future or derail it? Success depends on aligning clear priorities with disciplined execution. By choosing an archetype that matches their goals, risk appetite, and resources, leaders can align the five pillars to build a focused roadmap. Whether pushing boundaries as a Bold Innovator or scaling proprietary systems as a Strategic Builder, each pillar must support the chosen path. With the right framework, gen AI becomes a tool for transformation, driving real impact and measurable results.