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Apr 10, 2025

11 minutes read

Enterprise AI strategy: How to choose and build a winning template

Learn how to develop an enterprise AI strategy with our step-by-step template and choose the best AI approach for your company.

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_Updated: October 24, 2025_

AI is poised to become the engine driving innovation, efficiency, and competitive advantage across industries. Yet while most tech leaders recognize the potential of artificial intelligence, many are struggling to have a clear AI strategy to harness it effectively.  The fast pace of innovation in this sphere—not to mention the rush of generative AI into the market—is making AI strategy harder than ever.

Developing enterprise AI requires a huge investment in terms of time, capital, and resources. Without a strategic roadmap, the complexity and scope of  AI investments can end up as little more than experiments lacking any real impact. This is why having an AI strategy—a clear roadmap with goals and resourcing and, importantly, a reason why—is essential.

Learn how human resources platform, BambooHR, used Cohere as part of their AI growth strategy.

##### What is an enterprise AI strategy?

In simple terms, an AI strategy is a blueprint for how an organization plans to develop, deploy, and scale AI to create value. An AI strategy is more than just using AI software: It’s about having a structured approach to integrating enterprise AI in a way that aligns with broader business goals and can scale with growth.

Imagine an organization investing in AI-powered virtual assistants without considering how they fit into its customer service experience. Or a hospital implementing AI diagnostics without first gaining the trust of doctors who need to understand and use the technology.

###### The basics of AI strategy

An AI strategy helps ensure that AI is used deliberately and effectively. A strong AI strategy includes key components such as:

• Clear objectives: AI should be solving a real problem or improving efficiency, not just serving as a novelty.
• Data readiness: AI thrives on quality data. Organizations need a plan for collecting, organizing, and maintaining reliable data sources.
• Technology and applications: AI isn’t one-size-fits-all. A strategy should outline whether to build AI in-house, use third-party applications, or partner with AI vendors. And when to go with a hybrid AI strategy.
• People and skills: AI isn’t just about machines; at its core, it’s about people. Success depends on having access to the right expertise either in-house or through partnerships, and upskilling employees to be able to use enterprise AI effectively and confidently.
• Governance and ethics: AI must be transparent, fair, and aligned with legal and ethical standards to build trust and avoid unintended consequences, such as important business decisions being made based on biased or incorrect data.
• Scalability and continuous learning: AI isn’t a “set it and forget it” technology. It needs resources allocated to regular monitoring and updating it, and maintaining or making necessary adaptations, to remain effective.

###### The increasing need for flexible AI strategies

The influx of generative AI solutions has proven challenging for those with an AI strategy set in stone. With new opportunities and capabilities, GenAI is changing everything we knew about AI strategy.

Generative AI has changed the world of security, not just in terms of cybersecurity but also in how infrastructure is built and maintained. While legacy IT infrastructure has its own ready-made tools, AI is still innovating, which puts any security tools at risk of quickly becoming obsolete. The attack surface has also increased, especially in RAG architectures. You need to not only secure the data, but also the model and the application—and that requires careful strategic thinking.

As AI adoption spreads, organizations need to become more flexible in their approaches. This technology changes fast, and it demands that enterprise leaders change at the same pace or be left at risk—or left behind. Generative AI needs vast amounts of data in multimodal formats, which in turn brings increased risk of bias and hallucination. Equally, agentic AI workflows and RAG bring a renewed need for security and privacy in AI platforms; each new agent introduced means a new connection for bad actors to exploit.

AI strategy calls for careful consideration of modes of deployment—on-premises, in the cloud, a hybrid approach, or through a partner?—as well as how the AI platform itself is built and maintained. Flexibility to deal with the rapid nature of change in the market must be built-in to allow future-proofing of any strategy.

###### Who should own the enterprise AI strategy?

Ownership of AI strategy typically rests with a cross-functional leadership team, often spearheaded by a Chief AI Officer or Chief Digital Officer, who collaborates closely with department heads from IT, operations, legal, and other core business units. This team establishes:

• Governance frameworks across departments
• Aligns AI initiatives with organizational business goals
• Ensures accountability for ethical and regulatory compliance

Clear mandates and resource allocation from the c-suite are critical to empower this group to drive cohesive strategy execution. Without executive sponsorship, enterprise-wide AI initiatives could get uneven adoption rates and create an imbalance between teams.

Implementation should follow a collaborative structure, where strategic vision flows top-down from c-suite executives to middle management. Those who lead business functions then translate goals into actionable roadmaps for staff. Feedback should be solicited on an ongoing basis across all teams to not only maintain alignment but also tie back to evolving business needs. Adoption and...