Your enterprise AI strategy isn’t about technology

Businesses are quickly realising value from their AI investments. The ServiceNow Enterprise AI Maturity Index 2025 found that globally, more than two-thirds (67%) of surveyed leaders report increased gross margin in their organisation as a result of using AI.

As organisations roll out AI initiatives, some may fall into the trap of focusing solely on the technology rather than the business, people, and context that surround it.

AI doesn’t work in isolation. True AI transformation is built on a foundation of best-fit tooling, skills, cultural change, and a renewed approach to governance—and requires a strong enterprise AI strategy. Let’s unpack what that looks like. unpack what that looks like.

Aligning AI with outcomes

Organisations need the right technology layer in place to drive successful AI implementation. This includes clean, high-quality data and a well-optimised application portfolio with minimal technical debt.

Technology isn’t everything, however. When organisations rush to invest in a new AI project without considering how it relates to their desired business outcomes, it can lead to unfocused AI investment that lacks a clear purpose.

To overcome this, it’s important to ask, “What problem are we trying to solve?” Then, you can map AI use cases to tangible business outcomes. A strong AI strategy focuses on implementing the best solution for your business needs, not retrofitting an outcome to the latest tool.

I’ve seen enterprises make this mistake with many breakthrough technologies. One team approached ServiceNow thinking it needed robotic process automation (RPA) to automate a workflow. But when we discussed the outcome the team wanted, it became clear that RPA wasn’t necessary.

To avoid creating a hornet’s nest of complexity, organisations must fully consider where AI can add the most value before investing. This enables them to transform and enhance processes with AI rather than simply automate what they already have.

For example, a retailer using AI to make case notes during customer service calls is automating an existing process. Another retailer might use AI to analyse those case notes, along with social media insights, to inform stock decision-making for the next quarter. This can create greater impact.

Getting your workforce AI-ready

Implementing a successful AI strategy requires employers to prepare the workforce with the necessary skills. Not every employee needs to become a data scientist, but upskilling can help them become AI literate—with a foundational knowledge of what AI is capable of and how to use it responsibly to augment their work.

Leaders must also create a culture in which employees feel empowered to experiment with AI and find the most valuable use cases for their roles. Progress is happening in this area. More than half (61%) of the leaders we surveyed for the Enterprise AI Maturity Index are enabling teams to recommend AI solutions to everyday workplace problems.

By bringing employees along for the journey, leaders can build AI adoption across the organisation, which can lead to better outcomes.

A telecom company, for example, invited frontline service employees to experiment with generative AI at the contact centre. This enabled the organisation to increase customer satisfaction, achieve higher case resolution rates, and build a pipeline of new AI use cases.

Prioritising AI governance in the C-suite

Providing employees with the tools and environment to experiment with AI can be valuable—but experimentation needs guardrails. A strong enterprise AI strategy focuses on AI governance, compliance, and security by implementing clear policies around AI usage and training.

For example, leaders can introduce guardrails around how people use AI in their daily workflows. One way to do this is by establishing a file labelling system to differentiate content that should be used only in a secure AI environment—where it won’t be used to train public-facing AI models.

Legislation such as the EU AI Act makes good governance a business imperative in the face of regulatory penalties for noncompliance. Data security breaches can also pose large reputational and financial challenges. This forces the topic onto the C-suite agenda, with executives leading the charge on AI governance.

A successful AI strategy isn’t just about having the most advanced technologies. It’s built on a combination of purpose-built solutions aligned with desired business outcomes and investment in the necessary skills, culture, and governance to drive innovation with AI.

Find out how ServiceNow can help you put AI to work for people.