How to create an enterprise AI strategy that delivers real ROI

The past year or so has shown us that ad hoc AI experiments simply won’t deliver the ROI organizations hope for. 

“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.” (Gartner, The Pillars of a Successful Artificial Intelligence Strategy, 16 September 2025).

The missing link? A strategy. Strategies connect ambition to execution, providing a clear roadmap to value. But unlike other business strategies we’ve been using for years, there’s no tried-and-true playbook for AI strategies, especially for agentic AI. 

It’s easy to get stuck in analysis paralysis, endlessly evaluating every way you could adopt AI agents. Or, on the other end of the spectrum, it may be tempting to jump into the AI waters headfirst without a plan. 

I wouldn’t recommend either of these options. Instead, you’ll want to find a middle ground, building an AI business strategy that balances risk with quick learning. 

This blog will provide a clear framework for doing just that. You’ll learn how to create an enterprise AI strategy that helps you focus your efforts, move quickly, and, ultimately, implement AI in the most effective way for your business.

What is an enterprise AI strategy?

An enterprise AI strategy is a living roadmap that aligns AI initiatives with business goals, prioritizing high-impact AI use cases while managing technical and ethical risks.

At Lucid, we consider strategy the second phase of a business transformation, following readiness. During the AI readiness phase, you’re assessing the current state of your business and market to understand where AI could provide the most value to your business. This information directly informs your strategy. For instance, when documenting your current state, you may discover inefficiencies that AI could address.

The goal when first putting together your AI strategy is not to have a comprehensive, polished document. There’s simply too much unknown around AI for that to make sense. Rather, your goal is simply to define a clear starting point and a plan for uncovering more information, learning, and adapting your strategy. 

Essentially, building an AI strategy involves:

• Identifying what areas of the business could benefit most from AI (and what areas would introduce too much risk) 
• Planning how to incrementally roll out AI initiatives on a small scale, considering factors such as training, enablement, and change management
• Clarifying decision milestones with metrics you’ll use to determine the success of your initial AI projects
• Adjusting your plan based on initial learnings to scale what works 

Benefits of an enterprise AI strategy

Strategies, in any part of the business, help with effective resource allocation, decision-making, alignment, efficiency, and overall competitive posture. AI strategies are no different.

The benefits of an AI strategy, in particular, are that it:

• Helps get stakeholder buy-in. When you can show how specific AI initiatives tie to business KPIs, you address the skepticism leaders may have around it and shift the conversation from spending money to investing it.
• Reduces security, legal, and compliance risk. By defining data-privacy guardrails, access and decision rights, and bias-monitoring protocols, an AI strategy helps you balance risk reduction with speed. 
• Improves AI adoption rates. A Lucid survey on AI adoption shows that employees are hesitant to adopt AI due to uncertainty about its impact on their jobs, a lack of skills, or unclear guidelines. A strategy clarifies which AI tools are available to use and how to use them, increasing confidence and adoption. 

Key components of an enterprise AI strategy: What should your strategy include?

Your AI strategy should be a living document—one that you can easily update as you learn. 

I recommend using Lucid’s AI strategy brainstorm template to collaboratively build your strategy asynchronously or in a live session. This template provides the flexible working space you need when first brainstorming your strategy, along with structured elements and integrations to bring it to life.

With that said, let’s get into the main elements you need when first creating your enterprise AI strategy.

A vision

You may not need a vision to start experimenting with AI, but you will need a vision to go from random AI tool usage to enterprise value. An AI vision is what gives your AI transformation the strategic focus necessary to make prioritization decisions, align cross-functional efforts, and motivate teams.

According to Gartner®, “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. 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?” (Gartner, The Pillars of a Successful Artificial Intelligence Strategy, 16 September 2025).

You can think of the vision as the “why” behind AI adoption. At this point, it’s okay to be a bit aspirational. You can start working on your vision by brainstorming the long-term outcomes and goals you hope to achieve. 

The scope 

There are seemingly infinite possibilities for what AI can do, so you have to pick and choose which use cases will actually help you reach your vision. You can start broad, brainstorming use cases that could increase efficiency, improve the customer experience, or even create new products and services. 

Once you have a list of ideas, you’ll want to narrow it down to just a few to start. Using the Lucid template, you can work together to prioritize ideas, considering factors like feasibility, risk, and potential value.

There’s no right or wrong way to identify these use cases, but we’ve found it more beneficial to brainstorm around questions like “Where are teams spending too much time?” or “What processes are we struggling to scale?” rather than “Where could we apply AI?” The former helps you identify areas of the business where AI could help solve real problems.

For instance, at Lucid, we’ve noticed that a "green flag" for AI is any process that requires constant hiring to scale. If an org is ballooning just to keep up with volume, that process could be a prime candidate for an AI agent.

Be sure to document what AI use cases you will pursue first, as well as which ones you will not pursue in your initial round of AI implementation. Documenting these decisions can prevent scope creep and encourage follow-through on the ideas you do move forward with.

Measurable outcomes

Your AI strategy should also define what “success” looks like for the use cases you will start with. Identifying these outcomes upfront is one of the most powerful ways to scale AI projects. Metrics, after all, let you know if a project was successful and worth repeating or implementing in other teams. 

A helpful way to frame your initial metrics is as a testable hypothesis. For example:

By implementing an AI agent in our service ticketing workflow, we expect to increase ticket resolution volume by 40% within the first 90 days without increasing headcount.

This approach transforms a vague goal into a clear signal. If you hit the 40% mark, you have the green light to scale. If you hit 10%, you have the data needed to pivot or refine the project.

Other types of metrics you could look at include:

• Efficiency, such as a reduction in hours spent on specific tasks
• Quality, including improvements in accuracy or sentiment scores
• Impact on the bottom line, like cost reduction

Your metrics should be time-bound with clear owners, and I recommend identifying a regular cadence to review the data and adjust your strategy.

An implementation plan 

You now have the why (vision), the where (outcomes), and the what (scope) behind your AI transformation. You’re just missing the how, which is where your plan comes in. 

Identify what steps actually need to happen to implement each AI project. Be sure to address each of these questions as you build out your AI implementation plan:

• What enablement, skills training, and communication are needed to ensure smooth adoption?
• What system and infrastructure changes are needed to host the AI agent?
• What key milestones do you need to hit, and what dependencies could stall your progress? 
• Who is responsible for each AI initiative, and what other teams or stakeholders are involved? 

You can use a timeline in Lucid to map out each step, deadline, dependency, and owner. I recommend syncing your Lucid plan with your system of record, such as Jira or Asana, to keep teams aligned and your visuals up to date throughout the AI implementation. 

Governance guardrails 

Even when you’re just beginning, it’s important to identify what checks and balances are needed to manage security, legal, and cost considerations. 

Look at which data and systems the AI agent would need to view and/or access to complete tasks, and evaluate data sensitivity, quality, and lineage. What guardrails or human-in-the-loop processes do you need in place to ensure compliance, keep data secure, and prevent agents from running up unmonitored bills?

You may wish to use a RASCI chart to clarify roles, a swimlane process diagram to visualize where humans and agents work together, or a data flow diagram to identify any data concerns before deployment.

Tips for creating an enterprise AI strategy

As we’ve worked to build our own AI strategy at Lucid, we’ve learned some valuable lessons along the way. Here are the tips I’d recommend based on our progress so far.

Think of your initial strategy as a plan to learn

Remember: It’s impossible to design a full-blown strategy if you haven’t even tried implementing a single AI agent. At Lucid, we’re getting started by picking a really specific example and committing to it. 

Treating our strategy as a learning plan makes it easier to control (i.e., fewer governance concerns) and measure, providing a faster learning feedback loop. 

Plus, taking the learning approach inherently reduces the risk of investing in the wrong areas, allowing you to validate ideas first before designing a more concrete strategy around them. 

Prioritize cross-functional collaboration and alignment

Don’t expect to build an AI strategy in a silo. Each function, after all, has a different perspective, so getting diverse input allows you to identify the best opportunities to start.

We’ve hosted ideation sessions to capture leaders' top-of-mind concerns across the company. I’d encourage you to start with a similar cross-functional conversation. You may be surprised at how many potential “AI hotspots” you’ll quickly identify by just asking. If you’re using Lucid, you can also get this input asynchronously by commenting and tagging teammates directly where you’d like their feedback. 

As you begin to implement AI, look for channels to stay aligned on the results and progress of each AI project so you can share learnings. Team spaces in Lucid offer a great way to centralize your strategy and other resources, like timelines and technical diagrams. 

Use a mix of qualitative feedback and quantitative data 

Organizations can easily fall into the trap of being too "gut-driven,” always deferring to the loudest voice in the room, or becoming overly data-obsessed and attempting to remove all risk before acting.

Instead of seeking data for every possibility or avoiding it altogether, you can get the best of both worlds by starting with executive intuition (qualitative data) and readily available metrics (quantitative data) to identify three or four focus areas. Then, you can dig into team feedback and collect more granular metrics to pinpoint use cases within the broad focus areas.  

At Lucid, we’ve found that while each executive might not know the specific friction points, they do have a sense of which areas of their department are inefficient, hard to scale, or present other challenges that indicate opportunities for AI. 

From there, we can loop in the right teams to actually map out the processes in Lucid and identify the specific problem spots. For instance, we can add data to these maps to identify delays or excessive wait times between steps. 

See how Lucid supports every stage of AI transformation

Lucid provides the context and clarity that agentic AI transformation hinges on. Whether you’re building your AI strategy, preparing for transformation by documenting your current state, or ready to execute on your plan, move faster with Lucid.

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