The Ultimate Guide to Crafting an Effective AI Adoption Strategy

Try not to break out in a sweat if this sounds familiar: Your company has onboarded 25 different AI tools for various use cases because each team and department bought them for their own purposes. A few internal AI and data professionals are now stretched too thin with a big roster of half-baked ideas and projects, and no way for all these tools to speak with each other.  

Of the projects that teams do complete, anything they’ve learned is kept close to the chest with no channels for sharing discoveries. If you’re stressed even thinking about this scenario, take a deep breath. So many companies have sprinted full speed ahead in the race to adopt AI. The problem is that their team members are often racing toward wildly different goalposts with no agreement on where the finish line is. They need direction.  

As a data professional in the rapidly evolving AI era, you’re not just racing alongside your colleagues anymore; you also have to be a coach, setting that direction and helping create a strategy to succeed. Thankfully, you don’t need a five-year roadmap to adopt AI well. (Frankly, a five-year plan is probably too restrictive for a technological wave like this.) Instead, you should have a focused and flexible plan, clear use cases, and a strategy that works with how your business runs now.  

So relax, wipe off your brow, and let’s explore how to go about creating a strategy for adopting AI that will keep your team running together toward a finish line that makes sense for your organization.

Understanding AI adoption

AI is everywhere. Asking someone right now if they use AI is like asking someone if they use oxygen. Most people don’t even know when they are interacting with AI because it’s become so infused into the technology that people rely on in their day-to-day lives. That’s why understanding what AI adoption means from a business perspective can become such a conundrum.  

However, there’s no doubt that more companies are integrating AI into their work. According to a recent McKinsey Global Survey on AI, which included nearly 1,500 participants representing different industries, company sizes, and functional specialties, almost 80 percent of respondents said their organizations were using AI in at least one business function, a jump from 72 percent in early 2024.  

And for the first time since they began asking this question, the majority of respondents indicated that their companies were using AI in multiple business functions, with the average response being around three business functions. But even McKinsey admitted that they left what constituted “adoption” up to the respondents to decide.  

So, does onboarding those individual AI tools really count as AI adoption or should there be a more formalized process?  

The MIT Center for Information Systems Research (CISR) would probably argue the latter. Adoption doesn’t mean using a new tool; it means infusing AI into systems and workflows so that the technology actually improves a company’s performance. The researchers at MIT’s CISR broke down what AI maturity looks like into four stages:

1. Experimenting and preparing: Organizations educate their workforce about AI, define the parameters of how they can use AI and begin rolling out data-driven decision-making.  ‍
2. Building pilots and capabilities: Companies start to identify specific use cases for AI and simplify work processes through automation and the use of generative AI.  ‍
3. Developing AI ways of working: Companies begin to scale their AI platforms and dashboards, expanding their automation efforts and developing proprietary AI models.  ‍
4. Becoming AI future ready: Organizations build AI into all their decision-making and can actually sell their AI capabilities as a service.  

Why should companies pay attention to how they are advancing through these stages of true AI adoption? According to the researchers at MIT’s CISR, organizations in the first two stages performed below their industry’s financial average, in comparison to organizations in the second two stages that outperformed their industry peers.  

Building a digital transformation roadmap

AI can offer companies a host of benefits. Among the most commonly cited uses are:

• Transforming and analyzing large quantities of data quickly, helping to rapidly turn around what they’ve learned to inform important decisions that affect the organization and its department.
• Uncovering patterns in data that might otherwise have remained hidden, allowing better forecasting and predictions.
• Providing more accurate insights that may help to remove bias, which can creep into traditional data analysis.
• Automating processes and thereby eliminating the repetitive tasks employees have to conduct, giving them the opportunity to work on more valuable strategic projects.

Unsurprisingly, AI seems to be on the tip of every executive’s tongue, with directives regularly issued to implement and execute as fast as possible to speed up their company’s digital transformation. But those executives should slow down or risk entangling their data team in a web of disconnected digital tools that don’t end up delivering the value that sales teams promise.

The reality is that while AI can fundamentally change how teams across an organization work, there’s no one-size-fits-all approach to adopting these types of tools. Businesses, departments, and individual teams will each have their own unique needs, which require careful consideration. Consequently, companies need a more judicious approach to develop a cohesive strategy—one that takes into account the organization’s overall goals, existing capabilities, and opportunities for alignment.

It may be a bit of a buzzkill for the overeager CEO, but putting in the grunt work upfront will pay off. According to a recent Thompson Reuters report on AI’s impact on professional work, businesses that have developed a strategic AI plan are almost twice as likely to see revenue growth from their AI investment compared to those that adopted AI informally.

Now, if you’ve been lucky enough to pump the brakes on every middle manager at your organization signing up for their favorite AI tool, you still shouldn’t stall momentum on your company’s digital transformation. There’s real value to gain if you plan well.  

How do you begin to develop an actual strategy? In their book, “Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI,” researchers at the company lay out a framework, highlighting the six capabilities organizations should have for a successful digital and AI transformation:

1. Alignment from senior leaders on the vision, value, and direction of the transformation. ‍
2. Talent with the right skills to execute on the vision. ‍
3. Coordination between the business, operations, and technology units of your organization. ‍
4. Technology that encourages innovation. ‍
5. Data that is accurate and complete, along with insights that are made accessible across the organization. ‍
6. The ability to scale digital solutions to manage transformation and capture as much value as possible.

Let’s dig a little deeper into how to develop some of these capabilities.

Steps for AI strategy development

There are many different components to AI organizational readiness. But one of the most important is alignment across the organization on the goals of your AI strategy. Without a concrete vision, decisions similarly won’t have a purpose. The trick, according to the researchers at McKinsey, is to create “an ambitious yet realistic” transformation plan.  

Step 1: Establish what you are trying to achieve

Getting the C-Suite on board is always going to be a top priority, but when it comes to investing in AI, many leaders are already itching to start that transformation yesterday. What you’ll have to do is help them articulate a clear goal that can serve as the guidepost for teams across your company to race toward.  

For newcomers to the AI adoption race, the team at McKinsey suggests looking to other companies that have already integrated AI into their workflows and using their success as inspiration. Ask questions like, “Could a digital transformation create new ways to connect directly with our customers and improve their experience?” “Would AI reshape our supply chain and help eliminate waste?”  

This grounding decision will also help guide which business units you should really focus on for accelerating AI adoption. Not every department will need a fancy new tool, and some may even benefit from sticking to more traditional ways of working.  

Step 2: Find use cases that solve real problems  

Once you've nailed down the organizational goal, it's time to consider how it impacts the activities of the individual units and which tools make the most sense to support the different teams as they work toward achieving that goal. At this point, you’ll want to make sure that middle managers and individual contributors are on board with whatever changes or recommendations you are planning to introduce.

Andrew Wood, who writes AI Prompt Hackers on Substack, says that gaining buy-in really comes down to two things: showing concrete business value and building confidence.  

“If AI feels like a tech push from IT, people resist,” he explains. “But when you connect it to real problems business units already care about—customer retention, efficiency, revenue growth, removing pain—they lean in.”

He recommends involving leaders across the business units early and allowing them to help shape the use cases.

“Give teams small wins to prove it’s a tool that makes their jobs easier,” Wood says.  

Step 3: Secure genuine commitment from leadership

It’s one thing to say you want a digital transformation and another thing entirely to invest your budget into the resources required to execute that vision. To the leaders that talk a big game, the message is clear: It’s time to put your money where your mouth is.  

The talent, technology, and data capabilities necessary for organizations to make the digital leap often come with significant expenses. Your organization may need to hire new people or retrain the current workforce. Units may have to find new ways to store and analyze data at scale, which could mean upgrading your data systems. And the payoff may not be immediate.  

But if the goal is fully adopting AI and transforming your capabilities, organizations should look beyond the quick wins and commit the money, time, and resources it can take to fulfill a long-term vision.

Conducting an AI risk assessment  

Again, whether your company wants to use it or not, AI is probably already reshaping how many of your employees are working. That can reap many benefits for your organization, but it can also expose you to risk. Consider some of these sobering statistics from a recent report on trust and attitudes about AI from KPMG:

• 44% of US workers are knowingly using AI tools at work in ways that their employers haven’t authorized.
• 46% have uploaded sensitive company information and intellectual property to public AI platforms.
• 53% have presented AI-generated content as their own.
• 58% have relied on AI output without thoroughly assessing the information.
• 64% admit to putting less effort into their work thanks to reliance on AI.  

It’s not all doom and gloom though because better business intelligence and AI also seem to go hand inhand. Eighty percent of US workers believe AI has improved operational efficiency and innovative strategy, while more than half credit AI for increasing creativity, efficiency, quality of work, and new ways of thinking. So companies clearly have to balance risk and reward. And the best way to do that is good governance.  

“Governance can’t be an afterthought, because once AI is embedded in processes, it becomes very hard to unwind poor practices,” says Wood. “Good governance means clarity on data usage, transparency in decision-making, and a framework for evaluating both performance and risk.”

Needless to say, governance takes a lot of effort. But Woods believes it’s worth it.  

“Ethical guardrails around bias, fairness, and explainability will help make AI adoption sustainable and trustworthy,” Wood explains.

How do you mitigate risks through good governance? Lucky for you, Domo has already tackled this topic. Let’s review some of the suggestions for putting AI governance into practice:

• Define policies and roles: Document your non-negotiables, including the use of ethical AI, compliance standards, and procedures for addressing errors and incidents. Assign people to oversee the implementation by giving them both authority and responsibility.  ‍
• Build an AI model inventory: Create a detailed catalog that documents every AI model in your organization. What does it do? What are its associated risks? Who uses it? Where does the data come from?  ‍
• Employ risk management: Use established frameworks (like NIST AI RMF) but adapt them to your specific context. Look for organizational risks (regulatory compliance, reputation damage) and model-specific risks (bias, security holes, data poisoning).  ‍
• Strengthen data governance: Invest in data quality before you invest in advanced AI tools. Make sure your training data sets represent the populations your AI will serve. Verify compliance with privacy regulations like GDPR and HIPAA to protect peoples’ information and rights. ‍
• Conduct model verification and validation: Design testing protocols that include adversarial attacks, fairness assessments, and supply chain security reviews. Test your system architecture, not just individual models. Verify that your AI behaves appropriately when it encounters unexpected inputs.  ‍
• Apply tools: Look for solutions like Domo AI that centralize governance functions while integrating easily with your existing tech stack. Prioritize tools that automate routine compliance tasks, provide real-time monitoring, and offer clear visibility into model performance. ‍
• Monitor continuously: Use monitoring that tracks model performance, bias emergence, and compliance drift in real-time. Build dashboards that quickly surface problems, allowing you to address them before they impact users.  

Strategies to introduce AI

If you determine that adopting AI is worth the risk—and let’s be honest, you probably will to some extent—then it's time to decide how to roll out new tools and platforms to your teams.  

“Success needs training, communication, and sometimes rethinking roles,” Wood says. “Manage the technical and human sides together and the pilots will turn into real business impact.”  

Don’t be completely alarmed if you find there’s a significant skills gap among your employees when it comes to using AI. According to a Nash Squared/Harvey Nash report on digital leadership, the AI boom has created the “fastest developing tech skills shortage” for companies. More than half of technology leaders said that their companies lacked the AI skills essential for keeping up with their investments in the technology.  

What’s an executive to do?

“No company can outsource its way to digital excellence,” the researchers at McKinsey warn, so organizations will have to build their own digital talent pool. They can start to beef up their roster of experts by:

1. Conducting an assessment of the current crop of employees to determine what skills are necessary and whether they can be developed through training or hiring.  
2. Laying out clear education and career pathways with tailored and structured learning and development courses for employees who require upskilling.  
3. Setting up a special group, likely within HR, that is devoted to finding talent with the specific technological expertise your organization seeks.  
4. Partnering with companies and platforms that can offer expertise and help you realize the benefits of AI through their services.  

From there, you can start to run some pilot programs to test out how you can begin to introduce AI into your teams’ workflows. Like Wood said, it’s about giving teams small wins to not only show what’s possible, but also to help them become more comfortable with the tools. And once they do start to feel comfortable, you can begin to have discussions about what type of operating model can best support your AI and digital transformation.  

The future of AI adoption

AI is rapidly changing the way the world works. For organizations just starting to make the leap into a digital transformation, preparing for these changes can feel daunting. But what's clear is that the organizations that take the time to develop a well-planned strategy are better positioned to see a return on investment from AI.  

If you're ready to take the reins and start shaping your organization’s AI strategy, consider tools that will make the digital transformation easier. Domo offers a platform where adopting AI becomes practical, not hypothetical.  

With Domo, you can connect clean, governed data to tools, workflows, and models, including LLMs and AI agents. Teams can start small, move fast, and stay in control. Domo supports responsible growth with built-in visibility, security, and flexibility, so you don’t just run pilots—you run production.  

When you're ready to learn more about what Domo has to offer, connect with our sales team who can walk you through demos of the different features that our AI and data products can provide. You can also explore more about how to apply AI in practical, powerful ways to your organization by watching our Future of AI series.