What CEOs Need to Know Before Investing in AI

CEOs around the world face considerable pressure to make AI work.

Boards expect companies to adopt AI and show measurable business results. According to the Global AI Confessions Report, 72% of US CEOs say their boards are actively pushing for clear returns from AI investments.

With this level of pressure, it's easy to rush into AI projects just to say the company has an AI strategy. However, having considerable experience in AI development, at Empat, we can see that's often the wrong approach.

AI should be treated like any other major business investment. Leaders need to understand the problem they're trying to solve and the value they expect to create before committing time and budget to AI development. 

This guide explains how to evaluate AI opportunities before investing. In this article, we’ll explain how to decide whether to build an AI solution, buy an existing one, or work with a development partner.  

The Numbers Every CEO Should See First

Before launching your AI strategy, it's important to understand the risks you may face.

The problem doesn’t address the negative effects of AI. The issue is that many companies invest in AI without a clear business case, the right data, or a workflow for measuring success.

That’s why you may find this data beneficial:

• RAND Corporation analyzed more than 2,400 enterprise AI projects and found that more than 80% failed to deliver their planned business value. This amount is almost twice as high compared to ordinary IT projects.
• S&P Global Market Intelligence reported that the share of companies abandoning most of their AI initiatives rose from 17% in 2024 to 42% in 2025.
• Gartner predicts that 40% of AI projects will be canceled by 2027. At the same time, projects without AI-ready data continue to fail at a high rate.
• A 2025 MIT Sloan study found that 61% of enterprise AI projects were approved based on projected business benefits that were never measured after launch.

Even though these studies are from different organizations, they all outline the same market pattern. Most AI projects fail because companies don't define success upfront, lack the right data, or lose executive focus once the initial excitement fades.

Treat AI Like Capital Allocation, Not a Trend

When CEOs decide whether to build a new factory or expand into a new market, they don't do so just because their competitors do. They analyze the potential ROI and risks. You should evaluate the artificial intelligence launch in the same way.

We recommend asking these three questions before approving any AI investment:

• What business outcome will this improve? Will it increase revenue, reduce costs, shorten cycle times, or lower risk? Most importantly, how will success be measured in financial terms?
• How long will it take to generate value? Unlike many software projects, AI isn't a one-time investment. The thing is that models need monitoring, updates, and ongoing maintenance. That’s why it's important to understand the full cost over time.
• What is our downside? What happens if the project doesn't deliver the expected results? What if a key vendor raises prices, changes its product, or stops supporting the service?

These questions will also help you provide clear and meaningful answers for the boards when their pressure continues to grow.

Four Ways to Invest in AI  

When it comes to the choice of the AI business model, most CEOs are considering the same list of options:

• Buy a SaaS tool with AI features already built in. It’s the fastest-to-deploy variant, although it also provides the least control.
• Integrate a third-party model or API into the existing product. This option offers moderate speed and control.
• Build a custom AI solution from scratch. Even though it’s the slowest and most expensive strategy, it allows full control and IP ownership.
• Automate internal workflows with existing tools. The fourth option has the lowest-risk and the highest ROI starting point. Nevertheless, it’s often rather overlooked.

There’s no universal AI strategy that would fit all enterprises. The right choice depends on the problems and challenges that a business faces, as well as available capabilities.

For many organizations, the best approach is to start step by step. Internal automation can provide small but rather fast results. At the same time, those early successes often provide the confidence and data needed to justify larger AI investments.

If AI is central to your product or you want it to become a competitive advantage for your business, buying off-the-shelf tools will probably not be enough. In those cases, integrating existing AI models or building a custom solution can create greater long-term value. However, you also need to understand that the initial investment is higher.

Build vs. Buy vs. Partner: Which AI Development Approach Is Best?

Once you've decided that an AI investment is worth making, the next question is how to launch it in your organization. There are three main options that you should consider. 

Buy AI Tools

Buying an AI solution is definitely the fastest way to get started. The technology is already built, which means you can begin using it sooner and rely on the vendor for updates and maintenance.

However, usually the subscription price is only part of the cost.

Integrating a new AI tool with your existing systems, databases, and internal applications can significantly increase the overall investment. From our experience, integration work often costs 150–200% of the software's purchase price.

Vendor dependency is another issue to consider. Many organizations are concerned about vendor lock-in. Companies without an exit strategy may face switching costs many times higher than expected.

Such an AI strategy also requires ongoing maintenance, even after deployment. You need to monitor models, update, and retrain them as business data changes. As a result, it causes additional expenditures.

Build AI Technologies

Building a custom AI solution gives your business the highest level of control.

You own the technology, the data, and the intellectual property. If AI is a core part of your business or the primary competitive advantage, this can be the right long-term investment.

However, the major concerns deal with time and cost.

Developing an enterprise AI solution typically requires an investment of up to $1.5 million. It definitely poses considerable financial risks for businesses. At the same time, the break-even point usually takes more than two years to reach.

Partner with AI Development Company 

For many companies that are trying to launch AI, partnering with an experienced AI development team is the best and most balanced option.

On one hand, you gain access to specialized expertise and can launch rather fast. On the other hand, you create a solution that meets your business needs instead of adapting your processes to fit a generic product.

The biggest risk isn't that the technology becomes dependent on the partner.

If the external team builds the solution but your employees never learn how it works, your company may struggle to maintain or expand it after the project ends.

That's why knowledge transfer should be part of every engagement. A good AI partner helps your internal team operate the solution and improve it over time.

The Main Recommendation For CEOs About AI

AI can create huge business value, but only when you treat it as an investment rather than an experiment.

Every AI initiative should have a clear business objective, a realistic financial case, and measurable success criteria. Leaders also should choose the right approach based on the strategic value AI will create for their business.

About the Author

Kateryna Stankova

Kateryna Stankova is a Business Development Manager at Empat. They lead strategic growth initiatives, foster long-term client partnerships, and identify new market opportunities. With a strong understanding of the tech ecosystem and an emphasis on human-centered communication, they help bridge visionary ideas with scalable, high-impact software solutions.

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