Introduction: Lots of AI Activity, Very Little Growth

A mid-sized professional services firm decided last year that it could not afford to fall behind on AI. It bought licences for a popular AI assistant for every employee, hired a data specialist and launched five pilot projects across marketing, finance and HR.

Twelve months later, the CEO asked a simple question in the board meeting: “What has this done for our revenue or our margins?” Nobody had a clear answer. Some people used the tools daily. Most had tried them a few times and gone back to old habits. Two pilots had quietly stopped. The costs, however, were very clear.

This story is common, and the research backs it up. McKinsey’s 2025 State of AI survey found that 88% of organisations now use AI in at least one business function, yet only 39% report any impact on profit at company level. For most of them, that impact is less than 5% of EBIT.

The gap is starting to close for some. BCG’s September 2026 research found that nearly half of companies now capture meaningful value from AI, up from just 5% a year earlier. The difference between the two groups is not the technology they buy. It is the quality of their AI strategy and how well they execute it.

This article explains what that difference looks like in practice, and how leaders can build an AI strategy that turns AI spend into real business growth.

Build Your AI Strategy Around Business Problems, Not Tools

The most common mistake is starting with the technology. A vendor shows an impressive demo, a competitor announces an AI launch, and suddenly the business is buying tools before it has decided what problem they should solve.

A strong AI strategy starts from the other end. It begins with your business strategy and asks one question: where would faster decisions, lower costs or better customer experiences make the biggest difference to growth?

To answer that, sit down with your leadership team and work through three steps:

1. Name your top business goals for the next 12 to 24 months. For example, grow revenue in a new segment, improve gross margin by three points or cut customer response times in half.
2. Map the bottlenecks that slow each goal down. Look for work that is repetitive, slow, error-prone or dependent on a few overloaded people.
3. Ask where AI could remove or reduce each bottleneck. Only now does the technology enter the conversation.

Here is what this looks like in practice. A distribution business wants to grow revenue by 15%. Its bottleneck is not a lack of leads. It is that the sales team spends nearly half its week preparing quotes by hand. Using AI to draft quotes from past orders and price lists frees up time for selling. That is an AI strategy linked directly to business growth, not a tool searching for a purpose.

The research supports this approach. BCG found that the companies getting the most from AI start with strategic clarity on where AI creates value, and then focus their investment there.

Focus on a Few High-Value Use Cases and Measure Them

Five scattered pilots rarely beat two focused ones. When AI budgets are spread thinly across every department, each project gets too little attention to succeed, and none of them become big enough to move the numbers.

Choose with a simple scoring approach

Score each possible AI use case on two questions:

• Value: if this works, how much would it improve revenue, margin, speed or customer experience?
• Ease: do we have the data, the people and the process clarity to make it work in the next 3 to 6 months?

Start with the use cases that score high on both. Park the rest for later. Typical high-value starting points for growing businesses include:

• Sales proposals, quotes and follow-up emails.
• Customer service responses and ticket sorting.
• Finance tasks such as invoice matching, cash flow forecasting and reporting.
• Internal knowledge search, so staff stop hunting through folders and old emails.

Define success before you start

Every AI project needs a clear business measure agreed in advance, along with a baseline and a target. For example:

This matters more than it seems. If you cannot measure the result, you cannot prove the value, and AI projects without proven value are the first ones cut when budgets get tight. BCG’s leaders measure AI impact directly in the P&L. Your AI strategy should do the same, even if your version is a simple monthly scorecard.

AI Execution: Redesign the Work, Then Bring People With You

This is where most AI investments succeed or fail. Buying a tool is easy. Changing how work gets done is hard, and that is where the value sits.

Redesign the workflow, not just the task

Adding AI to a broken process gives you a faster broken process. McKinsey found that the companies seeing the most value from AI are nearly three times as likely to have fundamentally redesigned their workflows.

In practice, that means looking at the whole process from start to finish. Take the sales quote example. Real AI execution does not stop at “AI drafts the quote”. It asks:

• Who checks the quote, and does that approval step still need to exist?
• Can the quote go straight into the CRM and trigger an automatic follow-up?
• Which pricing decisions can now be made by the salesperson instead of waiting for a manager?

Each answer removes delay. Together, they turn a small time saving into a real change in sales speed and capacity.

Make adoption part of the plan

In our experience, the biggest barrier to AI execution is not the software. It is people who are unsure how to use it, worried about what it means for their jobs or simply too busy to learn something new.

Practical ways to drive adoption:

• Train by role, not by tool. Show the finance team how AI handles their month-end tasks, not a generic demo.
• Appoint AI champions in each team who test new uses and help colleagues.
• Talk openly about jobs. Explain that the goal is to remove low-value work so people can focus on customers, judgement and growth.
• Track usage alongside results. If adoption is low, find out why before blaming the technology.

When people see AI saving them real hours in their own work, adoption tends to spread on its own.

Lead Your AI Strategy From the Top and Scale What Works

AI cannot be handed to the IT team and left there. The businesses getting real growth from AI treat it as a leadership priority, run across the whole company.

Give AI a senior owner and a regular review

McKinsey found that in companies getting the most value from AI, respondents were three times as likely to say senior leaders show clear ownership of and commitment to AI initiatives. That ownership shows up in simple, visible ways:

• A named executive is accountable for the AI strategy and its results.
• AI progress is reviewed monthly in the leadership meeting, using the same scorecard as other priorities.
• Budget decisions follow results. Projects that hit their targets get more investment. Projects that do not are fixed or stopped.

Set clear rules early

Good governance does not need to be complicated. Even a two-page policy helps people use AI with confidence. Cover the basics:

• Which tools are approved, and which data must never be entered into them.
• Where a human must review AI output before it reaches a customer.
• Who to ask when something is unclear.

For businesses in regulated sectors or handling sensitive customer data, this step protects both your reputation and your clients.

Scale in waves, not all at once

Once a use case proves its value, roll it out to the next team or region. Then return to your priority list and pick the next use case. This steady cycle of prove, scale and repeat builds momentum without overwhelming the business.

It also changes how growth-focused leaders think about AI. Efficiency is a good start, but McKinsey’s top performers are more likely to also set growth and innovation goals, such as new services, faster market entry or better customer insight. That is where an AI strategy shifts from saving money to making it.

Conclusion: Make 2026 the Year AI Pays for Itself

AI is no longer a question of whether to invest. Most businesses already have. The real question is whether that investment shows up in revenue, margins and customer results.

The companies that get there follow the same pattern. They start from business problems, focus on a few high-value use cases, measure results from day one, redesign the work around AI and lead the change from the top.

If you want a practical place to start this quarter:

1. List your top three business goals and the biggest bottleneck behind each one.
2. Pick one or two AI use cases that remove those bottlenecks, with a clear measure and target.
3. Name a senior owner and review progress every month alongside your other priorities.

The key takeaway: an AI strategy only creates growth when it is treated as a business strategy, with clear priorities, accountable owners and disciplined AI execution.

How Straxecutes Can Help

At Straxecutes, we help founders, CEOs and leadership teams turn plans into measurable performance. We bring the strategy and execution discipline that makes AI investments pay off. We help you:

• Shape an AI strategy linked to your business goals, so every AI investment has a clear purpose and measure.
• Prioritise the right use cases across sales, finance, operations and customer service.
• Redesign the processes around AI so time savings turn into growth, not just busier teams.
• Build the leadership rhythm and accountability to track results, scale what works and stop what does not.
• Prepare your people through practical leadership development that helps teams adopt new ways of working.

If your business is spending on AI but not yet seeing the growth, let’s talk. Book a conversation with Straxecutes and find out where AI can create the most value in your business first.

Sources

• The state of AI in 2025: Agents, innovation, and transformation, McKinsey, November 2025 (survey of 1,993 participants).
• AI Is Starting to Pay Off. Almost 50% of Companies Now Generate Value with It, BCG press release, 30 September 2026 (survey of 1,330 senior leaders).