Adopting an Intentional AI Strategy in 2026

As of late 2025, 88% of organizations are using AI in at least one business function, up from just 55% in 2023. But while this is the case, only 7% have fully scaled AI across their organization, while 62% are still experimenting or piloting. Most companies are drifting with AI—using tools because they’re available or trendy, not because they’re part of a coherent strategy.

While some may hope that the AI wave will pass, the truth is the opposite: AI is here to stay. Companies that don’t embrace AI will likely fall behind. But the companies that are building intentionally will pull ahead.

Gartner predicts that by 2028, organizations with sustained AI-first strategies will achieve 25% better business results than their peers. As we enter 2026, the window to move from drifting to building is closing. It’s a certainty that AI will reshape your industry—the only question is whether you’ll direct that change or react to it.

Why strategy can’t wait

Many organizations still question the need for an AI strategy. They don’t see direct applications, are concerned about the unknowns, or think AI is only for coding.

But AI is already embedded in industries far beyond traditional tech. In healthcare, for example, AI systems can already examine stroke patients’ brains with twice the accuracy of human professionals and detect epilepsy lesions that radiologists miss. In education, 57% of higher education institutions are prioritizing AI for digital acceleration and personalized learning.

Here’s an uncomfortable truth: your organization is already using AI, whether you have an AI strategy in place or not. Many employees are using AI tools to complete their work. Some are using approved tools according to clear protocols. Others are pasting proprietary data into the free version of ChatGPT and copying the results blindly.

The absence of a strategy creates two problems. First, employees using AI haphazardly create security vulnerabilities, compliance issues, and what’s now called “workslop”—low-quality output that looks professional but lacks depth and substance. Second, employees who should be using AI aren’t, either because they don’t know which tools are approved or because they fear being replaced by them.

Strategy solves both problems. It channels AI use toward value creation while establishing guardrails. It helps employees transform anxiety into capability. And most importantly, it purposefully redefines what good work means in your organization—because AI is already redefining roles, whether you acknowledge it or not

3 strategic imperatives for 2026

Building an intentional AI strategy requires more than just declaring “we’re AI-first” on your homepage. It requires structural changes across three key dimensions:

1. Treat AI as infrastructure, not innovation.

The first mistake organizations make is treating AI as something special—like a center of excellence. This thinking leads to AI remaining peripheral.

Instead, AI must become infrastructure: as unremarkable as email, as essential as your Slack channels. Your leadership team should actively identify processes to automate, not to reduce headcount, but to redirect human effort toward higher-value work. When AI handles the routine tasks, your people can focus on the complex, creative, and strategic work that actually moves your business forward.

This shift must come from a cultural change. Software engineers should reach for AI coding assistants by default. Finance teams should assume AI will flag anomalies in real-time. Support teams should expect AI to handle basic questions. The goal is to make AI invisible, not impressive, as it’s woven into how work gets done every day.

2. Establish baseline AI competency standards.

You wouldn’t hire engineers who can’t use version control. You shouldn’t hire engineers who can’t effectively use AI coding tools.

As AI streamlines routine coding tasks, the role of software developers is evolving. Developers who master AI tools can focus on architecture, complex problem-solving, and work that requires deep contextual understanding—the work AI can’t handle. Developers who don’t will struggle to keep pace.

Having baseline standards doesn’t mean everyone needs to be an AI researcher. It means establishing basic competencies: understanding which tasks AI excels at, how to write effective prompts, how to validate AI output, and when to override AI recommendations. These should be hiring criteria, onboarding requirements, training mandates, and promotion considerations.

The alternative is a growing skills gap within your own organization, resulting in team members who’ve embraced AI becoming more productive, while those who haven’t struggle with the basics.

3. Implement guardrails before incidents happen.

AI offers organizations transformative productivity and efficiency gains. It also introduces serious risks: copyright infringement lawsuits, data leakage, hallucinated information presented as fact, and security vulnerabilities, to name just a few. The question isn’t whether these risks exist, but rather whether you’ll address them proactively or reactively.

Being reactive is expensive. It means learning about data leaks from customer complaints, discovering copyright issues through legal threats, or finding that proprietary code was used to train external models after the fact.

Being proactive means establishing clear policies now: which AI tools are approved, what data can be shared with them, how to verify AI-generated output, and what happens when the policies are violated. This requires training employees not just on how to use AI, but on how to use it responsibly within your organization’s risk tolerance.

Guardrails, when properly designed and implemented, aren’t about limiting AI use, but about enabling it safely at scale.

Building, not chasing

Becoming AI-first requires changes that feel difficult because transformation of any kind is challenging. But organizations that invest in deploying AI capabilities to existing operations are fundamentally redesigning how work gets done.

At MacPaw, we don’t treat AI as an add-on or a trend to ride. It’s the foundation of how we build products, support customers, and operate as a company. This didn’t happen because AI became fashionable. It happened because we recognize that the companies that integrate AI deepest and earliest will have compounding advantages that competitors can’t easily replicate.

The difference between 2026 and 2025 won’t be more AI tools; rather, it will be which organizations stopped drifting and started building. The strategy you implement now determines which category you’ll be in.

By Volodymyr Kubytskyi, Director of AI at MacPaw