As of late 2025, 88 percent of organizations are using AI in at least one business function, a sharp rise from 55 percent in 2023. Yet beneath this rapid adoption lies a more sobering reality. Only 7 percent of companies have fully scaled AI across their organizations, while 62 percent remain stuck in experimentation or pilot phases. For most businesses, AI usage is reactive rather than intentional, driven by tool availability or hype rather than a clear strategic vision.
This gap between adoption and impact has created a state of AI drift. Companies are using AI, but few are building the foundations needed to turn it into a durable competitive advantage.
AI Is Not a Passing Trend
Some organizations still hope that the current AI wave will slow down or fade. Evidence suggests the opposite. AI is becoming embedded across industries, reshaping how work is done and how value is created. According to Gartner, organizations with sustained AI-first strategies are expected to achieve 25 percent better business outcomes by 2028 compared with their peers.
As 2026 approaches, the window to move from experimentation to execution is narrowing. AI will reshape every sector. The only question is whether companies will actively shape that transformation or be forced to react to it.
Why an AI Strategy Can’t Wait
Many organizations delay building an AI strategy because they don’t see immediate use cases, worry about risk, or assume AI is relevant only to technical teams. In reality, AI is already deeply embedded in non-technical fields. In healthcare, AI systems are outperforming human specialists in stroke analysis and detecting epilepsy-related brain lesions. In education, more than half of higher education institutions are prioritizing AI to accelerate digital transformation and personalized learning.
Crucially, most organizations are already using AI without formal oversight. Employees routinely rely on AI tools to draft documents, analyze data, or write code. Without a strategy, this creates two major risks. First, unmanaged AI use introduces compliance, security, and data privacy issues, along with low-quality output that appears polished but lacks substance. Second, employees who could benefit from AI avoid it due to uncertainty or fear of being replaced.
A clear strategy addresses both problems by aligning AI use with value creation while setting clear boundaries and expectations.
Three Strategic Imperatives for 2026
- Treat AI as Infrastructure
The most common mistake organizations make is treating AI as a special innovation project rather than core infrastructure. When AI is isolated in centers of excellence, it remains peripheral. Instead, AI should become as routine as email or internal messaging tools. Leaders should proactively identify processes where AI can handle repetitive tasks, freeing people to focus on creative, strategic, and high-impact work. The goal is not impressive demos, but invisible integration into everyday workflows.
- Set Baseline AI Competency Standards
Just as modern teams expect engineers to understand version control, they must now expect baseline AI literacy. This applies beyond engineering. Employees need to understand where AI adds value, how to prompt it effectively, how to validate its output, and when human judgment must override it. These competencies should be reflected in hiring, onboarding, training, and promotion criteria. Without standards, organizations risk creating internal productivity gaps between AI-enabled employees and everyone else.
- Build Guardrails Before Incidents Occur
AI introduces real risks, from data leakage to copyright violations and hallucinated outputs presented as facts. Addressing these risks after an incident is costly and damaging. Proactive organizations establish clear policies around approved tools, data usage, output verification, and accountability. Well-designed guardrails do not restrict AI use. They enable safe, confident adoption at scale.
Building Instead of Chasing
Becoming AI-first requires uncomfortable change, but the payoff compounds over time. At MacPaw, AI is treated not as an add-on but as foundational infrastructure across products, customer support, and operations. According to Volodymyr Kubytskyi, this approach is driven by the belief that early and deep AI integration creates advantages competitors struggle to replicate.
The defining difference between 2025 and 2026 will not be the number of AI tools available. It will be which organizations stopped drifting and started building. The strategies put in place today will determine who leads in the AI-shaped economy of tomorrow.
