I’ve been using AI since the early days of ChatGPT. Like many people in technology, I was immediately curious when generative AI first became widely available. What started as simple experimentation quickly became part of my daily routine. Initially, I used AI to answer questions, summarize information, and draft content. Over time, however, I realized its potential extended far beyond those basic tasks.
Over the past few years, AI has become an integral part of how I work. I’ve used it to build presentations, draft policies, structure business documents, review information, accelerate research, and learn new concepts more efficiently. I’ve experimented with multiple platforms, created AI assistants for specific business needs, and worked with teams across different functions to identify practical ways AI can create measurable value. Today, it’s difficult to imagine my professional life without it.
Yet after years of working closely with AI, I’ve come to a somewhat unexpected conclusion: the biggest challenge isn’t learning how to use AI. The real challenge is understanding where it belongs. While many organisations continue to focus on AI tools and capabilities, the more important conversation is about how AI fits into business processes, decision-making, and everyday operations. In my experience, organisations achieve the greatest value not when employees use AI occasionally, but when AI becomes a natural and seamless part of how work flows across teams and functions.
The Evolution of AI Adoption
When generative AI first emerged, most discussions centered on tools. Businesses wanted to know which platform was best, which model produced the most accurate results, and what prompts generated the best outputs. At the time, these questions were completely valid. Organisations and individuals alike were trying to understand the capabilities and limitations of a technology that seemed to evolve almost weekly.
Today, however, the conversation is beginning to mature. Most organisations have already started experimenting with AI in some form. Employees use it to draft emails, summarize meetings, analyze data, generate content, and support decision-making. According to McKinsey’s Global Survey on AI, more than 70% of organisations now report using AI in at least one business function, while Microsoft’s Work Trend Index found that nearly 75% of knowledge workers globally are already using AI at work. These figures illustrate an important reality: access to AI is no longer the primary challenge.
The next challenge is integration. The key question businesses now face is how to move beyond individual productivity gains and embed AI into the way work actually happens. This is where many organisations currently find themselves. The opportunity is no longer simply about adopting AI technologies, but about redesigning workflows so AI can improve efficiency, enhance decision-making, reduce repetitive work, and support employees while maintaining appropriate governance and oversight.
There Is No Single AI Solution
One of the most important lessons I’ve learned is that there is no universal AI platform capable of serving every need across an organisation. Different teams have different objectives, workflows, and operational requirements. As a result, different forms of AI deliver value in different contexts.
For software developers and engineering teams, specialised AI tools often provide the greatest benefits because they integrate directly into coding environments, testing frameworks, and development workflows. In contrast, teams in finance, operations, human resources, sales, and administration often gain more value from AI embedded within their everyday productivity tools. These teams typically need support with communication, reporting, analysis, planning, documentation, and knowledge management rather than software development.
Even in my own role, I regularly switch between multiple AI models depending on the task at hand. Some are particularly effective at research and information gathering, while others excel at reasoning, content creation, data analysis, or exploring complex business scenarios. Early in my AI journey, I spent considerable time comparing tools and evaluating features. Today, I spend far more time thinking about workflows.
The organisations seeing the greatest return on their AI investments are often not those with the most sophisticated platforms. Instead, they are the ones that successfully integrate AI into existing business processes, reduce friction for employees, and focus on solving genuine business problems. The most important question is rarely, “Which AI platform should we use?” Rather, it is, “What business outcome are we trying to achieve?”
When organisations begin with the desired outcome rather than the technology itself, AI becomes more than a productivity tool. It becomes a business enabler. That shift in perspective is what transforms AI from an interesting innovation into a strategic capability capable of delivering meaningful, measurable, and sustainable business results.
