Understanding the Different Needs of Business Users and AI Builders

The biggest challenge with enterprise AI adoption today is not access to AI tools. Most organisations already have access to powerful AI models and platforms. The real challenge is helping people understand how to use AI effectively, responsibly, and in a way that creates meaningful value. A common misconception is that successful AI adoption means everyone needs to become an AI expert. In reality, different people interact with AI in different ways, and their learning needs are different.

For a business user, the key question is not how the underlying AI model works internally, but how AI can help improve their day-to-day work while ensuring that the outputs can be trusted. Business users need to understand where AI can help, where it can fail, and how to validate the results. AI is particularly valuable in tasks involving repetitive knowledge work, such as summarising information, preparing drafts, analysing content, generating ideas, or restructuring information. It helps reduce the effort required to get started and allows people to spend more time applying their expertise, judgement, and creativity.

However, effective AI adoption also requires understanding its limitations. AI systems can generate responses that are fluent, confident, and well-structured while still being incorrect. A response that sounds convincing is not necessarily a response that is accurate. Therefore, users need to develop the habit of validating AI outputs by checking whether the information comes from a trusted source, whether it is current and relevant, and whether it aligns with their own domain knowledge. The right mindset is not “AI gave me the answer”, but rather “AI gave me a starting point that I need to evaluate.”

For example, if a user asks an AI assistant how to onboard a data product into an internal data quality platform, the AI assistant may retrieve information from documentation, FAQs, previous discussions, and adoption guides to provide a quick response. This can significantly reduce the time spent searching across multiple sources. However, the user still needs to apply judgement to determine whether the answer applies to their specific data product, business context, and requirements. The value comes from combining AI speed with human expertise.

The role of an AI builder is different. An AI builder is not simply using AI capabilities; they are responsible for creating AI systems that others can rely on. Their questions are deeper: How does the model behave? How do we provide the right context? How do we measure whether the system is working correctly? How do we ensure security, governance, and reliability?

Understanding model behaviour is the starting point. AI models are very capable at tasks such as summarisation, classification, transformation, pattern recognition, and generating responses based on available context. However, they do not automatically know an organisation’s internal knowledge, policies, processes, or decisions. A general-purpose AI model may understand language very well but still provide an incorrect answer when asked about company-specific processes.

This is where approaches such as retrieval-augmented generation become important. Instead of expecting the model to know everything, AI builders design systems where the model can access trusted organisational knowledge before generating a response. The process becomes: understand the user’s question, retrieve relevant information, provide that context to the AI model, and then generate a response grounded in organisational knowledge. This transforms AI from a general-purpose assistant into a domain-aware capability.

However, providing access to knowledge alone is not enough. AI builders also need to think about evaluation. A user may ask, “Does this AI assistant work?” but an AI builder needs to ask, “How do we know it works consistently?” This requires measuring whether the right information was retrieved, whether the generated response is accurate, whether users find it useful, and whether the system operates within security and governance expectations.

Architecture decisions also become important because not every problem requires the same AI approach. A simple task such as rewriting an email may only require direct interaction with a language model. A company knowledge assistant may require retrieval-based approaches because it needs access to internal information. More complex workflows involving multiple systems, decisions, and actions may require agent-based approaches with tools, automation, and human approvals.

The important point is that the mistake is not building agents. The mistake is assuming that agents alone create intelligence. Reliable AI capabilities emerge when organisations combine strong foundations: trusted data, accessible knowledge, appropriate AI models, evaluation mechanisms, governance, and human oversight.

Successful AI adoption therefore requires collaboration between business users and AI builders. Business users bring domain expertise, context, and judgement. AI builders create the technical foundations that make AI reliable, scalable, and trustworthy. Neither side can create sustainable AI value alone.

The future of enterprise AI will not belong to organisations that simply deploy the most AI tools or build the most autonomous agents. It will belong to organisations that understand where AI creates value, where human judgement remains essential, and how to design effective collaboration between humans and machines. The ultimate goal is not replacing human intelligence with artificial intelligence, but creating augmented intelligence where AI removes friction, accelerates knowledge work, and enables people to focus on the decisions and problems where human expertise matters most.

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