AI Training Is Not the Same as AI Adoption
There is a significant difference between teaching people how to use an AI tool and helping an organisation adopt AI in a meaningful, sustainable way.
A training session can introduce employees to ChatGPT, Microsoft Copilot, Claude, Gemini or another AI platform. It can demonstrate useful prompts, explain key features and give participants an opportunity to experiment.
That training may be valuable.
But training alone does not automatically change the way people work.
After the initial excitement, many employees return to their normal routines. Some use AI occasionally. Others avoid it because they are uncertain about what is permitted, which tools they should use or whether they can trust the output.
A few confident individuals may become highly productive, while the rest of the organisation remains unsure of how AI applies to their roles.
This is why I approach AI training as part of a broader adoption and implementation process rather than as a once-off learning event.
The real challenge is not access to AI
Most organisations no longer need to be convinced that AI matters.
Employees are already hearing about AI, experimenting with free tools or using features that have quietly appeared inside the platforms they work with every day.
The real challenge is turning scattered experimentation into productive, responsible and repeatable working practices.
Organisations need to answer questions such as:
- Where can AI create genuine value in our business?
- Which tools are appropriate for different types of work?
- What information can employees safely use?
- How do we reduce inaccurate or unreliable outputs?
- How do we move from individual experimentation to shared organisational capability?
- How do we measure whether AI is saving time or improving results?
- How do we prevent every employee from developing a completely different way of working?
These are not purely training questions.
They are adoption, workflow, governance, leadership and change-management questions.
Prompting is only the first layer
Prompting remains an important skill.
People need to understand how to give AI clear context, instructions, constraints and output requirements. They also need to know how to refine a response, check assumptions and verify important information.
But effective AI adoption involves more than learning how to write a good prompt.
I view AI adoption as a layered capability.
Layer 1: Prompting skills
Employees need a practical understanding of how to communicate with AI.
This includes providing context, defining the role AI should play, explaining the desired outcome, supplying relevant source material and setting clear criteria for the response.
They also need to understand that prompting is not a single command followed by a perfect result. It is usually an iterative process involving review, refinement and judgement.
Layer 2: Using the full capabilities of AI platforms
Many people use only a fraction of the capabilities available within tools such as ChatGPT, Copilot, Claude and Gemini.
They may type individual prompts into a blank chat window without exploring features such as:
- Projects and workspaces
- Personalisation and memory
- Document and data analysis
- Research capabilities
- Notebooks and knowledge collections
- Shared prompts and templates
- Custom assistants, agents or specialised workflows
- Collaborative pages and reusable resources
These features can turn AI from an occasional question-and-answer tool into a structured working environment.
The objective is not to teach every feature simply because it exists. It is to identify which capabilities are useful for the organisation’s actual work.
Layer 3: Using the AI already embedded in business platforms
AI adoption should not be limited to standalone AI tools.
Many organisations already have AI capabilities embedded within Microsoft 365, Google Workspace, CRM systems, project management platforms, marketing tools, analytics software and other business applications.
Employees may have access to AI-assisted writing, meeting summaries, spreadsheet analysis, research, reporting, customer insights and workflow automation without fully understanding what is available.
A practical AI strategy therefore needs to include the organisation’s existing technology environment.
This is particularly important because the most useful AI solution is not always a separate tool. It may be a capability already available inside the platform where the work is taking place.
Layer 4: Connecting AI across the broader workflow
The greatest value often emerges when different AI capabilities are combined across a workflow.
For example, a team may use AI to:
- Summarise customer feedback.
- Identify recurring themes and risks.
- Turn those findings into a management report.
- Generate recommended actions.
- Create tasks in a project management platform.
- Draft stakeholder communication.
- Track the implementation of agreed actions.
The goal is not simply to complete one task faster.
It is to redesign how information moves through the organisation, how decisions are supported and how repetitive work is managed.
Start with the work, not the tool
One of the most common mistakes in AI training is beginning with a long list of tools and features.
This can be impressive, but it can also overwhelm participants.
A more effective starting point is the work itself.
Before deciding what employees should learn, it is useful to understand:
- Which tasks consume the most time?
- Where are the bottlenecks?
- Which reports are repeatedly created?
- Where is information difficult to find?
- Which tasks rely heavily on copying, formatting or summarising?
- Where do employees repeatedly create similar documents?
- Which processes are delayed because knowledge sits with one person?
- Where are errors, inconsistencies or duplicated effort occurring?
- Which decisions could be improved through better access to information?
This workflow discovery process helps connect AI to real business needs.
It also prevents organisations from adopting AI simply because a tool is popular.
The question becomes less about, “What can this AI tool do?” and more about, “Where could AI improve the way this team currently works?”
Different departments require different approaches
A generic introduction can create a shared foundation, but implementation becomes meaningful when it is connected to specific roles and functions.
A finance team may need help with spreadsheet analysis, variance explanations, document comparison and management reporting.
A marketing team may focus on research, campaign planning, audience insights, content development and performance analysis.
An operations team may use AI for process documentation, incident analysis, project reporting and standard operating procedures.
A leadership team may require decision support, scenario analysis, research synthesis and better ways to interrogate business information.
Human resources may explore policy development, employee communication, onboarding resources and learning support.
The AI capabilities may be similar, but the workflows, risks, data and required outputs are different.
This is why effective implementation usually requires a combination of shared foundational learning and function-specific application.
The importance of reusable AI assets
Another important difference between training and implementation is what remains after the session.
Participants should not have to rely entirely on memory or start from a blank page every time they use AI.
Organisations can build reusable and shareable AI assets such as:
- Prompt libraries
- Workflow templates
- AI playbooks
- Standard research prompts
- Reporting frameworks
- Shared projects or workspaces
- Copilot Pages
- Notebook collections
- Team-specific AI guides
- Verification checklists
- Approved use-case libraries
- Implementation checklists
These assets help employees apply what they have learnt consistently.
They also allow good practices to be shared across teams instead of remaining with a small number of confident users.
Over time, the organisation begins to build its own internal AI intellectual property: not the AI technology itself, but the structured knowledge of how the organisation uses AI effectively.
AI adoption requires confidence and judgement
Employees will not use AI productively simply because they have been told to use it.
They need confidence.
That confidence comes from understanding both the capabilities and the limitations of the technology.
Employees need to know:
- When AI is useful
- When it is not appropriate
- What information should not be entered
- How to verify important outputs
- How to identify weak or fabricated responses
- When human expertise must override the AI
- How accountability remains with the person using the tool
Trust should not mean accepting everything AI produces.
It should mean understanding how to use AI with the correct level of scrutiny.
This is particularly important in areas involving legal information, financial decisions, sensitive employee data, customer information or strategic business decisions.
Leadership and technology teams must be involved
AI implementation cannot sit entirely with the learning and development department.
Leadership must help define priorities and expected outcomes.
Information technology teams need to clarify access, licensing, security, data protection and platform capabilities.
Managers need to support employees as they experiment with new ways of working.
Employees need a safe environment in which to ask questions, practise and learn from mistakes.
Without this alignment, training may create enthusiasm that the organisation is not ready to support.
For example, employees may attend a Microsoft Copilot session only to discover that they do not have the correct licences, features or permissions.
Others may begin using unapproved tools because there is no clear guidance.
A short setup and access review before training can prevent many of these problems.
Implementation happens after the training session
Training may introduce the possibilities, but adoption develops through application.
Employees need opportunities to test AI within their own workflows, receive feedback, refine their approach and share what they have learnt.
This may involve:
- Workflow discovery sessions
- Department-specific workshops
- Guided implementation exercises
- Office hours or support sessions
- Prompt and workflow reviews
- Use-case development
- Internal champions
- Manager check-ins
- Adoption surveys
- Productivity and quality measurements
- Follow-up implementation reviews
The objective is not to make every employee an AI expert.
It is to help employees use AI appropriately and effectively within their responsibilities.
Measure outcomes, not attendance
It is easy to measure how many people attended an AI training session.
It is more useful to understand what changed afterwards.
Possible indicators include:
- Time saved on recurring tasks
- Reduced turnaround times
- Improved reporting quality
- Faster access to information
- Increased consistency across documents
- Reduced duplication of work
- Improved employee confidence
- Number of useful workflows implemented
- Adoption of shared templates and prompts
- Reduction in avoidable administrative work
- New capabilities created within teams
Not every benefit will be immediately financial.
Some improvements may appear as better decision-making, reduced frustration, faster onboarding or greater consistency.
However, organisations should still define what success looks like before implementation begins.
AI adoption is ultimately about better ways of working
The purpose of AI adoption is not to use AI everywhere.
It is not to replace judgement, experience or human relationships.
It is also not to introduce technology for the sake of appearing innovative.
The purpose is to identify where AI can help people work more effectively, make better use of organisational knowledge, reduce unnecessary effort and improve the quality or speed of important work.
Training is an essential part of that journey.
But meaningful adoption requires more than a presentation, a list of prompts or a demonstration of the latest tools.
It requires a structured process that connects people, technology, workflows, governance and measurable business outcomes.
When these elements work together, AI moves beyond experimentation.
It becomes a practical organisational capability.
