From AI Adoption to AI Transformation: What Organisations Need to Do Next
Artificial intelligence has moved rapidly from an emerging technology to a practical business tool. Organisations across industries are now experimenting with generative AI, automation and AI-powered agents to improve productivity, support employees and change how work is delivered.
However, adopting an AI tool is not the same as transforming an organisation.
An employee using ChatGPT to draft an email is AI adoption. Redesigning the entire customer-service process around human employees working alongside AI systems is closer to AI transformation.
This distinction is becoming increasingly important. McKinsey's 2025 global survey found that 88% of respondents reported that their organisations regularly use AI in at least one business function, yet only 7% said AI had been fully scaled across their organisations (McKinsey & Company, AI at work but not at scale 2025).
The challenge for organisations is therefore shifting from:
“How can we use AI?”
to:
“How can we redesign our organisation to create lasting value from AI?”
AI Adoption Is Growing, But Transformation Remains Limited
The growth of AI adoption has been substantial.
McKinsey's 2025 State of AI survey found that 88% of respondents reported regular AI use in at least one business function, compared with 78% in 2024. However, the same research found that only 7% reported that AI had been fully scaled across their organisations (McKinsey & Company, AI at work but not at scale 2025).
This suggests a significant gap between experimentation and organisational integration.
AI adoption vs AI transformation
AI adoption and organisational scaling
McKinsey survey results showing the difference between reported AI use and organisations reporting that AI is fully scaled.
The difference is important. An organisation can have hundreds of employees using AI tools without having changed its underlying processes, governance structures or operating model.
In fact, McKinsey found that only 21% of respondents whose organisations were using generative AI said their organisations had fundamentally redesigned at least some workflows. The research also identified workflow redesign as the organisational attribute with the strongest relationship to reported EBIT impact from generative AI (McKinsey & Company, The state of AI: Global Survey 2025).
AI transformation therefore requires more than giving employees access to new technology. It requires organisations to reconsider how work gets done.
1. Redesign Workflows, Not Just Tasks
One of the most common approaches to AI adoption is to take an existing task and add an AI tool to it.
For example:
Existing process → Employee completes task → AI assists employee
This can create efficiency improvements, but it may leave the wider process unchanged.
A transformation-focused approach starts differently:
Existing process → Identify bottlenecks → Identify AI opportunities → Redesign workflow → Implement AI → Measure results
Consider a customer-service department.
Instead of simply giving employees an AI chatbot to help write responses, an organisation could redesign the entire customer-service workflow.
AI could potentially:
Classify incoming enquiries
Retrieve relevant information
Draft a response
Identify complex or sensitive cases
Escalate those cases to an employee
Record the interaction
Analyse recurring customer issues
The employee's role then changes from manually processing every enquiry to supervising, resolving exceptions and handling higher-value interactions.
This is the difference between adding AI to a process and redesigning the process around AI.
McKinsey's research supports this distinction, identifying workflow redesign as particularly important for organisations seeking measurable value from generative AI (McKinsey & Company, The state of AI: Global Survey 2025).
2. Develop People Alongside the Technology
Technology alone cannot deliver organisational transformation.
Employees need the knowledge and confidence to use AI effectively, understand its limitations and determine when human judgement is required.
This is becoming increasingly important as the skills required by organisations continue to change.
The World Economic Forum's Future of Jobs Report 2025, based on responses from more than 1,000 companies, estimates that 39% of workers' existing skill sets will be transformed or become outdated between 2025 and 2030. It also found that 63% of employers identify skills gaps as the biggest barrier to business transformation (WEC. 2025).
Expected change in workers' core skills
Share of workers' core skills expected to change
Employer estimates of the share of workers' core skills expected to change over the following five years, across editions of the World Economic Forum Future of Jobs Survey.
The changing skills landscape means organisations cannot treat AI training as a one-off technical exercise.
Employees may need development in areas such as:
AI literacy
Data interpretation
Critical thinking
Prompt and workflow design
Digital skills
Cybersecurity awareness
Problem-solving
Leadership
Communication
Human-AI collaboration
Importantly, the World Economic Forum also expects human capabilities such as analytical thinking, resilience, leadership and collaboration to remain important alongside rapidly growing technology skills (WEC, 2025).
The implication is that AI transformation is not simply about developing technical AI skills. It is about developing a workforce capable of combining technology with human judgement.
3. Create an AI-Ready Operating Model
Once AI becomes embedded across an organisation, traditional organisational structures may need to evolve.
AI implementation raises questions such as:
Who is responsible for AI decisions?
Who approves new AI applications?
Who manages AI-related risks?
Which data can AI systems access?
Which decisions must remain under human control?
Who monitors AI performance?
How should employees report problems?
These questions need to be addressed before AI becomes deeply embedded into critical business processes.
McKinsey's 2025 research found that organisations are already developing different approaches to AI governance. Risk, compliance and data governance are frequently managed centrally, while technology talent and AI adoption are more likely to use hybrid models involving both central and departmental teams (McKinsey & Company, The state of AI: Global Survey 2025).
This suggests that AI transformation requires an organisational structure capable of balancing central oversight with practical implementation within individual departments.
4. Move From AI Experiments to Measurable Business Outcomes
Another challenge is determining whether AI investment is actually creating value.
It is easy to measure AI adoption:
“70% of employees have access to an AI tool.”
But access does not necessarily equal business value.
Organisations should instead consider metrics such as:
Productivity
Hours saved per employee
Tasks completed per employee
Process cycle time
Financial performance
Operating costs
Revenue generated
Cost per transaction
Customer performance
Response times
Customer satisfaction
Resolution rates
Quality
Error rates
Rework
Compliance issues
Workforce development
AI training completion
Employee AI proficiency
Internal adoption rates
For example, instead of reporting:
“Our employees use AI regularly.”
An organisation could report:
“AI reduced average report preparation time from four hours to 90 minutes.”
The second measurement connects AI directly to an operational outcome.
This is where organisations can begin moving from technology investment to measurable transformation.
5. Prepare for the Growth of AI Agents
The next phase of AI adoption is likely to involve more than traditional chatbots and generative AI assistants.
AI agents are increasingly being designed to perform sequences of tasks, interact with systems and support workflows with greater autonomy.
Microsoft's 2025 Work Trend Index, based on research involving workers and organisational data, found that 82% of leaders considered 2025 a pivotal year to rethink core aspects of strategy and operations. Leaders also expected their teams to begin training and managing AI agents within five years (The 2025 Annual Work Trend Index: The Frontier Firm is born - The Official Microsoft Blog, 2025).
Microsoft's 2026 Work Trend Index subsequently surveyed 20,000 AI-using workers across 10 countries and analysed Microsoft 365 productivity signals to examine how AI agents are changing work (2026 Work Trend Index Report: Agents, Human agency, and opportunity, Microsoft Blog, 2025).
This points towards a future where organisations may increasingly operate through human-AI teams.
That does not necessarily mean replacing employees.
Instead, organisations may divide work differently:
AI systems
Process large amounts of information
Perform repetitive tasks
Monitor workflows
Generate drafts
Analyse patterns
Execute defined processes
People
Set objectives
Make complex decisions
Manage relationships
Provide judgement
Handle exceptions
Take responsibility for outcomes
The key question for organisations will therefore be how effectively they can design these human-AI relationships.
6. Address the Skills Gap Before Scaling AI
Scaling AI without preparing employees can create another problem: technology adoption can move faster than workforce capability.
IBM's Global AI Adoption Index found that, among the large organisations surveyed, 42% had actively deployed AI, while another 40% were exploring or experimenting with AI. Among the barriers reported, 33% identified limited AI skills and expertise, while 25% cited data complexity and 23% cited ethical concerns (Data suggests growth in Enterprise Adoption of AI is due to widespread deployment by early adopters, IBM Newsroom, 2024).
Reported barriers to AI deployment
Reported barriers to AI deployment
Selected barriers reported by large organisations surveyed in the IBM Global AI Adoption Index 2023.
These figures highlight an important point: AI transformation is partly a people and organisational challenge.
Organisations may have access to sophisticated technology, but without the skills to implement, manage and use it effectively, the potential value of that technology can remain unrealised.
7. Build a Continuous AI Transformation Strategy
AI transformation should not be treated as a single project with a fixed end date.
AI capabilities will continue to develop, business processes will change and employees will need to continually update their skills.
A practical transformation strategy can be structured around five stages:
1. Assess
Understand where the organisation currently uses AI, where employees need support and which processes create the greatest opportunities.
2. Prioritise
Identify AI opportunities according to potential business value rather than simply adopting technology because it is new.
3. Prepare
Develop employee skills, establish governance and ensure that data, technology and processes are ready.
4. Transform
Redesign workflows and integrate AI into the organisation's operating model.
5. Measure and Improve
Track productivity, financial, customer and workforce outcomes and use those results to continuously improve the approach.
This creates a cycle:
Assess → Prioritise → Prepare → Transform → Measure → Improve
Rather than:
Buy AI → Deploy AI → Hope for results
The Real Value of AI Transformation
AI adoption is already becoming widespread, but the data suggests that widespread use does not automatically mean widespread transformation.
McKinsey's research shows that AI use has become much more common, while full organisational scaling remains limited (AI at work but not at scale; (McKinsey & Company, 2025).
At the same time, the World Economic Forum's research indicates that skills requirements are changing rapidly, with skills gaps remaining a major barrier to organisational transformation (WEC, 2025).
The organisations that move beyond experimentation will need to consider more than technology.
They will need to consider people, processes, leadership, governance, skills and measurement.
AI transformation is therefore not simply an IT project.
It is an organisational transformation project.
The organisations preparing for this shift should ask four fundamental questions:
Are our processes ready for AI?
Are our employees ready to work with AI?
Do we have the governance needed to use AI responsibly?
Can we measure whether our AI investments are creating business value?
Answering these questions can help organisations move from simply using AI to fundamentally changing how work gets done.
Conclusion
The next stage of AI will not be defined solely by which organisations have access to the newest tools.
It will increasingly depend on how effectively organisations integrate those tools into their people, processes and strategies.
AI adoption provides the technology.
Transformation comes from what an organisation does with it.
For organisations looking to move forward, the priority should be clear: develop the workforce, redesign the right processes, establish appropriate governance and measure outcomes that matter to the business.
The organisations that treat AI as an organisational transformation challenge, rather than simply another piece of technology, will be better positioned to understand and capture its potential.
References
McKinsey & Company (2025). The State of AI: How Organizations Are Rewiring to Capture Value.
McKinsey & Company (2025). AI at Work but Not at Scale.
World Economic Forum (2025). The Future of Jobs Report 2025.
World Economic Forum (2025). Future of Jobs Report 2025: 78 Million New Job Opportunities by 2030 but Urgent Upskilling Needed to Prepare Workforces.
Microsoft (2025). 2025 Work Trend Index: The Frontier Firm Is Born.
Microsoft (2026). 2026 Work Trend Index: Agents, Human Agency, and the Opportunity for Every Organization.
IBM (2024). Global AI Adoption Index 2023.
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