AI can accelerate software implementation while meetings, handovers and approvals continue at their previous pace. The next stage of AI transformation is redesigning how work moves through the business, with human judgement and accountability placed at deliberate decision points.
I have spent almost two decades working in software engineering.
For most of that time, implementation was one of the slowest and most expensive parts of software delivery. A substantial feature could take several weeks to build. Requirements, triage, estimation, planning, meetings, reviews and approvals also took time, but that coordination surrounded a large body of implementation work.
The proportions made sense.
Today, I increasingly see AI-assisted implementation compress work that once took weeks into days, hours or, for smaller tasks, minutes.
That has led me to an uncomfortable personal observation:
I am increasingly becoming the bottleneck.
Not because my experience has lost its value. Not because AI is always correct. And certainly not because human judgement is no longer necessary.
I am becoming the bottleneck because AI can research, compare, draft, code, test, document and revise much faster than I can manually supervise every step.
At an individual level, this looks like a productivity improvement.
At an organisational level, it exposes a much larger problem.
The bottleneck has moved
Consider a simplified software delivery process. The timings below are illustrative, not measured benchmarks or delivery promises.
In the past, a piece of work might have required:
Three days of discovery, coordination and approvals, followed by two weeks of implementation.
If AI reduces the implementation from two weeks to three hours, the process becomes:
Three days of discovery, coordination and approvals, followed by three hours of implementation.
Most organisations would report this as successful AI adoption. The developers are faster. The implementation cost has fallen. More work can be completed.
But look at the proportions again.
The three days of coordination have not changed. They are now the slowest and potentially most expensive part of the process.
This is where many organisations appear to be stuck. They are using AI to accelerate tasks while preserving an operating model designed around the speed, capacity and cost of human labour.
Microsoft described this gap directly in its 2026 Work Trend Index:
“In many cases, people are ready. The systems around them are not.”
Microsoft found that organisational factors such as culture, management support and talent practices accounted for twice the reported AI impact of individual effort alone. It also found that only one in four AI users believed their leadership was clearly and consistently aligned on AI, while 45 per cent said it felt safer to focus on current goals than to redesign how work gets done. (Microsoft Work Trend Index)
That is not primarily a technology problem.
It is an organisational design problem.
Most businesses are accelerating the old model
An article published by McKinsey in July 2026 provides an important comparison.
Only 21 per cent of companies surveyed had fundamentally redesigned their operating models around AI. Put differently, almost four out of five had not.
Among the companies producing substantial financial value from AI, defined by McKinsey as attributing at least 5 per cent of EBIT to AI, the pattern was markedly different. These companies were three times more likely to pursue broad operating-model redesign. They were also twice as likely to redesign workflows before selecting AI tools. (McKinsey: The operating-model advantage)
This comparison matters, although the association does not establish that redesign alone caused the financial results.
It suggests that the difference between successful and unsuccessful AI adoption is not simply access to a better model, a larger technology budget or more AI licences.
The more important difference may be whether the organisation is willing to change how work moves through the business.
Most companies still add AI to existing structures:
- a copilot inside an unchanged workflow
- an AI assistant supporting an unchanged management hierarchy
- a coding agent receiving work through the same lengthy triage process
- an AI-generated report passing through the same sequence of meetings and approvals
The task becomes faster, but the workflow does not.
McKinsey describes this as value remaining trapped at the task level. Individual activities accelerate, while information continues to pass through the same organisational bottlenecks. (McKinsey: The operating-model advantage)
This explains why a company can have many employees actively using AI without the company itself becoming noticeably faster.
AI is beginning to address the coordination layer
There is another reason businesses need to look beyond task-level productivity.
AI is not limited to writing code, creating documents or answering questions. Its growing capability includes understanding requests, gathering context, asking clarifying questions, comparing alternatives, routing work, monitoring progress, validating results and coordinating actions across systems.
McKinsey puts this clearly:
“AI is different because it targets the coordination layer itself.”
AI systems can increasingly synthesise information, prioritise actions, forecast outcomes, make bounded decisions and coordinate workflows across teams. That creates the possibility of redesigning entire workflows rather than simply accelerating isolated activities within them. (McKinsey: The operating-model advantage)
Imagine the earlier software-delivery example moving to its next stage.
Instead of three days of human coordination followed by three hours of AI-assisted implementation, much of the initial coordination might eventually be completed by AI in half an hour.
That half-hour figure is not a universal prediction. It is an illustration of the direction in which the capability is moving.
An AI system could receive a customer request, ask targeted questions, examine the existing product, attempt to reproduce the problem, collect logs, identify affected components, draft acceptance criteria, prepare technical documentation and present its understanding to the customer for confirmation.
Only then would a human become involved to assess business priority, risk, contractual implications, scheduling and any decision that genuinely requires human authority.
The workflow might look like this:
Customer request → AI discovery and triage → human prioritisation and risk decision → AI execution and orchestration → human accountability and finalisation
For lower-risk and highly repeatable processes, it may eventually become:
Human intent → AI-managed workflow → human exception handling and outcome ownership
This is not a proposal to remove humans from business.
It is a proposal to place humans where human involvement creates the most value.
Human in the loop should not mean human in every loop
“Human in the loop” remains an important principle, particularly where work involves safety, security, privacy, legal obligations, financial authority or material business risk.
The problem begins when the phrase becomes an unquestioned requirement for human participation at every stage.
A human receives the request.
Another human rewrites it as a ticket.
A third person reviews the ticket.
A fourth person schedules a meeting.
Several people attend the meeting.
Someone documents the discussion.
Another person prepares the work for implementation.
An engineer then explains the task to an AI system.
After the AI completes the work, the process travels back through another sequence of reviews, summaries, demonstrations and approvals.
Some of those controls may be essential. Others may exist only because information historically had to be interpreted and transferred manually between people.
These are not the same thing.
A useful human checkpoint should have a clear reason for existing:
- Does it require human judgement?
- Does it assign accountability?
- Does it manage material risk?
- Is it required by law, regulation or contract?
- Does it protect an important customer relationship?
- Does it handle an exception the system cannot reliably resolve?
If the person is only receiving, summarising, reformatting, forwarding, scheduling or explaining information, that step is increasingly a candidate for AI-mediated coordination.
This does not mean removing governance. AI systems can make mistakes, and McKinsey warns that errors may propagate at machine speed when agentic systems are placed inside workflows that were not designed for them. (McKinsey: The operating-model advantage)
The answer is not uncontrolled autonomy.
The answer is deliberate autonomy within clear boundaries, supported by traceability, testing, security, auditability and human escalation.
We need guardrails. We do not necessarily need a manual shadow process that repeats everything the AI has already done.
The next competitor may be much smaller than you expect
Established businesses naturally compare themselves with other established businesses.
A large software agency watches other large software agencies. A consulting company compares itself with companies that have similar structures, staff numbers and operating costs.
AI changes that comparison.
The next serious competitor may be a very small agency. It may be a team of two or three people. In some cases, it may be one experienced person supported by multiple AI systems and agents.
A small AI-native business can increasingly assemble capabilities across research, customer communication, software development, testing, documentation, contract drafting, design, analysis, marketing and administration.
This does not guarantee quality. AI-native businesses still need expertise, trustworthy processes, security controls and sound judgement.
But they can begin without many of the historical layers that established organisations carry.
They do not necessarily need to reproduce the traditional structure of account management, business analysis, project management, technical leadership, engineering, testing, documentation and administration before they can deliver a result.
They can design the workflow around the outcome from the beginning.
That creates a different type of competitive pressure.
We may not be discussing a competitor that is 1 or 2 per cent cheaper.
The resulting difference in value could be measured in multiples rather than percentage points.
As a competitive scenario rather than a measured result, imagine a service costing one-third or even one-tenth as much in a suitable workflow. This will not apply equally to every industry or every type of work. Physical delivery, heavily regulated activities, complex relationships and high-risk decisions will retain different cost structures.
But a new competitor does not need this advantage everywhere.
It only needs a few valuable areas where it can deliver a comparable or better outcome in days instead of weeks, at a fraction of the traditional cost.
Large organisations still have substantial advantages: customer trust, established relationships, proprietary data, capital, brand recognition and deep institutional knowledge.
But those advantages do not automatically compensate for a slow and expensive operating model.
Smallness itself is not the competitive advantage.
Being designed around AI from the beginning is the advantage.
Productivity gains will become price pressure
An established business may initially use AI to deliver work faster while continuing to charge historical prices.
That is understandable. The business invested in its expertise, reputation, systems and customer relationships. Higher productivity can initially produce a higher profit margin.
But that position is unlikely to remain stable.
McKinsey’s April 2026 analysis argues that productivity improvement alone is unlikely to create a durable competitive advantage. As AI capabilities spread, competition tends to erode productivity gains and transfer more of the benefit to customers through lower prices, faster service or better outcomes. (McKinsey: Where AI will create value)
Eventually, a competitor will use its lower cost base to reduce its price.
Another will offer much faster delivery.
Another will combine a lower price with higher quality and a better customer experience.
At that point, the established company cannot protect its old pricing merely by saying that the work once required several weeks.
The customer is buying the outcome, not the history of how much labour the outcome used to require.
This does not mean every service should become cheap. Expertise, responsibility, risk and business value remain valuable.
It means the market will increasingly challenge organisations whose price is driven primarily by internal coordination costs that competitors have removed.
Bureaucracy is not automatically wrong
It is easy to describe every meeting, approval or management layer as bureaucracy and assume that it should disappear.
That would be careless.
Some organisational processes preserve institutional memory. Some protect privacy and security. Some establish separation of duties. Some provide necessary financial, contractual or regulatory control. Some prevent individuals or automated systems from making consequential decisions without accountability.
Those protections remain important.
The question is not whether an organisation should have bureaucracy.
The question is whether it needs this much bureaucracy, organised in this particular way.
Every additional meeting, handover and approval increases cost. When implementation took weeks, several days of coordination might have been proportionate.
When implementation takes hours, the same process can make coordination more expensive than production.
And as AI becomes capable of performing more coordination itself, organisations will need to distinguish between genuine governance and inherited administrative friction.
What business leaders should do on Monday morning
Do not begin by buying another AI product.
Do not begin by counting licences, prompts, pilots or employees who have completed AI training.
Bring together the people responsible for business strategy, technology, operations, product delivery, risk and customer outcomes.
Select one real, important end-to-end workflow. It could be customer request to delivery, incident to resolution, opportunity to contract, or order to payment.
Map the entire workflow, from the first customer interaction through delivery and finalisation.
Then redesign it as though the company were being created today.
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Define the outcome and the risk boundaries. Be clear about what success means and what the system must never be allowed to do autonomously.
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Decompose the workflow. Separate information transfer, analysis, decisions, execution, validation and accountability.
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Assign each step deliberately. Decide whether it should be AI-operated, human-operated, or AI-operated with human involvement only for exceptions.
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Remove duplicate translation and approval layers. Do not require one person to explain information to another person who then explains it to an AI system.
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Measure the business result. Track total cycle time, coordination cost, quality, failure rates, customer value and financial impact, not merely the number of AI tools deployed.
Start with a domain where the business value is meaningful, the risks are understood and outcomes can be measured.
The objective is not to automate the existing process.
The objective is to determine whether the existing process should continue to exist.
The cost of moving slowly
The urgency is not theoretical.
In a McKinsey survey published in July 2026, conducted in April with 1,205 executives and managers across 94 countries, 40 per cent expected their current business model to require significant change within three years simply to remain economically viable.
Among organisations identified as the slowest movers, that figure increased to nearly 60 per cent. (McKinsey: Why accelerated resource allocation matters)
This does not mean every prediction about AI will be correct.
It does mean that moving slowly is not necessarily the safe option.
The greatest risk may not be adopting AI too aggressively. For some businesses, the greater risk will be preserving an organisation whose cost, speed and structure were shaped by limitations that no longer apply.
AI is becoming faster and more capable with every generation.
The strategic question is no longer simply:
How many of our employees are using AI?
The better question is:
How much of our organisation still exists because information and work once had to move at human speed?
Human judgement will remain important. Human accountability will remain essential. Human relationships will continue to matter.
But human involvement should be placed deliberately, not inherited automatically from the operating model of the past.
The next serious competitor may be smaller than you expect, faster than you think and free from much of the coordination cost you assumed every professional organisation had to carry.
Your AI strategy should be ready for that.
