AI Readiness: What Businesses Need to Get Right

Artificial intelligence has become considerably more accessible to businesses. Tasks that once required specialist software or technical teams can now be performed using commercially available tools. Employees can draft documents, analyse information, summarise meetings, write code and support customer interactions with relatively little technical preparation.

The speed of adoption is remarkable. The more difficult question is whether organisations are equipped to turn these capabilities into sustained improvements in business performance.

The OECD's 2025 study Generative AI and the SME Workforce, based on a representative survey of more than 5,000 SMEs across seven countries, found that 31% were already using generative AI. The research also identified benefits in employee performance and skills, alongside differences in adoption across sectors and organisations.

A subsequent OECD survey published in April 2026 found continued growth in AI adoption among SMEs, but uneven progress in integrating AI securely and strategically into business operations.

This distinction deserves attention.

An employee using AI to complete an individual task more quickly does not necessarily mean the business is delivering services faster, improving profitability or increasing customer satisfaction.

The value depends on what happens before and after that task, how the output is checked and whether the organisation changes its working practices to make use of the additional capacity.

For leadership teams, AI readiness is therefore a question of operational capability, investment discipline and organisational judgement.

The difference between AI use and business value

There is credible evidence that AI can improve productivity in specific working environments.

A study by Erik Brynjolfsson, Danielle Li and Lindsey Raymond, published in The Quarterly Journal of Economics in 2025, examined the introduction of an AI assistant among more than 5,000 customer-support agents.

The researchers found an average productivity improvement of approximately 15%, measured through customer issues resolved per hour.

The gains were not evenly distributed. Less experienced and lower-skilled workers benefited more substantially, while highly experienced workers saw smaller improvements and, in some cases, slight reductions in output quality.

These findings are important for two reasons.

First, they demonstrate that AI can produce measurable gains in real business environments rather than only in laboratory experiments.

Second, they show why organisations should avoid assuming that the same technology will produce identical results across employees, tasks or departments.

The customer-support study examined a particular application, workforce and operating environment. Its results should not be treated as a universal forecast for AI investment.

A separate 2025 field experiment involving 7,137 knowledge workers across 66 firms found that employees who used an AI tool integrated into their work applications spent less time on email. However, researchers did not detect broader changes in the quantity or composition of tasks resulting from individual access to the technology.

Together, these studies point to an important management issue: individual time savings and organisation-wide productivity improvements are not the same outcome.

If AI reduces the time required to prepare a proposal, but the proposal continues waiting several days for internal approval, the customer may experience little improvement.

If a customer-support employee resolves more enquiries but difficult cases still require repeated escalation, the organisation may retain its existing service bottlenecks.

The business case must account for the complete process.

1. Start with the work, not the technology

Many AI initiatives begin with a demonstration of what a tool can do.

A product can generate a polished report, analyse a spreadsheet or respond to customer questions. The demonstration is compelling, and the organisation begins looking for places to deploy it.

This approach can produce useful experiments. It is less reliable as a basis for substantial investment.

A stronger starting point is to identify an existing business problem.

Consider a professional services company that spends considerable time preparing client proposals.

The problem may appear to be slow document production. But closer examination might reveal that employees spend relatively little time drafting and considerably more time gathering requirements, locating previous project information, estimating costs and obtaining commercial approval.

An AI writing assistant may accelerate the drafting stage without addressing the larger causes of delay.

A proper assessment would examine the entire proposal process:

• Where does information originate?
• Which activities require specialist judgement?
• Which steps are repetitive or rules-based?
• Where do delays and errors occur?
• Which information can safely be made available to an AI system?
• How will output quality be assessed?

The answers may support an AI solution, a conventional workflow automation, improved information management or a combination of approaches.

Not every operational problem requires artificial intelligence.

For straightforward, deterministic tasks, conventional software rules may be cheaper, more predictable and easier to maintain.

AI becomes particularly relevant where the work involves interpreting unstructured information, identifying patterns, generating content or supporting decisions that cannot be fully specified through simple rules.

The choice should follow the requirements of the task.

2. Data quality determines what AI can reliably support

Businesses frequently describe data as an asset. In practice, operational information may be dispersed across spreadsheets, email accounts, shared folders and different software applications.

Records can be incomplete, inconsistent or outdated.

These weaknesses become more consequential when AI systems use that information to produce summaries, recommendations or automated actions.

Imagine an organisation introducing an AI assistant to help employees answer customer questions about service agreements.

If contract versions are inconsistent, amendments are stored separately and customer-specific conditions are not clearly identified, the assistant may retrieve information that is incomplete or no longer applicable.

The problem is not necessarily the model's ability to generate language. It is the reliability of the information available to it.

Before deploying AI against internal business data, organisations should examine four practical questions.

Is the information accurate?

Records need sufficient accuracy for the intended use. A tool supporting general document discovery may tolerate some inconsistencies; one supporting financial calculations or contractual decisions may not.

Is the information current?

Outdated pricing, superseded policies and expired contractual terms can produce incorrect recommendations even when the AI system retrieves information successfully.

Is the information accessible to the right people?

An AI interface should not allow employees to obtain information they would otherwise be prohibited from accessing.

Existing access permissions and confidentiality requirements need to remain effective.

Can the organisation trace important outputs?

Where AI contributes to a material decision, employees may need to identify the underlying documents, assumptions and calculations.

For many SMEs, the immediate priority is not building a large data platform. It is establishing reliable information ownership, consistent records and appropriate access controls.

These improvements benefit the business regardless of whether it subsequently expands its AI use.

3. Workflow redesign is where larger gains may emerge

McKinsey's March 2025 global survey examined organisational practices associated with financial value from generative AI.

Among the 25 attributes studied, workflow redesign had the strongest association with reported EBIT impact from generative AI.

The survey found that 21% of respondents whose organisations used generative AI reported that at least some workflows had been fundamentally redesigned.

These are survey findings, not proof that redesign alone causes improved profitability. Nevertheless, they reinforce a practical distinction between adding a tool to an existing process and changing how the process operates.

Consider a customer onboarding workflow.

An organisation might initially use AI to draft welcome emails or summarise documents. Employees complete those tasks faster, but customer onboarding still involves repeated information requests and manual status checks.

A more substantial redesign might establish a structured information intake, automatically identify missing documents, route exceptions to the appropriate team and provide employees with a consolidated view of the customer's progress.

AI could support particular activities within that workflow, while conventional software manages validation, routing and record updates.

The improvement comes from the design of the complete process.

For businesses considering AI-enabled workflows, three distinctions are useful.

Assistance: AI helps an employee complete a task while the employee remains responsible for the output.

Automation: A defined task is completed with limited human intervention, subject to agreed controls.

Decision support: AI provides information, analysis or recommendations that inform a human decision.

These arrangements require different levels of testing, oversight and operational integration.

An organisation should establish which model is appropriate before selecting the technology.

4. Employee capability is a business requirement

AI readiness is often discussed in terms of infrastructure and software.

The skills of the people using the systems deserve equal attention.

The OECD's June 2026 policy brief AI and Skills: What We Know So Far identifies skills shortages as a significant barrier to AI adoption, particularly among SMEs.

The OECD reports that more than half of SMEs not using generative AI identify a lack of relevant skills as a barrier.

This does not mean every organisation needs to recruit AI engineers or data scientists.

Most employees require a more practical combination of capabilities.

They need to understand what a tool can do, recognise when its output may be unreliable, protect confidential information and exercise judgement when reviewing results.

Managers require additional skills.

They must identify suitable applications, define acceptable performance, allocate responsibilities and determine when employees should intervene.

Technical specialists may be needed for integration, system configuration, security and more complex applications.

These are different training requirements and should not be treated as a single organisation-wide course.

For example, a marketing employee using AI to prepare a first draft needs guidance on accuracy, originality and brand requirements.

A finance employee using AI to analyse management information needs to understand source reliability, calculation verification and confidentiality.

A manager introducing an AI-supported customer workflow needs to understand how performance, escalation and accountability will operate.

Effective training should reflect these differences.

Why informal experimentation is insufficient

Employees often discover useful AI applications independently.

That experimentation can be valuable, particularly in smaller organisations where formal innovation budgets are limited.

However, informal use can also create inconsistencies.

Employees may rely on different tools, apply different standards for checking outputs or be uncertain about what information can be shared.

A proportionate organisational approach would provide:

• A clear list of approved tools and permitted uses
• Practical guidance on confidential and personal information
• Examples of tasks requiring additional verification
• Training relevant to employees' actual responsibilities
• A way for teams to share successful applications and report problems

The objective is to make experimentation productive and responsible, rather than discourage it.

5. The economics of AI require more than a licence-cost calculation

AI tools can appear inexpensive because initial subscription fees are relatively modest.

The full cost of an operational deployment may be considerably higher.

Businesses should account for implementation, integration, data preparation, employee training, testing, security controls, monitoring and ongoing maintenance.

More sophisticated systems may also incur usage-based charges, including costs associated with model requests, document processing and external services.

These expenses matter when comparing AI with alternative solutions.

Suppose a business spends ₹5 lakh implementing an AI-assisted document-processing workflow.

If the tool saves employees 20 hours per week, management still needs to determine whether that saving produces measurable economic value.

Can employees use the released capacity to handle additional work? Does it reduce overtime or external expenditure? Does it improve customer response times or reduce errors?

If the organisation cannot use the saved time productively, the financial benefit may be smaller than the headline efficiency gain suggests.

The business case should therefore distinguish between:

Time saved: Reduction in effort required for a task.

Capacity released: Additional work that existing employees can realistically undertake.

Costs avoided: Expenditure the organisation would otherwise incur.

Revenue or service impact: Improvements in throughput, customer experience or commercial outcomes.

These measures should not be treated as interchangeable.

A successful pilot may demonstrate time savings without yet establishing a positive financial return.

That is not necessarily a reason to abandon the initiative. It is a reason to be precise about what has been achieved and what remains to be demonstrated.

6. Testing needs to reflect real operating conditions

AI systems can produce convincing outputs even when the underlying information is incorrect.

Their performance can also vary depending on the type of request, the available context and the way the task is presented.

A successful demonstration using a handful of carefully selected examples is therefore insufficient evidence for a production deployment.

Testing should include representative business cases.

For an AI assistant supporting internal policy enquiries, the organisation might evaluate whether it:

• Identifies the correct policy version
• Provides answers supported by approved documents
• Recognises questions outside its available information
• Avoids exposing restricted material
• Refers uncertain or sensitive cases to an authorised employee

Performance should be evaluated against a baseline, such as the accuracy and speed of the existing manual process.

The organisation also needs to decide what constitutes an acceptable result.

A drafting assistant can tolerate errors that employees are expected to correct. A system influencing payroll, contractual obligations or eligibility decisions may require substantially stricter controls.

The appropriate standard depends on the consequences of failure.

This is where domain expertise becomes essential.

Employees who understand the business process must help design the tests and assess the outputs. Technical performance alone cannot establish whether a system is suitable for operational use.

7. Accountability must remain clear as AI takes on more work

As AI systems become more capable, businesses may use them to coordinate activities, interact with software and execute multi-step processes.

Agentic AI applications can extend beyond generating information to initiating actions.

That creates new operational possibilities, but it also increases the importance of permissions, approval thresholds and monitoring.

Consider an AI-enabled procurement workflow.

The system might identify a replenishment requirement, compare approved suppliers and prepare a purchase order.

Allowing it to complete a purchase without human review introduces a different level of financial and operational exposure.

The organisation must decide which actions the system can perform independently, which require approval and what should happen when information is incomplete or unusual.

Useful controls include limits on transaction values, restricted access to sensitive systems, records of actions taken and clear escalation routes.

Responsibility should also be assigned.

Who owns the business process? Who approves changes to the system? Who reviews errors or complaints? Who can suspend its operation if unexpected behaviour occurs?

These are ordinary management questions applied to a new form of technology.

Businesses do not need an unnecessarily complex governance structure for every AI application. They do need controls proportionate to the system's purpose and potential consequences.

For organisations operating in regulated sectors, additional legal and compliance requirements may apply.

8. Choosing the right starting point

An organisation with limited resources cannot investigate every potential AI application simultaneously.

Prioritisation is necessary.

A practical assessment can consider five dimensions:

A useful first project often combines a clear business problem, accessible information, measurable outcomes and manageable consequences if something goes wrong.

Examples might include internal knowledge retrieval, first-draft document preparation or support for routine service enquiries.

More consequential applications, such as autonomous financial transactions or decisions affecting employees and customers, require additional scrutiny.

The most visible use case is not always the most valuable one.

9. A 90-day approach to assessing AI readiness

For an SME beginning to formalise its AI strategy, a focused 90-day programme can provide useful evidence without requiring a large transformation budget.

Days 1–30: Establish the baseline

Identify three to five business processes where employees report substantial manual effort, delays or recurring quality problems.

For each process, document the existing workflow, available information, responsible teams and current performance.

Review any AI tools employees are already using and establish basic information-security expectations.

Select one or two applications for further assessment.

Days 31–60: Test in a controlled environment

Introduce a limited pilot with clear responsibilities and measurable objectives.

Use representative cases rather than only successful demonstrations.

Record implementation effort, employee feedback, output accuracy, time savings and exceptions.

Where relevant, compare the AI-supported workflow with a conventional automation or process improvement alternative.

Days 61–90: Decide what to continue

Evaluate the results against the baseline.

Determine whether the application has improved the business process, whether the benefits justify the costs and what additional controls would be needed for wider use.

The decision may be to scale the application, modify it, continue testing or discontinue it.

Each outcome can be useful if the organisation has collected reliable evidence.

The purpose of the exercise is to establish investment discipline and operational learning, not to demonstrate that AI must be deployed everywhere.

10. AI readiness should evolve with the business

An organisation's ability to use AI will change as its processes, data and workforce develop.

A business may initially benefit from simple employee-assistance tools. Later, it may identify opportunities for integrated workflows, more advanced analytics or carefully controlled automation.

The appropriate sequence depends on business priorities.

There is little value in developing sophisticated AI capabilities if the organisation cannot maintain reliable operational data or evaluate the results.

Equally, businesses should not assume they need perfect systems before beginning any experimentation.

A limited, well-controlled pilot can reveal where improvements are necessary.

Leadership should periodically review which applications are delivering value, which remain experimental and whether new capabilities justify further investment.

AI readiness is not a certification that an organisation achieves once. It is a continuing ability to assess opportunities, manage implementation and adapt working practices as the technology changes.

Conclusion: Readiness is demonstrated through results

AI is already capable of supporting meaningful improvements in many business activities.

Research shows measurable productivity gains in particular environments, while broader surveys indicate that adoption is advancing faster than consistent integration into business operations.

For business leaders, the central challenge is to distinguish technological capability from economic value.

That requires attention to data quality, workflow design, employee skills, implementation costs, testing and accountability.

A business does not become AI-ready by purchasing a particular platform or increasing the number of employees using AI tools.

It becomes better prepared when it can identify suitable applications, test them against real operational requirements and make informed decisions about where further investment is justified.

The strongest AI strategy is one that the organisation can explain in business terms: what problem it is solving, how success will be measured and who is responsible for the outcome.

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References

1. OECD (2026). Empowering SMEs in the Age of AI: The 2026 OECD D4SME Survey.

2. OECD (2026). AI and Skills: What We Know So Far.

3. OECD (2025). Generative AI and the SME Workforce: New Survey Evidence.

4. Brynjolfsson, E., Li, D. and Raymond, L. (2025). Generative AI at Work. The Quarterly Journal of Economics, 140(2), 889–942.

5. Dillon, E., Jaffe, S., Immorlica, N. and Stanton, C. (2025). Shifting Work Patterns with Generative AI. NBER Working Paper 33795.

6. McKinsey & Company (2025). The State of AI: How Organizations Are Rewiring to Capture Value.

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