Artificial intelligence (AI) is changing how businesses operate, from automating routine tasks and analysing customer behaviour to forecasting demand and supporting complex strategic decisions. Enterprises now have access to increasingly capable AI models, copilots, and autonomous agents.
However, adopting advanced technology does not automatically translate into higher revenue, better profit margins, improved productivity, or stronger customer relationships. The real challenge is ensuring that AI capabilities are connected to the decisions businesses need to make and the outcomes they want to achieve.
Many organisations still struggle with fragmented data, disconnected systems, slow approval processes, unclear accountability, and difficulties measuring the return on AI investments.
These problems cannot be solved by introducing another tool alone. Businesses need a structured approach that combines AI intelligence with institutional knowledge, human judgment, governance, and effective execution.
This is where the concept of Enterprise Agency becomes important. It refers to an organisation's ability to turn AI-generated insights into governed decisions and translate those decisions into measurable action.
While agentic AI focuses on systems that can independently perform tasks, Enterprise Agency focuses on how an organisation makes decisions, authorises actions, and remains accountable for the results.
As AI becomes more deeply integrated into business operations, competitive advantage will depend not simply on which companies use the most advanced technology, but on which companies use it most effectively.
Better decisions, supported by reliable information and responsible execution, will increasingly determine the business value of AI.
The rapid development of AI has created new opportunities for enterprises to improve productivity, personalise customer experiences, automate repetitive work, and analyse large volumes of information. However, technical capability and business value are two different things.
An AI system might summarise thousands of customer complaints in minutes, identify patterns in sales data, or recommend adjustments to inventory levels. However, businesses still need to assess the relevance and accuracy of AI-generated insights, identify the people responsible for authorising decisions, and establish clear processes for putting those decisions into practice.
This gap between technological capability and organisational effectiveness is one of the most important challenges in enterprise AI adoption.
According to McKinsey's 2025 global survey on AI, nearly two-thirds of respondents said their organisations had not yet begun scaling AI across the enterprise. Although 62% reported that their organisations were at least experimenting with AI agents, only 39% reported an impact on earnings before interest and taxes (EBIT) at the enterprise level. These findings highlight the difference between experimenting with AI and translating it into broader business performance.
The problem is not necessarily that AI systems are incapable of producing useful results. In many cases, businesses lack the processes required to integrate those results into everyday decisions.
For example, an AI-powered sales forecasting system may predict a decline in demand. However, if the sales, procurement, finance, and logistics teams work with disconnected data and separate approval procedures, the organisation may respond too slowly to prevent excess inventory or unnecessary expenditure.
Businesses therefore need to evaluate AI through three connected questions:
What can AI do? This concerns the technical capabilities of models, copilots, and autonomous agents.
What should the business decide? This requires objectives, reliable context, business rules, and human judgment.
How will the decision create value? This depends on execution, accountability, and measurable outcomes.
Enterprise Agency connects these three dimensions. It helps organisations move beyond isolated AI applications towards a coordinated approach in which intelligence supports decisions and decisions lead to accountable action.
The terms Agentic AI and Enterprise Agency are related, but they describe different aspects of business transformation.
Agentic AI refers to AI systems designed to pursue defined goals by planning steps, using tools, interacting with software, and adapting their actions within specified limits. Depending on the system, an AI agent might research a supplier, compare prices, prepare a purchase order, or escalate an exception for human approval.
These capabilities can reduce manual effort and accelerate workflows. Nevertheless, greater autonomy does not automatically mean that an agent understands an organisation's priorities, legal obligations, risk tolerance, or internal decision-making structure.
Enterprise Agency is an organisational capability rather than simply a feature of an AI model. It describes an enterprise's ability to combine AI-generated intelligence with business context, decision rights, governance, and execution.
An organisation with strong Enterprise Agency can establish which decisions AI may support, which actions it may perform independently, when approval is necessary, and how results will be monitored.
Consider a bank evaluating a change to its lending policy. One AI system may identify a potential revenue opportunity, another may flag increased credit risk, and a compliance system may identify regulatory constraints. Enterprise Agency provides the decision-making structure needed to bring these perspectives together.
Instead of allowing separate systems to produce disconnected recommendations, the bank can apply common business rules, identify trade-offs, obtain the required approvals, record the rationale, and monitor the results.
The distinction is straightforward: Agentic AI expands what technology can do, while Enterprise Agency determines how an organisation uses that capability responsibly and effectively.
Neither replaces the other. Enterprises need capable AI systems as well as organisational processes that ensure those systems contribute to business objectives.
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AI models can process information, identify patterns, and generate recommendations. However, their outputs are only as useful as the information, constraints, and objectives available to them.
Enterprise environments contain layers of context that are rarely captured in a single database. These include historical decisions, customer commitments, contractual obligations, financial targets, regulatory requirements, internal policies, and lessons learned from previous operations.
Without access to relevant and reliable context, AI can produce recommendations that appear reasonable but are unsuitable for the actual business situation.
Imagine a retailer preparing for a major seasonal promotion. Its marketing platform predicts strong demand, the inventory system shows limited stock, and the finance team has imposed tighter working-capital targets.
If these systems operate independently, each may produce a recommendation that makes sense within its own boundaries. The marketing team might advocate increasing promotional spending, while operations could favour limiting discounts due to stock constraints, and the finance department may focus on lowering inventory levels to control costs.
The business challenge is not simply obtaining more predictions. It is deciding how to balance demand, product availability, customer expectations, and financial constraints.
A connected decision process allows the retailer to combine these signals, compare scenarios, establish priorities, and coordinate actions across departments.
Enterprises should organise essential knowledge so that both employees and AI systems can access the correct information when making decisions. This includes maintaining clear definitions of important metrics, documenting business rules, assigning data ownership, and controlling access to sensitive information.
For example, the term revenue might mean gross sales to one team and net revenue after discounts and returns to another. An AI-generated report could compare these figures incorrectly if the definitions are inconsistent.
A governed business glossary, reliable data pipelines, and shared metric definitions help reduce such inconsistencies.
The 2025 DORA report on AI-assisted software development similarly emphasises that AI adoption is a systems-level challenge, not merely a matter of selecting better tools. Organisational capabilities and the surrounding technology environment influence whether local productivity improvements translate into broader performance gains.
The practical lesson is that businesses should improve the quality and accessibility of their institutional knowledge alongside their AI capabilities.
AI produces business value when its outputs influence real decisions and improve operational outcomes. An insight that remains in a dashboard or presentation may be informative, but it does not necessarily change performance.
Enterprises need to connect analysis with the workflows in which decisions are made and actions are completed.
Adding a chatbot to an inefficient process may make individual tasks faster without addressing the underlying delays. A more effective approach examines the entire workflow, identifies bottlenecks, and determines where AI can improve the process.
McKinsey's March 2025 research identified workflow redesign as the organisational practice most strongly associated with EBIT impact among the adoption-related practices it examined. Yet only 21% of respondents whose organisations were using generative AI reported that their organisations had fundamentally redesigned at least some workflows. This suggests that many businesses were still integrating AI into existing processes rather than rethinking how work should be performed.
Consider an accounts-payable department processing supplier invoices. AI can extract invoice details, match records, identify unusual charges, and recommend approvals. However, if exceptions still require several manual hand-offs, the overall payment process may remain slow.
A redesigned workflow could automatically process low-risk invoices within approved limits, route mismatches to the appropriate employee, and escalate high-value or suspicious transactions for review. The result can be measured through processing time, error rates, overdue payments, and the cost of handling each invoice.
Successful implementation does not require every organisation to launch a large transformation programme immediately. Enterprises can begin with a focused process where the business problem, decision owner, and expected benefits are clearly defined.
Useful starting points include:
Reducing customer-service resolution times.
Improving demand forecasting and inventory planning.
Identifying revenue leakage from billing errors or missed renewals.
Accelerating contract review and document processing.
Detecting unusual transactions and operational risks.
Before implementation, organisations should record baseline performance and establish measurable targets. They should then compare results after deployment, taking account of implementation costs, human review, maintenance, and any new risks.
The objective is not to maximise the number of automated tasks. It is to improve the performance of the business process as a whole.
As AI systems become more involved in financial, operational, and customer-related decisions, governance becomes a central requirement rather than an administrative afterthought.
A recommendation may influence pricing, determine which supplier receives an order, prioritise a customer complaint, or flag a transaction for investigation. Each decision can carry financial, legal, reputational, or ethical consequences.
Businesses therefore need to establish who is responsible for AI-assisted decisions and how those decisions can be reviewed.
A reliable decision record should capture the information necessary to reconstruct what happened. Depending on the application, this may include:
The objective and business rules governing the decision.
The data and assumptions used to generate a recommendation.
The AI model or system version involved.
The confidence level, limitations, and relevant uncertainties.
The identity or role of the person who approved an action.
The action taken, any subsequent changes, and the resulting outcome.
Not every application requires the same level of documentation. A low-risk internal summarisation tool does not need the same controls as an AI system that influences lending, insurance eligibility, or medical decisions.
However, the principle remains consistent: organisations should be able to explain how important decisions were reached and establish responsibility for the actions that followed.
The US National Institute of Standards and Technology (NIST) provides a practical foundation through its AI Risk Management Framework and its Generative AI Profile. These resources help organisations identify, assess, manage, and monitor AI-related risks throughout the system lifecycle.
Enterprises can apply this approach by classifying AI use cases according to their potential impact, establishing appropriate approval requirements, testing systems before deployment, and monitoring them after launch.
For example, an AI tool that drafts an internal meeting summary may operate with relatively limited oversight. An AI system that recommends changes to customer credit limits should face stricter validation, access controls, human review, and monitoring.
Human involvement should also be meaningful. Employees need enough information, authority, and time to challenge questionable recommendations rather than simply approving whatever the system produces.
Governance should enable responsible action, not create unnecessary bureaucracy. Clear decision rights and proportionate controls can help organisations move quickly while keeping important risks manageable.
One of the most common mistakes in AI adoption is treating activity as evidence of success. The number of employees using AI, the volume of generated content, or the number of automated tasks may demonstrate adoption, but these measures do not necessarily establish business value.
An enterprise should evaluate AI through a balanced set of operational, financial, customer, and risk-related indicators.
|
Business objective |
Useful performance indicators |
|---|---|
|
Revenue growth |
Conversion rates, retention, revenue per customer |
|
Cost efficiency |
Cost per transaction, processing time, rework |
|
Working capital |
Inventory turnover, days sales outstanding, cash-conversion cycle |
|
Customer experience |
Resolution time, satisfaction, repeat-contact rate |
|
Operational resilience |
Downtime, recovery time, supply disruption |
|
Risk and compliance |
Error rates, policy violations, audit findings |
|
Workforce productivity |
Time saved, quality of output, capacity redirected to higher-value work |
These indicators should be selected according to the use case rather than applied uniformly to every AI project.
For example, a customer-service assistant may reduce average handling time, but if customers must contact the company repeatedly to resolve the same issue, the apparent productivity gain may be misleading. A more complete evaluation would examine resolution quality, customer satisfaction, repeat contacts, and the cost of human escalation.
Similarly, an AI-powered sales tool may generate more leads without increasing profitable conversions. Businesses should therefore measure the quality and financial contribution of those leads, not simply their volume.
McKinsey's 2025 research found that only a minority of respondents reported enterprise-level EBIT impact from AI, even as adoption and experimentation expanded. Its earlier 2025 research also identified well-defined performance indicators as an important practice associated with stronger financial outcomes.
The lesson is clear: AI investments should be connected to explicit business objectives, measurable baselines, and regular performance reviews.
Established practices in technology, finance, retail, and customer operations demonstrate how organisations can connect intelligence with execution. The following examples illustrate proven operating approaches; they should not be interpreted as claims that every organisation has already achieved these outcomes through AI.
AI coding assistants can help developers write, explain, and test code. However, faster code generation does not automatically mean that a software organisation delivers better products.
Google Cloud's 2025 DORA research examines AI-assisted software development as part of a broader organisational system. Its approach highlights the importance of the surrounding conditions that influence software delivery, including the way teams work and the capabilities supporting their processes.
Best practice: Combine AI-assisted development with code review, automated testing, security checks, continuous integration, and monitoring of production performance. Evaluate delivery speed alongside reliability, defect rates, and recovery time.
Banks can use AI to analyse transaction patterns, support fraud detection, assist customer service, and identify potential credit risks. However, automated recommendations must operate within risk policies, privacy requirements, and applicable regulations.
Best practice: Define which actions can be automated, which require additional verification, and which must be reviewed by authorised employees. Maintain decision records, test for unfair or inconsistent outcomes, and monitor false positives as well as missed risks.
This approach allows banks to benefit from rapid analysis without treating an algorithmic recommendation as unquestionable authority.
AI-based demand forecasting can help retailers anticipate sales patterns, plan replenishment, and identify potential shortages. Yet a forecast becomes useful only when procurement, inventory, logistics, and merchandising teams can respond to it.
Best practice: Combine forecasts with inventory constraints, supplier lead times, promotional calendars, and financial targets. Establish exception-based workflows so routine replenishment decisions can proceed efficiently while unusual or high-impact situations receive human attention.
Generative AI can help service teams summarise conversations, retrieve relevant information, and draft responses. Nevertheless, a rapid answer that does not solve the customer's problem may increase frustration and create additional work.
Best practice: Integrate AI assistance with approved knowledge bases, customer-history controls, escalation procedures, and quality monitoring. Measure successful resolution, repeat contacts, customer satisfaction, and the accuracy of responses.
Across these examples, a common principle emerges: technology performs better when it is supported by well-designed processes, clear accountability, reliable information, and feedback from actual outcomes.
As AI makes certain software-development and analytical tasks faster or less expensive, the basis of competition in technology services is changing.
Historically, technology projects were often evaluated through delivery timelines, implementation costs, system availability, and the completion of agreed technical requirements. These remain important, but they are insufficient when organisations increasingly expect technology to improve business performance.
Providers must understand the decisions their clients need to make, the constraints under which they operate, and the outcomes that matter. This requires collaboration among technology specialists, business leaders, domain experts, data teams, and risk professionals.
The same shift affects workforce skills. As AI takes on more routine analytical and administrative work, organisations need employees who can frame business problems, assess evidence, understand trade-offs, design workflows, and challenge automated recommendations.
The World Economic Forum's Future of Jobs Report 2025 identifies analytical thinking as a core skill for employers, alongside increasing demand for AI and big-data skills. The implication is not that technical expertise is becoming unimportant. Rather, technical capability and sound judgment increasingly need to work together.
Leadership responsibilities are changing as well. Senior executives must determine where AI can create value, establish acceptable risk boundaries, allocate resources, and ensure that departments work towards shared objectives. Managers must translate those priorities into operational practices and give employees the training needed to use AI effectively.
For technology and consulting providers, this creates an opportunity to move beyond supplying tools towards helping clients redesign processes, integrate systems, establish governance, and evaluate outcomes.
The value of expertise increasingly lies in knowing how to apply technology to the right problem under real operating conditions.
Businesses do not need to replace every existing system to improve AI-driven decision-making. They can develop Enterprise Agency incrementally by strengthening the way decisions are defined, approved, executed, and evaluated.
The following roadmap offers a practical starting point.
Begin by identifying decisions that materially affect revenue, costs, customer experience, risk, or operational performance. Prioritise opportunities where AI can improve the quality or speed of those decisions.
Document where information originates, who makes the decision, which approvals are required, and where delays occur. This reveals whether the primary obstacle is poor data, unclear responsibility, fragmented systems, or unnecessary manual work.
Connect relevant data sources, document business rules, standardise key metrics, and ensure that AI systems can access only the information they are authorised to use.
Specify which actions AI can recommend, which it can execute within predefined limits, and which require human approval. Establish escalation procedures for uncertain, unusual, or high-impact situations.
Connect AI outputs to the business systems where actions occur. This might involve routing an approved invoice, updating a replenishment request, assigning a service case, or escalating a compliance exception.
Compare actual results with baseline performance. Review errors, investigate unexpected outcomes, incorporate employee feedback, and adjust workflows or controls when necessary.
Organisations should also account for the total cost of ownership, including model usage, integration, infrastructure, employee training, security, human review, and ongoing maintenance.
A phased approach makes it easier to identify what works before expanding AI across more complex or higher-risk business functions.
The next stage of enterprise AI adoption will require more than access to increasingly capable models. Businesses must develop the organisational capabilities needed to convert intelligence into coordinated action.
This does not mean that every decision should be automated or that human judgment should become secondary. Some decisions will benefit from greater autonomy, while others will continue to require expert review, negotiation, and human accountability. The appropriate balance depends on the potential consequences, the reliability of the system, and the organisation's risk tolerance.
What matters is creating a coherent relationship between AI capabilities, business objectives, and execution. Enterprises that establish clear decision rights, manage institutional knowledge, redesign workflows, and measure outcomes will be better positioned to determine whether AI is genuinely improving performance.
Enterprise Agency provides a useful way to think about this challenge. It shifts the conversation from the number of tools an organisation deploys to its ability to make sound decisions and act on them consistently.
AI is expanding what enterprises can analyse, create, and automate. Yet access to powerful technology does not automatically eliminate revenue leakage, fragmented decision-making, operational delays, or accountability gaps. These challenges require organisational changes as well as technical improvements.
The evidence from enterprise AI research suggests that scaling value depends on factors such as workflow redesign, measurable objectives, effective governance, and integration into everyday operations. Businesses must therefore treat AI adoption as a business transformation challenge rather than a technology procurement exercise.
For leaders, the priority is to identify where better decisions can create measurable value, provide AI systems with reliable business context, and establish the processes needed to turn recommendations into responsible action.
The competitive advantage will not come simply from using more AI. It will come from combining capable technology with human judgment, institutional knowledge, clear accountability, and disciplined execution.
Ultimately, the most important question is not how much AI an enterprise can deploy, but how much better it can perform because of the decisions AI helps it make.