How AI can reveal overlooked losses between business activities—and how to determine whether acting on them will improve financial performance.

AI business process analysis helps established businesses identify hidden operational costs, decision delays and information gaps that conventional reporting often fails to connect across departments.

Its strategic value lies in making previously expensive forms of operational intelligence economically accessible, allowing businesses to reconsider workflows, approvals and decisions originally designed around limited information.

However, discovering inefficiencies does not automatically improve profitability: measurable value depends on redesigning how the business operates, addressing the constraints that determine overall performance, and demonstrating that the resulting changes improve margin, throughput, cash flow or customer outcomes.

Your Business May Be Designed Around Information Constraints That No Longer Exist

An established business can have competent managers, functioning systems and improving departmental performance while remaining structurally inefficient.

Sales meets its targets. Operations maintains output. Finance controls expenditure. Yet margins remain under pressure, customer commitments become harder to fulfil, and routine decisions keep returning to the owner.

The instinct is to improve execution. Increase accountability. Reduce processing time. Add capacity. Automate repetitive work.

These responses may improve individual activities without changing the conditions that produce the underlying problem.

The business may not be suffering from poor execution. It may be operating within a structure designed around information limitations that no longer need to exist.

Historically, assembling and interpreting information was expensive. Managers relied on experience to reconcile conflicting records, investigate exceptions, and make cross-departmental decisions.

Approval hierarchies compensated for uncertainty. Departmental boundaries simplified accountability. Reporting systems measured activities that could be reliably recorded.

Employees developed informal methods for resolving problems that formal processes could not accommodate.

Many of these arrangements were rational when established.

The difficulty emerges when they remain embedded in the operating model long after the conditions that justified them have changed.

The structural problem is not where most businesses measure it

A business creates financial value through connected activities, but most performance systems measure those activities separately.

Sales measures conversion. Operations measures output. Finance measures collections. Customer service measures response times.

Each function can demonstrate improvement while the business absorbs growing coordination costs.

A sales commitment changes operational priorities. An incomplete order creates clarification work. A delivery exception changes invoicing conditions. A delayed approval affects customer expectations.

The economic consequence emerges across the sequence, but accountability and reporting remain divided between its parts.

A handoff is not simply the transfer of work. It is the transfer of information, assumptions, commitments and decision rights.

When those elements are incomplete, the receiving function must reconstruct what the previous function knew.

That reconstruction consumes capacity, creates uncertainty and increases the possibility of a different decision.

The cost may eventually show up as overtime, rework, margin erosion, delayed cash collection, or lost customer business. But the cause often remains invisible in departmental reporting.

Management sees where the consequence appears and assumes the problem originates there.

Slow quotations become a sales productivity issue. Delivery delays become an operations capacity issue. Repeated escalations become a delegation issue.

Sometimes those explanations are correct.

But a delay recorded in operations may originate in a commitment made without reliable capacity information. A finance dispute may originate in an undocumented customer agreement. An approval bottleneck may exist because employees cannot access the information required to make an authorised decision.

The function experiencing the problem is not necessarily the function creating it.

AI changes the economics of understanding these relationships

Businesses have always been able to investigate cross-functional failures.

The obstacle was often the cost.

Someone had to reconstruct events, examine correspondence, compare records, interview employees, and identify recurring patterns. For an established business without a dedicated analytics function, the investigation could consume more management capacity than the suspected problem appeared to justify.

Consequently, many losses remained unexamined.

AI business process analysis can change that calculation.

Information scattered across operational records, correspondence, customer interactions and disconnected systems can increasingly be examined together, subject to appropriate access, reliability and governance.

This makes some previously uneconomic forms of investigation practical.

But the significance extends beyond discovering inefficiencies.

When the economics of information change, the logic of the operating model may need to change with them.

Information constraints determine more than how quickly work moves. They influence how much complexity the business can manage, how many decisions require senior judgment and which customer requirements can be served profitably.

An approval process originally designed because information was difficult to verify may no longer be justified in its existing form. A management escalation created to compensate for fragmented records may become avoidable. A decision made late because relevant evidence was unavailable may become possible earlier.

The opportunity is not simply to give employees better information.

It is to reconsider how decisions should happen, where authority should sit and whether management capacity must continue expanding in proportion to operational complexity.

This does not automatically justify AI investment. Conventional reporting, clearer accountability or straightforward process redesign may still offer a better solution.

Better intelligence also does not guarantee financial improvement.

If the business identifies recurring delays but retains the same decision structure, the delays may continue. If AI accelerates order processing while fulfilment capacity remains constrained, congestion may increase.

The commercial test is whether new intelligence enables a changed operating state that improves the performance of the whole business.

For owners of $5M–$20M businesses, this creates a specific strategic responsibility: distinguish the constraints that remain necessary from those the organisation continues to operate around because they have become familiar.

Your business may still be organised around information you can now afford to know.

Why Business Value Gets Lost Between Processes, Not Just Within Them

Departmental efficiency and business-wide performance are not the same thing.

Consider a $12 million business where marketing generates qualified enquiries, sales converts them, and operations meets its production targets.

Despite these results, delivery reliability is deteriorating and profitability is weaker than expected.

Sales has been accepting urgent orders to protect revenue. Operations fulfils them through overtime and schedule changes. Finance records the resulting sales but may not attribute the full disruption cost to the commitments that caused it.

Each function has acted rationally in pursuit of its own objectives.

Collectively, they have produced a less profitable outcome.

The underlying problem is not necessarily poor communication. Decisions made in one function change the economics of another without those consequences being fully visible at the point of decision.

These decisions continue for another reason.

Sales does not want to lose the customer. Operations does not want to be accused of obstructing revenue. Finance can identify the additional expenditure but has limited influence over commitments already made.

Everyone understands that urgent orders create disruption. Nobody has sufficient authority, information, or incentive to resolve the conflict before making the commitment.

The system lets each function make a reasonable decision without bearing the full consequences.

A delivery commitment is therefore more than a sales decision. It allocates future operational capacity, influences scheduling and creates a financial obligation.

If those consequences aren’t represented when the commitment is made, the business makes a system-wide decision using departmental information.

That is the structural weakness.

Traditional performance measures can reinforce it because managers are rewarded for outcomes within their control, even when those outcomes create costs elsewhere.

The more useful unit of analysis is the complete commercial sequence: from customer demand through fulfilment, invoicing and collection.

This does not mean the business must manage every process centrally. It means important decisions should reflect the dependencies they create.

The owner may already know that urgent orders are expensive. The unresolved issue is whether the business has designed a way to make commercially sound commitments without requiring the owner to arbitrate between departments.

Until that changes, better departmental performance may simply make the underlying conflict harder to see.

Observable behaviour: Departmental KPIs improve while urgent interventions, customer escalations and margin exceptions continue.

Business consequence: Management invests in local improvements while the decisions producing system-wide underperformance remain unchanged.

A business owner notices that quotations are taking too long and asks the sales manager to improve turnaround times.

The team works harder, but Friday afternoon still brings a queue of enquiries waiting for technical clarification.

Looking across the full process reveals that sales cannot reliably access the specifications needed to quote without involving operations.

The owner realises that the problem was never simply slow salespeople—it was a decision process built around unavailable information.

What Traditional Reporting and Process Analysis Fail to Reveal

Business systems record transactions more consistently than they record the conditions that made those transactions difficult.

A CRM records enquiries, quotations and sales outcomes. An ERP system records orders and operational events. Accounting software records revenue, expenditure and receivables.

These systems provide essential evidence.

But their records rarely explain every interaction that influenced the result.

A quotation may be recorded as issued three days after an enquiry. The system may not show that sales spent those days clarifying specifications with operations, waiting for approval, or reconstructing information from previous customer correspondence.

If the customer declines, the outcome may be recorded as price or no response.

The actual cause remains uncertain.

Conventional process mapping and process mining can expose many of these problems. They are established, useful disciplines, particularly where reliable event data exists.

Their limitations arise when commercially important evidence sits outside structured records or when repeating an investigation is too expensive.

Informal corrections, telephone decisions, undocumented exceptions and abandoned enquiries may leave incomplete traces.

But not all missing information is missing by accident.

Employees often resolve problems informally because recording them creates additional work, exposes a mistake or triggers an escalation that delays the customer.

A salesperson quietly corrects an incomplete order. Operations absorbs a last-minute change rather than creating a dispute. A manager approves an exception over the phone because the formal process would take too long.

The work gets completed.

The business loses the evidence of what it took to complete it.

The very behaviours that keep the business operating create reporting gaps.

This complicates the assumption that better reporting simply requires better data collection.

Employees may be working around a process because following it makes their jobs harder. Unless that underlying condition changes, requiring more detailed records may add administration without improving information quality.

The important distinction is between what the business measures, what it can observe and what it needs to know.

The overlooked opportunity may not be another performance indicator. It may be evidence that explains why a decision was delayed, why a customer disengaged, or why the same exception keeps recurring.

Some events leave no recoverable record. AI cannot establish what never happened simply by analysing existing data.

But it may make previously neglected evidence usable.

The diagnostic starting point should therefore be a commercially important unanswered question, not a general ambition to collect more data.

Observable behaviour: Managers have detailed reports but still rely on lengthy meetings and personal recollection to explain difficult orders or recurring customer problems.

Business consequence: The organisation continues making decisions from incomplete records while the informal work required to keep operations moving remains largely invisible.

What AI Can Now Discover That Was Previously Too Costly to Investigate

AI changes the economics of investigation by making fragmented operational information cheaper to interpret and connect.

Consider a distribution business with inconsistent quotation turnaround times.

Its systems show when enquiries arrive and when quotations are issued. They do not consistently explain why certain enquiries require repeated clarification.

An AI-assisted investigation could examine authorised quotation notes, correspondence, product records, and historical outcomes to identify recurring conditions that drive delays.

Enquiries involving particular specifications may frequently require technical review. Perhaps certain combinations of products create approval dependencies. Repeat customers may experience delays because earlier agreements are hard to retrieve.

These relationships may have existed for years.

The change is that investigating them systematically may now cost less.

Traditional process mining can reconstruct recorded workflows. AI can extend analysis into unstructured information, support exception classification, and help identify patterns across records not originally designed to be analysed together.

It can also predict which new cases are likely to encounter similar problems.

However, detecting a pattern does not establish its cause.

If a product category is associated with slow quotations, the underlying problem could be technical complexity, unclear authority or unreliable supplier information.

That finding requires validation before the business changes its process.

The deeper opportunity is not unlimited analysis. It is lowering the cost of obtaining information that can change commercially consequential decisions.

A small recurring clarification may not justify an improvement project on its own. Across hundreds of transactions, the same condition may become financially significant.

AI can help establish whether that pattern exists and how frequently it occurs.

The business must still determine whether preventing it is worthwhile.

Observable behaviour: Employees repeatedly investigate similar exceptions because the reasons behind previous cases are buried in correspondence and operational notes.

Business consequence: The organisation keeps paying to reconstruct information it already has while recurring losses go unaddressed.

How to Identify Which Hidden Losses Are Commercially Worth Addressing

An identified inefficiency becomes a business opportunity only when changing it can improve a commercially meaningful outcome.

This is where many AI business cases become unreliable.

Suppose a business processes 500 orders each month. Analysis identifies a verification activity requiring 12 minutes per order.

Eliminating that activity could free up 100 employee hours per month.

That is a capacity improvement.

It is not automatically a financial saving.

If salaries remain unchanged, no additional profitable orders are processed, and no expenditure is avoided, the business may experience less pressure without increasing profit.

That improvement can still be worthwhile. But its value must be described accurately.

The economic question is what the released capacity enables.

If verification constrains fulfilment while profitable demand exceeds available capacity, reducing verification time may increase throughput.

If demand is insufficient, the same improvement may simply create idle capacity.

But a more difficult possibility exists.

Verification may prevent costly mistakes involving complex orders, unusual customer requirements, or contractual commitments.

Removing it could improve processing speed while increasing the cost of errors.

Some apparently inefficient activities are the price of controlling a more expensive failure.

The correct decision may be to retain the control, redesign how it operates or apply it selectively where the commercial risk justifies the effort.

This is why the value of an improvement depends on the constraint it changes, not merely the activity it accelerates.

Before deciding whether AI can help, the business must distinguish three conditions that often produce similar symptoms.

Information is missing. The business cannot make a sufficiently informed decision because it has not captured or made relevant evidence available. The first question is whether obtaining that information would materially change the outcome.

Information exists but is inaccessible. Employees repeatedly reconstruct evidence held in correspondence, records or other functions. The opportunity may lie in making that information available at the point of decision.

Information is available but not acted upon. The business already knows enough, but authority, incentives, capacity or workflow design prevent an effective response. More analysis is unlikely to resolve the constraint.

These are different structural problems. Treating all three as opportunities for AI analysis risks investing in intelligence when the real requirement is access or operating redesign.

A credible assessment must connect the identified problem to revenue, contribution margin, avoidable expenditure, working capital or customer outcomes.

It must also account for implementation costs, operational risks and whether a simpler intervention would achieve the same result.

An approval delay caused by unclear authority may require a revised delegation policy rather than AI.

The purpose of analysis is not to justify technology.

It is to identify the most economically effective change.

Observable behaviour: Improvement proposals quantify hours saved without establishing whether the activity is unnecessary, whether it controls a greater risk or whether the underlying information is already available.

Business consequence: The business funds interventions that improve activity measures without resolving the constraint preventing financial improvement—and may remove controls whose value was never properly understood.

What Must Change in Decisions, Workflows and Accountability to Capture the Value

Better intelligence creates value only when it changes how the business makes decisions or performs work.

Suppose AI analysis shows that incomplete order specifications strongly correlate with rework and delivery delays.

The finding explains a recurring problem.

But unless the business changes how it accepts, verifies, or escalates orders, the same failures may continue.

The required intervention is an operating-design decision.

Should critical specifications be validated before order confirmation? Which exceptions require technical review? Who can approve an urgent commitment when available capacity is limited?

These questions define the changed state.

AI might identify missing information, flag unusual conditions or present relevant historical evidence. But the commercial improvement depends on what employees are authorised and expected to do with that information.

A better prediction does not create a better business unless someone can act on it.

Many established businesses rely on owners and senior managers to resolve routine uncertainty because employees lack information, authority or confidence.

But there is another reason those decisions keep returning to the top.

Nobody else wants to carry the consequences of getting them wrong.

The owner may have approved the same type of exception dozens of times. Employees know the likely answer. They still ask because approval transfers responsibility.

Better information will not necessarily change that behaviour.

A business does not decentralise decisions simply by decentralising information. It must also redesign accountability.

That means establishing which decisions employees are expected to make, what evidence is sufficient, which risks they are authorised to accept and when escalation is genuinely required.

It also means accepting that sensible decisions can occasionally produce disappointing outcomes without automatically becoming evidence of individual failure.

Without that distinction, employees have little reason to exercise judgment independently.

As transaction volumes and customer requirements become more complex, this dependence can limit the business’s ability to grow without adding management capacity.

Introducing AI recommendations without changing those conditions creates another layer of work.

Employees review the recommendation, seek approval and then follow the original process.

The business has added intelligence without reducing dependency.

Clear decision rights, information requirements, and exception thresholds can make routine decisions more consistent while ensuring genuinely consequential exceptions still receive appropriate human judgment.

This can also change which customer requirements the business can serve economically. Better-supported routine decisions can make work that previously demanded disproportionate senior attention manageable, provided operational capacity and commercial margins justify it.

The strategic opportunity is not simply to make existing decisions faster. It is to change which decisions require scarce management judgment in the first place.

That is a different operating model.

And it requires management to give up some decisions it has become accustomed to controlling.

A distribution business finds that repeated order corrections are consuming its operations manager’s mornings.

AI-assisted analysis of order notes and correspondence identifies recurring specification gaps, but the breakthrough comes when the business changes when orders can be confirmed and who can approve exceptions.

Routine cases begin moving through a clearer process, while genuinely unusual decisions still receive management attention.

The owner stops treating constant intervention as evidence of responsible leadership and starts treating it as a sign that the system needs better design.

When AI Adds No Value—or Simply Moves the Constraint Elsewhere

Accelerating an activity doesn’t improve the whole business when another constraint determines performance.

Consider a company that uses AI to reduce quotation preparation from two hours to 20 minutes.

Sales responds faster and can process more enquiries.

The sales manager reports a substantial productivity improvement. Quotation turnaround is better, and the team has more capacity to pursue opportunities.

But operations already has more accepted orders than it can fulfil within promised delivery windows.

Faster quotation processing may create additional commitments without increasing delivery capacity.

Backlogs grow. Overtime rises. Customers become frustrated.

The local improvement is real.

The system-wide benefit may be negative.

Faster sales activity is not growth if the business cannot fulfil the resulting demand profitably.

This is uncomfortable because the initiative may have delivered exactly what management requested.

The technology works. The measured process improves. The team responsible for implementing it has achieved its target.

Yet the business may be worse off.

The failure is not necessarily in the AI. It is in defining success around an activity rather than the commercial outcome that activity is supposed to support.

Sometimes AI is unnecessary because existing information is sufficient and the problem is managerial.

Sometimes it is uneconomic because the process occurs too infrequently or the expected benefit is too small.

Sometimes the information is useful, but the organisation lacks the capacity to act on it.

For example, predicting late deliveries may improve planning and customer communication. But if scheduling, capacity and customer commitments cannot change, prediction alone may not prevent delays.

There are also situations where reliability, safety, privacy or regulatory requirements make a proposed AI intervention unsuitable.

Technical capability does not establish commercial justification.

An established business cannot afford to confuse successful automation with successful operating improvement.

The constraint determines whether faster activity creates value. Management determines whether the intervention addresses that constraint.

Observable behaviour: AI-enabled activities become faster while downstream queues, exceptions or customer complaints increase.

Business consequence: The organisation celebrates a productivity improvement while absorbing additional congestion, delivery risk and costs elsewhere in the business.

How to Measure Whether Better Intelligence Improved Business Performance

Measure AI’s return by the financial difference caused by the changed operation, not by the amount of analysis performed or work automated.

Suppose AI-assisted analysis identifies a recurring source of order rework.

Management changes the validation process, clarifies decision authority and introduces targeted exception detection.

Three months later, rework has fallen by 30%.

That demonstrates operational improvement.

The next question is whether the reduction eliminated overtime, prevented material waste, increased profitable throughput or reduced other identifiable losses.

Measurement therefore begins before implementation.

The business needs a baseline for the relevant process, a defined changed state and a clear explanation of how the intervention is expected to affect commercial outcomes.

After implementation, compare results with an appropriate baseline or control, accounting for other changes that may have influenced performance.

The full cost must include software, integration, redesign, training, oversight and ongoing operation.

For example, an initiative costing $45,000 in its first year and producing $70,000 in attributable financial benefits generates a $25,000 net benefit.

That conclusion holds only if the benefits are incremental, financially real, and not counted twice.

Do not count released employee time as both a cash saving and additional productive capacity unless each benefit independently materialises.

Measurement should also establish whether the original constraint was relieved or merely moved elsewhere.

A reduction in quotation time accompanied by a growing fulfilment backlog may indicate that the intervention improved one activity without increasing the business’s capacity to deliver profitable work.

The objective is to establish whether better intelligence caused a commercially valuable change across the operating system.

Observable behaviour: Project reports celebrate processing speed, automation rates and prediction accuracy without reconciling those results to financial performance.

Business consequence: Management cannot reliably distinguish profitable operating-model improvement from successful technical implementation.

Imagine two businesses using the same AI capability to identify orders at risk of late delivery.

One generates increasingly accurate alerts that managers review each morning; the other changes how delivery commitments are approved before orders enter production.

The first knows more about its problems, while the second has a mechanism for preventing some of them.

The difference is not the sophistication of the intelligence but whether the business was willing to redesign the decisions around it.

Conclusion

The most difficult business problems are not always the ones producing the most visible disruption.

They are often the conditions that have become normal.

Managers repeatedly approve routine exceptions. Employees reconstruct missing information. Departments meet their targets while the business absorbs delays, rework and avoidable costs.

Over time, these arrangements become accepted as the price of operating at scale.

But some exist because the business was designed around information that was historically too difficult or expensive to obtain.

AI changes that possibility.

It can make certain forms of operational intelligence economically accessible, revealing relationships and recurring conditions that conventional reporting may not adequately explain.

That does not mean every discovered inefficiency should be corrected or every process should be automated.

The opportunity is to determine whether better information makes a different operating design commercially worthwhile.

Start with a business outcome that consistently disappoints. Follow the decisions and dependencies contributing to it. Establish what information is missing, why it matters and whether obtaining it would enable a better decision.

Then define what must change operationally—and how the financial benefit will be demonstrated.

The result may justify AI. It may reveal a simpler management intervention. Either outcome is preferable to investing without understanding the constraint.

For an established business owner, this ultimately comes down to control.

You can keep managing the consequences of fragmented information with more meetings, approvals, and personal intervention.

Or you can examine whether the structure producing those demands is still necessary.

Your business may still be organised around information you can now afford to know.

You no longer have to automatically accept that condition.

The next step is to identify where it exists, determine what changing it would be worth, and decide whether the business should continue operating as it does today.

Choose one recurring decision that still reaches you or a senior manager.

Ask why it cannot be made reliably by the person closest to the work.

Is the necessary information unavailable? Is it scattered across people and systems? Or is the information already available, but nobody has the authority or willingness to act on it?

The answer will tell you whether the constraint is information, access or accountability.

It will also tell you whether AI has a meaningful role—or whether the business needs a management decision before it needs another technology.

Action Steps

Trace a commercial outcome across departmental boundaries

Select a recurring outcome affecting margin, throughput, cash collection or customer performance. Trace the complete sequence of activities and decisions contributing to it, including where work changes ownership. This establishes whether the visible problem originates within the reporting function or elsewhere, determining where investigation should begin.

Identify information constraints embedded in existing workflows

Examine repeated approvals, clarification requests, manual checks and management escalations. Determine which exist because information is unavailable, unreliable or difficult to interpret at the point of decision. Separate genuinely necessary controls from arrangements that compensate for historical information limitations; this determines which decisions are candidates for redesign.

Test whether AI changes the economics of obtaining that information

Identify the evidence required to resolve each information gap and assess whether existing systems, reporting or conventional analysis can provide it economically. Consider AI only where it materially improves feasibility, cost, timeliness or interpretability. The decision is whether new analytical capability justifies changing the current operating arrangement.

Establish the commercial mechanism before approving an intervention

Connect each identified problem to a measurable financial outcome. Determine whether improvement would increase profitable throughput, avoid expenditure, release working capital or prevent identifiable losses. Exclude theoretical labour savings unless the released capacity has a credible productive use or produces an actual cost reduction.

Define the changed operating state before implementation

Specify which decisions will occur differently, what information will support them, who has authority and how exceptions will be managed. Confirm that downstream functions can absorb the expected improvement. This prevents the business from purchasing better intelligence while retaining the operating structure that originally limited performance.

Validate financial results against the original constraint

Record baseline performance, implementation costs and expected financial outcomes before introducing change. Compare realised results against an appropriate baseline or control, accounting for external influences and displaced bottlenecks. Continue, revise or stop the intervention according to demonstrated system-wide value rather than technical performance alone.

FAQs

Bonus Section: Three Uncomfortable Ideas About AI and the Way Your Business Operates

You may be trying to improve processes that should no longer exist in their current form. That is the uncomfortable possibility behind AI-enabled business analysis. Most improvement programmes assume the existing workflow is necessary and concentrate on making it more efficient.

But some workflows exist because information was historically difficult to obtain, verify or interpret. When those limitations change, the structure built around them deserves examination. Three shifts in thinking follow.

Stop automating decisions that should no longer need escalation

Repeated approvals are not always evidence of necessary managerial control. Sometimes they reveal that employees lack access to information or decision authority that could safely be made available.

AI may make relevant evidence accessible earlier, allowing routine decisions to move closer to the work while genuinely consequential exceptions retain oversight.

Consequence of inaction: Management remains the bottleneck, even after the underlying information problem becomes solvable.

Your most valuable data may describe work that never officially happened

A completed transaction tells you what occurred. It rarely captures the abandoned enquiry, informal correction, avoided escalation or decision that was never made.

AI can sometimes recover useful evidence of these events from existing communications and operational records. This changes the potential scope of analysis, although missing events and alternative outcomes cannot always be reconstructed reliably.

Consequence of inaction: The business continues optimising recorded activity while potentially significant commercial losses remain outside its field of view.

Better intelligence can make your existing operating model less appropriate

An approval hierarchy may have been sensible when information was scarce. A departmental boundary may have simplified coordination when cross-functional visibility was expensive.

Those arrangements can outlive their original justification.

The opportunity is not necessarily to remove them. It is to test whether they still serve a necessary commercial purpose.

Consequence of inaction: New analytical capabilities are layered onto an operating structure designed for older limitations.

The deeper possibility is that AI does more than improve how a business operates. It can reveal which parts of its existing design are no longer economically justified.

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