When uncertainty puts pressure on budgets, make it cheaper to change course
August 6, 2026
Executive Brief
Summary
Economic uncertainty does not remove the need to invest. Customers still expect better service, employees still need effective systems, and competitors will continue improving how they operate. The risk comes from committing too much of the budget to assumptions that may change before the work pays off. Companies can keep moving by funding smaller stages, preserving the useful parts of each investment, and measuring completed business results. AI makes this discipline especially important because its promise is developing faster than many companies’ ability to use it reliably.
Questions answered in this article
- How should companies invest during an economic crisis?
- Fund work in stages that produce usable business value. Each stage should preserve the company’s ability to revise the plan if the market, budget, or technology changes.
- How can a company make technology investments less risky?
- Protect the data, knowledge, and processes that will remain valuable if a vendor or platform is replaced. Limit the initial commitment, measure the business result, and understand the cost of changing direction before expanding.
- How should companies evaluate AI investments?
- Measure the cost and quality of finished work, including the time people spend reviewing and correcting AI output. Give AI a larger role only after it performs consistently inside a defined business process.
- How can a company avoid vendor lock-in?
- Keep essential data and business rules under company control. Structure important vendor connections so one provider can be changed without rebuilding the entire customer or employee experience.
The cost of standing still is real
When revenue is uncertain, delaying a technology investment can feel responsible. Cash remains available, the company avoids a difficult commitment, and leadership gets more time to see how the market develops.
Delay still carries a cost. Customers continue encountering the same problems, employees are asked to do more with less, and competitors have more time to improve their own operations.
The answer cannot be to freeze every decision until conditions become clear. It also cannot be to approve large programs based on assumptions that may be outdated before implementation is complete.
Leaders need a way to make progress while preserving the ability to respond. That means structuring investments so the company can learn, keep what works, and change what does not without paying for the same progress twice.
Know what will survive a change in direction
Before approving an investment, identify what the company will still own if the vendor, platform, or plan changes.
The answer should include more than a contract clause saying the data belongs to you. The data must be retrievable in a programatic, useful form, with definitions and relationships that another system can understand.
The company should also retain the operating knowledge created during the project. If a team determines how customer requests should be categorized, when a financial exception requires review, or which information an employee needs to approve a transaction, those decisions should remain understandable outside the product where they were configured.
This gives every stage of the investment lasting value. Even if the final technology choice changes, the company keeps better data, clearer processes, useful integrations, and a stronger understanding of the work.
That continuity matters when budgets are tight. Leadership can revise the plan without writing off everything already funded.
Fund a result before funding a rollout
A large rollout often asks leadership to approve several untested assumptions at once. The technology must work, people must adopt it, the process must improve, and the financial result must justify the effort.
Break those assumptions apart. Start with one part of the business where the problem is visible and the outcome can be measured.
A financial services company may want to reduce the time employees spend preparing documentation for routine account reviews. The first investment could focus on one review category with established rules, specific work, and an experienced team capable of judging the result.
The goal should describe the business change. Reduce preparation time while maintaining accuracy and required review. That gives leadership something more useful to evaluate than whether the software was installed successfully.
If the first stage works, the company can expand with evidence. If the result falls short, the team can revise the process, try another provider, or stop without disrupting every review category.
This approach also improves budgeting. Each stage produces information that makes the next funding decision more accurate.
Know what Plan B will cost
Every major technology decision includes an exit cost. You may pay it when a vendor raises prices, a platform stops supporting an important capability, or the needs of the business move beyond what the system can handle. In negotiation terms, this is your BATNA: the best available option if the preferred path stops making sense.
That cost is often hidden during procurement because the first-year implementation receives most of the attention. Leadership should also understand what it would take to move the data, preserve the workflow, retrain employees, and replace the connections to other systems.
A low exit cost does not mean the company expects the investment to fail. It gives leadership more leverage if the market changes or the provider no longer delivers enough value.
Start with a few direct questions. Can the company retrieve its data in a documented, usable form? Which business rules will exist only inside the platform? How many other systems will connect directly to it? Who inside the company will understand how the process works?
The answers reveal how much freedom the company is giving up. Leadership will know how much it would cost, how long the change would take, what value from the original investment could be preserved, and what the alternative would deliver.
Keep important vendor connections contained
A vendor can become difficult to replace when its technical requirements spread through the company’s systems. The more places that depend on one provider’s data structure and rules, the more work it takes to change direction..
Search provides a useful example. Apache Solr and Elasticsearch offer similar capabilities, but each has its own requirements for indexing content and returning results. If the website is built around one platform’s specific format, replacing it can require changes across the entire search experience.
You can reduce that dependence by creating a common format for the content being indexed. A separate connection translates that information for Solr, Elasticsearch, or another provider, while the website sends users to the same results experience.
If you decide to change search platforms, you replace the provider-specific connection instead of rebuilding the content feed and every page that uses search. The work stays contained, which lowers the cost and disruption of Plan B.
AI shows why finished work matters
AI has intensified the pressure to invest. Boards and leadership teams see useful capabilities developing quickly, while employees are already experimenting with tools inside and outside the company’s approved systems.
The promise is significant, but the first output can give a misleading impression of value. A system may create a report, draft, analysis, or block of code in seconds, then require hours of experienced review before the business can use it.
As I wrote in AI Is Moving Fast. Here’s How to Make Sure It Creates Value., the useful measure is finished work. Under financial pressure, that distinction becomes more important because rework consumes the same budget and capacity the investment was supposed to preserve.
Measure the entire path to an approved result. Include the cost of the tool, the time required to prepare the task, the review performed by employees, the corrections, and the work needed when the output is unreliable.
This does not diminish AI’s potential. It gives leadership a credible way to identify where that potential is producing an economic return.
An AI system that drafts ten customer responses may look productive. If a senior employee has to reconstruct eight of them, the system has increased activity without creating much capacity.
Another system may handle a narrow internal task with consistent results and return several hours to the team each week. That smaller use can provide more value because the finished result requires less intervention.
Give AI a role it can earn
AI investment becomes easier to manage when the system has a defined responsibility. Choose a task with clear inputs, a recognizable result, and someone who can judge whether the work is correct.
The system might gather information for an employee, prepare options, classify a request, or draft a response for approval. Record how often the result is accepted, corrected, or rejected.
Expand its responsibility when the evidence supports that decision. Keep tighter controls around customer communication, financial records, regulated decisions, and work where an error would be difficult to reverse.
Performance should be evaluated separately for each task. A system that performs well when classifying documents may still need close review when applying policy to a customer’s circumstances.
This allows the company to gain useful experience without reorganizing an important operation around a promise. It also gives employees a clearer understanding of when they can rely on the system and when their judgment remains essential.
Preserve the process if the AI changes
AI models, prices, and product features are changing quickly. A company should expect to reconsider at least some of its early choices.
Keep the business process understandable without the model. The company should control the source information, permissions, approval rules, and definition of a completed result.
The AI provider can then perform a specific part of the work. If another model becomes more accurate or economical, the company can evaluate it against the same task and standards.
This protects the investment already made in the process. Employees do not have to relearn the entire operation, and leadership can compare providers using the company’s actual work.
It also limits the risk created by AI features built into existing software. Those features can be convenient because the platform already has access to users and information, but they may place instructions, company context, and performance history inside a product that is difficult to leave.
Before approving broad adoption, confirm what the company can retrieve and reuse elsewhere. Understand whether the underlying model can change, how company information is used, and what happens to the workflow if the feature is removed or repriced.
Keep part of the budget available for what you learn
A rigid annual plan leaves little room to act on new information. If every dollar is committed at the beginning, the company may continue funding a weak assumption because no practical alternative remains.
Reserve part of the budget for decisions that will follow the first stage. That money may support expansion when the result is strong, a correction when the process needs work, or another provider when the first option underperforms.
This is particularly useful for AI because pricing, capabilities, and implementation patterns are still moving. The company can act now without pretending that its first choice will be its final choice.
Flexibility in the budget also changes the quality of internal conversations. Teams can report what they learned honestly because leadership has preserved a way to respond.
A pilot that reveals a weak result has still created value if it prevents a larger commitment. That only works when the company has not spent the full budget proving the first idea.
Use the next 90 days to create one flexible investment
Choose one business problem that costs time, revenue, or customer trust. Define the completed result and calculate what the current process requires.
Fund one stage that can improve that result within 90 days. Keep the company’s data and operating rules separate from the chosen tool, and limit the number of systems or teams that depend on it during the test.
Measure the finished work throughout the period. Track the result, the time people spend producing it, and the effort required to correct problems.
Before expanding, determine what the company will preserve if the tool changes. Confirm that the data, process knowledge, and useful technical work can support another option.
At the end of the 90 days, leadership should have a better result or a better decision. Either outcome can protect the budget when the investment was designed to produce evidence before deeper commitment.
Move forward without giving up your options
Economic pressure makes technology decisions harder because companies need progress and flexibility at the same time. The budget must address current problems without assuming that today’s market, vendor, or capability will remain unchanged.
You can manage that tension by funding work in stages, protecting what the company owns, and measuring usable business results. AI belongs in that approach because it can create real capacity, but it should earn a larger role through evidence.
The companies that keep moving will still make decisions they later revise. Their advantage will come from how much value they preserve when they change course.
