A customer asks a question.
Your team answers tomorrow.
A buyer requests a quotation.
Someone starts searching through spreadsheets.
A specialist opens a new case.
The first hour disappears into finding the documents.
None of these moments looks like an AI problem.
They look like normal business.
That is exactly why they deserve attention.
Before you ask, “How should we use AI?”, ask:
“Where are customers, employees, and decisions waiting—and what is that waiting costing us?”
You may not need another chatbot.
You may need to close a gap that has become so familiar nobody questions it anymore.
One retailer tested AI. The results were not what a sales pitch would suggest.
A working paper published in October 2025 examined seven randomized experiments at a large international e-commerce platform.
Some interventions improved sales.
One produced no detectable sales increase.
Here are four of the results:
| Business process | AI intervention | Reported sales effect |
|---|---|---|
| Pre-purchase customer service | Answering customer questions | +16.3% |
| Search-query refinement | Better interpretation and translation | Approximately +2–3% |
| Product descriptions | Richer, structured product information | Approximately +2–3% |
| Google advertising titles | AI-optimized titles | No detectable increase |
These were separate experiments against different baselines—not gains that can be added together or applied to every business.
The most important detail sits behind the largest number.
In the customer-service experiment, 44,614 customers were assigned to different experiences. One group could receive help from an AI agent. The control group received a scripted message indicating that customer service was unavailable.
The AI-assisted group generated 16.3% more sales relative to that control.
The result did not show that AI beat an excellent human sales team.
It showed that useful assistance beat an unanswered question.
The opportunity was not “add AI.”
The opportunity was “stop leaving a customer without help.”
That is a much better starting point for a business conversation.
Does any of this sound familiar?
You do not need to understand model architectures to recognize an operational bottleneck.
You probably already hear it in your team’s daily conversations.
“We’ll send the quotation once we find the information.”
The request arrives.
Product details are in one file. Prices are in another. Availability requires a message to someone else.
The customer waits while your team assembles an answer.
A possible starting project:
Turn an incoming inquiry into a structured request, retrieve approved information, flag missing details, and prepare a quotation draft for review.
Not autonomous pricing.
Not unapproved commercial commitments.
A faster path to a correct, reviewable response.
What I would measure:
- Time from inquiry to quotation.
- Errors and manual corrections.
- Staff effort per quotation.
- Quotation-to-order conversion.
- Total operating cost.
“Our best people spend too much time looking things up.”
Your experienced employees know how to solve the problem.
But first, they have to find the contract, locate the policy, reconstruct the history, or compare several versions of a document.
A possible starting project:
Assemble relevant information with links to its sources, identify gaps, and prepare a draft for an expert to review.
The objective is not to remove expertise.
It is to reduce the preparation work surrounding it.
The test is simple:
Can the expert verify the result faster than doing the original task?
If verification takes just as long, the system has not earned its place.
“We’re growing, but everything feels slower.”
More customers.
More orders.
More exceptions.
More internal messages asking, “Who is handling this?”
A possible starting project:
Select one growing queue and test whether better intake, classification, information retrieval, or routing can reduce waiting.
Sometimes the answer will involve AI.
Sometimes it will be a clearer form, a software integration, or straightforward automation.
The customer should not have to fund an AI architecture when a simpler fix would do.
The companies I would investigate first
I would start with businesses where information delays sit close to revenue, service quality, or expensive employee time.
| Business type | Bottleneck worth investigating |
|---|---|
| Distributors and wholesalers | Slow inquiries, quotation preparation, scattered product information |
| Online retailers | Unanswered product questions, poor search, incomplete descriptions |
| Professional service firms | Document review, evidence gathering, repeated drafting |
| Support organizations | Repeated questions, difficult knowledge retrieval, slow onboarding |
| Growing service businesses | Intake backlogs, fragmented customer context, manual handoffs |
This is a practical prospecting framework—not proof that every company in these sectors needs a custom AI system.
The useful question is not:
“Are you in an AI-friendly industry?”
It is:
“Can we identify one costly problem that is specific enough to test?”
What a sensible first engagement should produce
Buying AI services should not require buying a grand vision before understanding the problem.
I would structure the first engagement around a decision:
“What is worth implementing here—and what should we leave alone?”
The output should make five things clear.
1. The bottleneck
A named process, a responsible owner, and a description of where work gets stuck.
“Improve productivity” is not specific enough.
“Reduce the time needed to prepare this type of quotation” is a starting point.
2. The baseline
What happens today?
How much work arrives? How long does it take? What needs correcting? What does it cost?
Where reliable measurements do not exist, establishing them is part of the work.
3. The information boundary
What data is required?
Where does it live?
Who may access it?
What is missing, outdated, or contradictory?
The goal is not automatically to centralize every dataset in the company. It is to establish whether the selected task has the information it needs.
4. The smallest useful intervention
A recommendation should distinguish between:
- A process change.
- Conventional automation.
- A ready-made AI tool.
- A custom AI application.
- An agent with tightly defined permissions.
Custom development should earn its complexity.
5. The pilot decision
A limited test with:
- A defined scope.
- An agreed budget.
- A primary outcome.
- Quality and safety guardrails.
- A responsible evaluator.
- Conditions for scaling, revising, or stopping.
A successful first engagement may conclude:
“This is worth building.”
It may also conclude:
“Fix the data first.”
Or:
“You do not need AI for this.”
That clarity is a deliverable—not a disappointment.
What I would not promise you
I would not take a percentage from another company’s case study and present it as your expected return.
I would not describe a working demo as a production-ready operation.
I would not propose an autonomous agent before defining what it is allowed to read, change, send, or approve.
And I would not use “AI transformation” as a substitute for a measurable objective.
A 16.3% sales lift in one experiment is evidence about that experiment.
Your business needs its own baseline, test, and economics.
The question is not whether the technology is impressive.
The question is whether the improvement survives contact with your actual workflow.
A better way to start the conversation
You do not need to arrive with an AI strategy.
You can arrive with a sentence:
“This task takes too long.”
“Customers keep waiting for this answer.”
“Our specialists repeat this work every day.”
“We have the information, but nobody can find it quickly.”
Those are useful starting points.
From there, the work is to separate symptoms from causes, identify the relevant information, and determine whether AI is the right intervention.
The best first project is not necessarily the biggest opportunity on a slide.
It is the one that produces credible evidence without exposing the business to unnecessary complexity.
Find the gap before you buy the system
Before approving another AI subscription or custom development proposal, ask:
“Which customer will wait less?”
“Which employee will finish a useful task sooner?”
“Which error will become less frequent?”
“How will we know?”
If the answers are vague, the project is not ready.
Start with the bottleneck.
Make the technology justify itself.
Let’s Find Your First Worthwhile AI Project
If your business is dealing with slow quotations, unanswered inquiries, document-heavy work, or operational backlogs, start with the process—not a shopping list of tools.
For consultation on AI use-case selection, data readiness, and focused implementation planning, visit:
https://almodarresieh.github.io/
When you get in touch, describe:
- The task that repeatedly slows your team down.
- How it is handled today.
- Where the relevant information lives.
- What a better outcome would look like.
You do not need a technical specification.
Bring one recurring problem worth investigating.
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