The Tool Desk
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Why “where can we use AI?” is the wrong first question
AI can be applied to almost any department, so the question “Where can we use AI?” produces an endless list of experiments. It does not establish which business outcome matters, what is failing now or whether AI is appropriate.
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A better starting point is: What problem are we actually trying to solve? Sarah Wood, founder of Creative Sauce AI, captures the principle in three words: “Start with the work.” The technology can change; the desired outcome should remain clear.
Optivus Technologies describes the sequence as finding the process worth fixing, measuring its current cost, building the smallest system that can improve it, putting that system in the hands of real users and measuring the result. The companies that do this well are not necessarily those with the largest AI budgets.
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Find friction in the actual workflow
Start by observing what people do, not by listing tools. Ask employees: “What do you do repeatedly that feels like a waste of your time?” Look for work that contains:
- Repeated copying, re-entry or movement between spreadsheets and systems
- Searching for information, chasing approvals or checking the same details
- Rewriting documents, emails, notes or reports
- Queues created by handoffs or waiting for another system
- Manual interpretation of emails, forms, images, notes or reports
- Workarounds around software that does not fit the process
- Knowledge held by one person rather than documented or findable
Repeated movement usually indicates an integration or automation opportunity. Repeated interpretation—understanding language, classifying material or handling varied inputs—is where AI may help. A workaround may instead call for documentation, information architecture, process redesign or removal of a step.
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Map the process before selecting technology
Document one complete process from trigger to outcome. Include:
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- Information required at each stage and where it is stored
- Decisions, rules and exceptions
- Every handoff, waiting period and system switch
- Errors, rework and dropped tasks
- How often the process runs and who performs it
- The consequences for customers, employees, revenue, compliance or safety
Then establish a local baseline. Record current response time, error rate, consistency, capacity, visibility, dropped work, reliance on a single expert, customer experience and decision quality. The available evidence does not establish a universal productivity percentage for this approach; improvement has to be measured against the organization’s own starting point.
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Classify the problem before choosing the intervention
| Problem type | First intervention to consider | When AI becomes relevant |
|---|---|---|
| Process | Redesign the sequence, remove a step or clarify ownership | Only if flexible interpretation is still required after redesign |
| Information | Improve structure, findability, documentation and trust in the data | When people need help interpreting or retrieving varied material |
| Integration | Connect systems so people stop moving data manually | When inputs are unstructured and cannot be handled by fixed rules alone |
| Automation | Encode clear, repeatable rules with ordinary automation or formulas | When the rules are variable, linguistic or difficult to specify precisely |
| AI | Apply language, interpretation, reasoning or flexible handling | When those capabilities improve the measured outcome enough to justify their risk and cost |
Use a spreadsheet formula, a conventional automation, an integration or deletion of a needless step when it solves the problem reliably. The newest AI is not automatically the right AI.
Choose the smallest change that can work
Evaluate each candidate intervention against the same decision points:
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- Stay in Touch with Loved Ones: Share photos or videos from your phone to electronic wall calendar with app, instantly update your life moment, and keep in touch with your family, especially those who are far away; you can also surprise your smart calendar digital wall by sending him a wish card; it's also great for keeping tech-illiterate elders in touch with the youngsters.
- Expected impact on the defined outcome
- Implementation effort and speed to deploy
- Data quality and availability
- Integration complexity
- Cost of an error
- Amount of human review required
- Governance, privacy and security obligations
- How easily the change can be reversed
Compare AI with ordinary automation, integration, process redesign and doing nothing. A more sophisticated system should win only when it improves the outcome enough to warrant its additional complexity and controls.
Keep people where judgment and consequences matter
Do not label an entire job “automatable.” Break it into tasks. Keep people involved in unusual situations, high-consequence decisions, sensitive customer interactions, negotiation, trust-building and ambiguous choices. As task criticality rises, add review, escalation and explicit accountability.
A healthcare framework described in the evidence uses two dimensions—human judgment and workflow interconnectedness—to decide whether a task should be automated, augmented, hybridized or redesigned first. The practical lesson is to match the human role to the risk rather than treating automation as all-or-nothing.
The same principle appears in education as “pedagogy first, AI second.” Teachers and school leaders set instructional goals and retain professional judgment; AI can then create differentiated supports from standards and student work. The goal comes before the technology.
Put the change into real use and measure it
- Select a bounded workflow. Choose one process with a visible baseline and a clear owner.
- Define the target outcome. Specify what should improve and by how much, without assuming AI is the answer.
- Build the smallest intervention. Start with a redesigned step, documented information, an integration or rule-based automation before adding flexible AI.
- Design review and escalation. State which outputs require approval, what happens when confidence is low and who is accountable for the final decision.
- Deploy with the people doing the work. Observe real usage, exceptions and workarounds rather than relying on a demonstration.
- Compare results with the baseline. Track response time, errors, consistency, capacity, visibility, dropped tasks, dependence on one expert, customer experience and decision quality.
- Expand only after evidence. Keep, revise, replace or remove the intervention based on measured results and its governance burden.
Microsoft Advertising’s guidance is direct: “First, focus on workflow transformation before tool deployment.” That order keeps the work—and the people accountable for it—at the center of the decision.
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A practical test for any proposed AI project
- Can the problem be solved by removing or simplifying a step?
- Are the necessary facts structured, current and trustworthy?
- Is the task interpretation-heavy rather than merely repetitive data movement?
- What is the cost of a wrong answer, and who reviews it?
- Can the organization measure improvement against today’s baseline?
- Can the change be stopped or reversed if it makes the workflow worse?
If these questions cannot be answered, the next step is workflow assessment or process mapping—not another tool trial.
Quick Recap
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