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<p>A useful automation business case makes its assumptions visible. It describes the current workflow, proposed change, implementation and operating costs, and how the business expects to use the improvement.</p><p>This framework is a planning tool. Its examples are hypothetical and do not predict ClearPath client results.</p><h2>Establish the baseline</h2><p>Start with a defined workflow and period, such as one month. Record transaction volume, active work time, rework, exception handling, and the people involved. Keep waiting time separate from paid work time.</p><p>Use a representative sample. A best-case demonstration or one rough estimate is a starting hypothesis, not a reliable baseline.</p><h2>Estimate the work that actually changes</h2><p>Calculate current effort and expected future effort, including human review and exception handling.</p><p>Monthly released hours = current monthly work hours minus expected monthly work hours after implementation.</p><p>Explain how the business will use the capacity: handle more volume, reduce overtime, clear a backlog, improve quality, or avoid a planned staffing increase.</p><p>Released time is not automatically cash savings. If expenses remain unchanged, label the benefit as capacity value unless there is a supported mechanism that changes cash flow.</p><h2>Include implementation and operating costs</h2><p>Account for discovery, design, development, integration, data preparation, evaluation, training, and rollout. Include internal staff time where it materially affects the decision.</p><p>Recurring costs may include software, model usage, hosting, monitoring, maintenance, human review, and exception handling. Avoid counting review effort twice if already included in expected work hours.</p><h2>A transparent illustrative model</h2><p>Hypothetical example, not a client result or market benchmark.</p><p>Suppose a workflow handles 1,000 items monthly. Current effort averages 12 minutes per item. The proposed workflow is estimated to require 5 minutes, including review and exceptions.</p><p>Current effort: 1,000 × 12 ÷ 60 = 200 hours per month.</p><p>Estimated future effort: 1,000 × 5 ÷ 60 = 83.3 hours per month.</p><p>Estimated released capacity: 116.7 hours per month.</p><p>At an assumed $50 per hour of loaded labor value, that represents approximately $5,833 of monthly capacity value.</p><p>If implementation costs $30,000 and incremental recurring costs are $1,000 monthly, the simple first-year model is:</p><p>($5,833 − $1,000) × 12 − $30,000 ≈ $28,000 in net modeled first-year value.</p><p>Dividing net value by the $42,000 total first-year investment gives a modeled return of approximately 67%.</p><p>This assumes a full year at the stated volume and effectiveness, no additional ramp-up costs, and that all released capacity has the assumed business value. It is not a forecast of cash profit or cash payback.</p><h2>Test assumptions that could change the decision</h2><p>Run a conservative case and a more favorable case. Change adoption, volume, review time, exception rates, implementation cost, and time to full use. Include the effect of a slower rollout.</p><p>Faster turnaround, fewer errors, or better service may create additional value. Quantify these benefits only when evidence supports them, and avoid counting the same benefit twice.</p><h2>Measure after launch</h2><p>Agree the baseline, measurement window, workflow owner, and success criteria before building. Compare actual volume, effort, quality, operating cost, and adoption with the assumptions.</p><p>A business case should improve the investment decision and provide a way to learn after launch. Precision in a spreadsheet cannot make an unsupported assumption true.</p><p>ClearPath assessments and transformation blueprints help define the business case before implementation. Use the Contact page to discuss your workflow.</p>

How to Calculate ROI on AI Automation

<p>The useful question is which approach fits each step of the work, and what should happen when that step cannot be completed reliably. A workflow can combine straightforward rules, AI-assisted interpretation, and human review. Define the job first, then choose the mechanisms.</p><h2>Start with rules when the rules are clear</h2><p>Traditional automation is a candidate when inputs are structured and the decision can be expressed explicitly. Examples include copying an approved record between systems, assigning a task based on a selected category, calculating a known formula, or sending a reminder after a defined interval.</p><p>Ask whether someone can write down the conditions and expected result without relying on interpretation. A rules-based step may be easier to inspect and test. It still needs handling for missing information, system failures, and unexpected inputs.</p><h2>Consider AI when the input needs interpretation</h2><p>AI assistance may be useful for varied documents, free-text requests, searching approved knowledge, summarizing material, or drafting an answer from context.</p><p>Define what counts as correct, which sources are permitted, and how a reviewer can verify the output. Specify what happens when the system is uncertain, contradictory, or wrong.</p><p>An impressive demonstration does not answer these questions. Evaluation should include representative inputs and difficult cases from the intended workflow, handled under appropriate information-sharing arrangements.</p><h2>Keep human judgment where consequences require it</h2><p>Some decisions depend on context, responsibility, or judgment that should remain with a person. A system can prepare information and highlight issues without making the final decision.</p><p>Separate the ability to draft or recommend from the permission to act. Generating a proposed customer response is different from sending it. Preparing a record update is different from changing the system of record.</p><h2>Example: a connected document workflow</h2><p>Illustrative example, not a client case study.</p><p>1. A document arrives through an approved channel.</p><p>2. A rules-based check confirms that the file type is supported.</p><p>3. AI assistance extracts agreed fields.</p><p>4. Validation rules flag missing fields or inconsistent values.</p><p>5. A person reviews exceptions and approves the output.</p><p>6. A conventional integration writes approved information to the next system.</p><p>AI handles a bounded interpretation task, explicit rules manage predictable checks, and a person owns the specified decisions.</p><h2>Evaluate the whole workflow</h2><p>Measure more than the speed of the AI step. Consider end-to-end cycle time, output quality, reviewer effort, exception volume, integration failures, operating costs, and user adoption.</p><p>A faster extraction step creates limited value if review becomes the new bottleneck. A modest automation may be worthwhile if it removes repeated handoffs and makes work easier to manage.</p><h2>A short decision checklist</h2><p>Can this step be expressed with clear rules? If interpretation is needed, can its output be evaluated? What information and permissions are required? Which actions require approval? Who handles exceptions and maintains the workflow? How will we know the complete process improved?</p><p>Choose the simplest combination that meets the operating requirement. The value comes from a dependable workflow, not from maximizing the number of AI components.</p><p>ClearPath helps scope AI agents, conventional automation, and process redesign around your business requirements. Use the Contact page to discuss your workflow.</p>

AI Automation vs. Traditional Automation: How to Choose

<p>An AI opportunity is not simply a task a model can perform. It is a change that could improve a real workflow, at an acceptable cost and level of risk, with someone responsible for making it work.</p><p>Start with the work rather than a list of tools. A useful first assessment should end with a small set of decisions you can act on.</p><h2>1. Find recurring friction</h2><p>Ask the people doing the work where they repeatedly copy information, search for answers, interpret documents, assemble reports, or chase missing inputs. Ask the workflow owner where delays, rework, and inconsistent outputs create a business problem.</p><p>Write down one concrete workflow at a time. “Use AI in operations” is too broad. “Prepare an onboarding file from forms and documents before an operations specialist reviews it” provides a useful boundary.</p><h2>2. Capture a baseline</h2><p>Record the trigger, finished output, people involved, systems used, and exceptions. Estimate volume, active work time, waiting time, and rework separately. Validate estimates with a representative sample before relying on them for an investment decision.</p><p>A task may require ten minutes of effort but spend several days waiting for approval. Faster drafting alone will not remove that bottleneck.</p><h2>3. Separate process problems from technology opportunities</h2><p>Look for unclear ownership, unnecessary approvals, duplicate information requests, and steps that no longer serve a purpose. Fixing these may reduce the need for automation or make a later build simpler.</p><p>Predictable rules and structured inputs may suit conventional automation. Interpretation, classification, drafting, or retrieval may be candidates for AI assistance, provided outputs can be checked and the consequences of errors are understood.</p><h2>4. Compare opportunities using explicit criteria</h2><p>• Business value: What could improve, and who would notice?<br>• Feasibility: Are data, system access, and integration options available?<br>• Quality: What errors are acceptable, and how would they be detected?<br>• Operating effort: Who reviews outputs, handles exceptions, and maintains the workflow?<br>• Adoption: Will the people involved use the new process?<br>• Ownership: Who can make decisions and accept the result?<br>A simple high/medium/low assessment can support discussion. Record the reasoning and confidence behind each rating. A numerical score cannot compensate for weak evidence.</p><h2>5. Select a bounded first initiative</h2><p>Choose a workflow with a clear owner, recurring volume, accessible inputs, and an observable output. Define what the first implementation includes and what remains manual.</p><p>A document workflow could begin by extracting a small set of fields and preparing a review queue. Automatically changing customer records may belong in a later phase after quality and operating controls have been evaluated.</p><p>Illustrative example, not a client case study: A services business might evaluate onboarding documents, recurring reporting, and internal knowledge search. Onboarding could be the best first candidate if it has a willing owner and accessible inputs, even if reporting appears to offer a larger theoretical benefit.</p><h2>The output: a decision-ready opportunity brief</h2><p>Summarize the problem, baseline, proposed change, required access, review requirements, estimated costs, benefit assumptions, risks, owner, and success criteria. That brief turns “we should do something with AI” into an initiative someone can scope and deliver.</p><p>Explore the AI Opportunity Assessment, request an AI strategy call, or read more insights.</p>

How to Identify AI Opportunities Inside Your Business

ClearPath Intake

ClearPath Intake is a founder-led AI transformation consultancy based in Silicon Valley. We help businesses identify practical AI opportunities, redesign processes, and implement AI agents, automation, internal tools, and business applications. Services include assessments, transformation planning, implementation, process redesign, and fractional delivery leadership.