Every operations leader knows their team spends too much time on manual work. The challenge is not identifying the problem. It is quantifying it well enough to get budget approval.
Here is a framework for building a business case for AI workflow automation that speaks the language of finance: concrete numbers, conservative assumptions, and a clear payback timeline.
Start with three numbers:
Team size involved in workflows. Count every person who touches manual processes daily. For most mid-market operations teams (50-500 employees), this is 10-30 people in the core workflow chain.
Fully-loaded hourly cost. Take each person's total compensation (salary + benefits + overhead) and divide by 2,080 annual working hours. For most professional roles, this lands between $35-$75 per hour.
Hours per week spent on manual workflow tasks. This is the hardest number to pin down because people underestimate it. Include data entry, routing documents, chasing approvals, sending follow-ups, updating systems, and creating status reports. Our research across 100+ implementations shows the average is 10-15 hours per person per week, which aligns with data showing teams waste 20-30% of capacity on repetitive work.
The formula: Team Size x Hourly Cost x Manual Hours/Week = Weekly Cost of Manual Work
Example: 15 people x $50/hour x 12 hours/week = $9,000/week in manual workflow cost. That is $468,000 per year.
Not everything can or should be automated. Tasks requiring human judgment (client negotiations, strategic decisions, creative work) stay with your team. Tasks requiring human hands but not human thinking (data entry, routing, follow-ups, system updates) are automation candidates.
Use these ranges as a guide:
Conservative (40-50%): You have limited process documentation and some workflows are highly variable.
Moderate (50-70%): You have established processes with clear rules for most steps. This is where most mid-market companies fall.
Aggressive (70-80%): Your workflows are well-documented, rule-based, and consistent. You have tried automation before and understand what works.
Continuing our example at a moderate 60% automation rate: $468,000 x 60% = $280,800 in annual automatable costs.
AI agents do not automate 100% of the automatable work from day one. Plan for a ramp-up:
Month 1: Agent deployment and calibration. Expect 50-60% of the automatable work to be handled.
Month 2-3: Optimization and expansion. Expect 70-80% coverage as the agents learn your workflows and edge cases.
Month 4+: Steady state. Expect 80-90% coverage with continuous improvement.
Annualized savings using a conservative steady-state of 80%: $280,800 x 80% = $224,640/year in recaptured capacity.
In hours, that is roughly: 15 people x 12 hours x 60% automation x 52 weeks = 5,616 hours/year. That is the equivalent of 2.7 full-time employees.
A typical Dyntyx engagement involves an initial deployment phase (first 30 days) followed by ongoing optimization and support. Without quoting specific pricing (which varies by scope), here is how to frame the comparison for your CFO:
Payback period = Monthly investment / Monthly savings. If your monthly savings are $18,000+ and your investment is a fraction of that, the payback period is measured in weeks, not months.
Total ROI = (Annual savings - Annual investment) / Annual investment. For most mid-market AI agent deployments, we see 3-5x annual ROI.
Compare against alternatives. Hiring 2-3 additional staff to handle the manual work would cost $150,000-$300,000/year in salary alone, plus recruiting, training, management overhead, and the 6+ months to full productivity. AI agents deploy in under 30 days.
Some of the most important benefits are harder to quantify but matter enormously to your organization:
Error reduction. Manual data entry and routing have inherent error rates. AI agents are consistent and auditable. Fewer errors mean fewer costly corrections, better compliance, and higher client satisfaction.
Scalability. When your business grows 30%, do you need 30% more staff? With AI agents, the same system handles increased volume without proportional headcount increases.
Employee satisfaction. Your best people did not sign up to do data entry. Freeing them from repetitive tasks improves retention, engagement, and the quality of their high-judgment work.
Speed. Faster workflows mean faster client delivery, faster month-end closes, faster compliance reporting, and faster response to market changes.
For your CFO meeting, distill everything above into one page:
The Problem: Our team spends [X] hours/week on manual workflows, costing [$Y]/year in operational capacity.
The Solution: AI agents that automate [Z%] of manual workflows, deploying in under 30 days.
The Numbers: Projected annual savings of [$A], payback period of [B weeks/months], with [C] FTE-equivalent capacity recovered.
The Risk: Low. We start with one workflow, measure results, and expand only if the ROI proves out.
The Ask: Approve [$D]/month for an initial 90-day deployment. We will have measurable results within 30 days.
We built a free ROI Calculator that walks you through this entire framework with your own numbers. Download it and run the analysis in 5 minutes.
If you want a more detailed assessment, our AI Readiness Assessment scores your organization across 6 dimensions and provides a personalized recommendation.
Dyntyx builds AI agents that own your workflows end-to-end. Every engagement ships with explicit KPIs so you can measure the ROI from day one. Book a discovery call to get your custom ROI analysis.
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