ROI of AI Projects: Framework, Formulas, and Practical Examples

Measuring and proving the return on investment of AI projects is the central challenge for businesses that want to invest in AI automation or that have already invested. According to research, 41 percent of companies cannot quantify the ROI of their AI initiatives. This uncertainty blocks follow-up investments, weakens arguments to executive leadership, and prevents successful pilot projects from scaling across the organization. Yet ROI measurement for AI automation is not rocket science — it simply requires the right methodology, clear metrics, and an honest assessment of all costs and benefits.

This article delivers the complete methodology: from the ROI formula with derivation, through quantification of direct and indirect savings, to five detailed calculation examples from different industries. Supplemented by a KPI dashboard with twelve metrics, a TCO calculation guide, and battle-tested strategies for selling the business case to executive leadership.

The ROI Formula for AI Automation: Derivation and Application

The Basic ROI Formula

The return on investment is fundamentally calculated as the ratio of net gain to total investment, multiplied by 100 percent:

ROI = ((Total Benefits - Total Costs) / Total Costs) x 100

For AI automation projects, this formula must be expanded, as both benefits and costs comprise multiple components.

The Extended ROI Formula for AI Automation

ROI = ((Direct Savings + Indirect Savings + Revenue Increase - Total Costs) / Total Costs) x 100

Where the components are defined as follows:

Direct Savings = Saved labor time (in hours) multiplied by fully loaded hourly rate + Eliminated error costs + Saved license or vendor costs

Indirect Savings = Faster cycle times (quantified as opportunity costs) + Improved employee satisfaction (reduced turnover) + Enhanced compliance (avoided fines and reputational damage)

Revenue Increase = Additional deals through improved lead qualification + Higher customer retention through better service + Cross-selling and upselling potential through better data analysis

Total Costs = One-time implementation costs + Ongoing operational costs (infrastructure, licenses, APIs) + Internal personnel costs (maintenance, development) + Training and change management costs

Calculating the Payback Period

Complementing ROI, the payback period is a critical metric:

Payback Period (months) = Total Investment / Monthly Net Savings

Monthly net savings equal monthly savings minus monthly operating costs. For typical AI automation projects, the payback period ranges from three to six months: significantly faster than most other IT investments.

Critical Notes on ROI Calculation

First: Always use the fully loaded hourly rate, not the gross salary. The fully loaded rate encompasses salary, social contributions, workspace costs, IT equipment, proportional management costs, and overhead. An employee with a $60,000 gross annual salary typically costs the company $85,000 to $95,000 per year — equivalent to an hourly rate of $48 to $54 based on 1,760 working hours per year.

Second: Be conservative. Calculate with the lower end of expected savings and the upper end of expected costs. An ROI that exceeds expectations is better than one that falls short of projections.

Third: Document all assumptions. Every number in your ROI calculation should be based on a traceable assumption that has been validated by the involved business departments.

Quantifying Direct vs. Indirect Savings

Direct Savings: Concrete and Measurable

Direct savings are the simplest part of the ROI calculation, as they are immediately measurable and attributable.

Labor Time Savings: The most significant direct savings component. Calculate as: (Minutes per process execution x Number of executions per month / 60) x Fully loaded hourly rate. Example: An invoice processing workflow saves 10 minutes per invoice. At 500 invoices per month and a $50 hourly rate: (10 x 500 / 60) x $50 = $4,167 per month.

Error Cost Elimination: Manual processes produce errors: typos, incorrect data entries, missed deadlines, wrong routing. Each error has a cost: the time to identify and correct it, plus any downstream impact (wrong payments, customer complaints, compliance violations). Quantify error costs as: (Number of errors per month x Average correction time per error x Hourly rate) + Average downstream cost per error. Example: 50 errors per month, 20 minutes average correction time, $50/hour rate, $100 average downstream cost: (50 x 20/60 x $50) + (50 x $100) = $5,833 per month.

License and Vendor Cost Elimination: Automation sometimes replaces paid tools or services. If an AI workflow replaces a $500/month OCR service or a $2,000/month outsourced data entry contract, these are direct savings.

Indirect Savings: Real but Harder to Quantify

Indirect savings are real economic benefits that do not appear directly on the balance sheet but have measurable impact over time.

Faster Cycle Times: When a process completes in minutes instead of hours or days, the speed improvement has economic value. For revenue-impacting processes, calculate the opportunity cost of delay. If faster invoice processing means better supplier relationships and 2 percent early payment discounts on $500,000 annual purchases, that represents $10,000 per year in indirect savings.

Reduced Employee Turnover: Employees who spend less time on repetitive tasks are more satisfied and less likely to leave. Each avoided turnover event saves $15,000 to $45,000 in recruiting, onboarding, and productivity ramp-up costs. If automation demonstrably reduces turnover by even one employee per year, the savings are substantial.

Improved Compliance: Automated processes follow rules consistently. This reduces compliance violations, audit findings, and the associated remediation costs. In regulated industries, a single compliance violation can cost hundreds of thousands of dollars in fines and legal fees.

Enhanced Customer Experience: Faster response times, fewer errors, and more consistent service quality improve customer satisfaction and retention. Quantify through Customer Lifetime Value (CLV) impact: if automation improves retention by 5 percent and average CLV is $10,000, the revenue protection is significant.

Five Detailed ROI Calculation Examples

Example 1: Invoice Processing Automation for a Distribution Company

Scenario: A distribution company processes 800 incoming invoices per month. Current process: manual entry into the ERP system.

Current costs: 15 minutes per invoice x 800 invoices = 200 hours per month. At $48/hour fully loaded: $9,600 per month. Error rate: 6 percent = 48 errors per month. Error correction: 25 minutes per error x $48/hour = $960 per month. Downstream error costs (wrong payments, credit notes): $2,400 per month. Total monthly cost: $12,960.

Automation costs: Implementation: $12,000 (one-time). Infrastructure: $200/month. API costs: $100/month. Maintenance (5 hours/month): $240/month. Residual manual oversight (20 hours/month): $960/month. Total monthly operating cost: $1,500.

Savings: Monthly savings: $12,960 - $1,500 = $11,460. Annual savings: $137,520. ROI after 12 months: ($137,520 - $12,000 - $18,000) / $12,000 x 100 = 896 percent. Payback period: $12,000 / $11,460 = 1.05 months.

Example 2: Customer Service Automation for a SaaS Company

Scenario: A SaaS company with 2,000 monthly support tickets. Three full-time support agents.

Current costs: 3 agents x $4,500/month fully loaded = $13,500/month. Average resolution time: 22 minutes per ticket.

Automation results: 45 percent of tickets auto-resolved. Remaining tickets: AI-assisted with suggested responses, reducing resolution time to 8 minutes. New staffing: 2 agents can handle remaining volume. Automated monthly cost: 2 agents x $4,500 = $9,000 + infrastructure $300 + API costs $200 = $9,500/month.

Savings: Monthly savings: $13,500 - $9,500 = $4,000 in direct costs + $3,000 estimated value of improved response time (customer retention) = $7,000 total. Implementation cost: $18,000. ROI after 12 months: ($84,000 - $18,000) / $18,000 x 100 = 367 percent. Payback period: 2.6 months.

Example 3: Lead Qualification Automation for a B2B Sales Team

Scenario: A B2B company generates 500 leads per month. Sales team of five representatives spending 30 percent of their time on lead qualification instead of closing.

Current cost of lead qualification: 5 reps x 0.3 x $6,000/month = $9,000/month in opportunity cost. Conversion rate: 3 percent.

Automation results: AI scores and qualifies all leads automatically. Sales reps spend 95 percent of time on qualified opportunities. Conversion rate improves to 4.5 percent (50 percent increase through better focus). Revenue impact: At $20,000 average deal value: (500 x 0.045 - 500 x 0.03) x $20,000 = $150,000 additional annual revenue.

Automation costs: Implementation: $15,000. Monthly: $500. Annual operating: $6,000.

ROI after 12 months: ($150,000 + $108,000 time savings - $15,000 - $6,000) / $21,000 x 100 = 1,129 percent. Payback period: under 1 month.

Example 4: Reporting Automation for a Financial Services Firm

Scenario: A financial services firm produces 12 weekly reports and 4 monthly reports, each requiring data from 6 systems.

Current costs: Weekly reports: 8 hours each x 12 reports x 4 weeks = 384 hours/month. Monthly reports: 16 hours each x 4 = 64 hours/month. Total: 448 hours/month. At $55/hour: $24,640/month.

Automation results: Fully automated data collection and report generation. Human review and commentary: 2 hours per weekly report + 4 hours per monthly report = 112 hours/month. At $55/hour: $6,160/month. Infrastructure: $400/month.

Savings: Monthly: $24,640 - $6,560 = $18,080. Implementation: $22,000. ROI after 12 months: ($216,960 - $22,000) / $22,000 x 100 = 886 percent. Payback period: 1.2 months.

Example 5: HR Onboarding Automation for a Growing Company

Scenario: A company hires 15 employees per month. HR team spends 12 hours per onboarding.

Current costs: 15 hires x 12 hours x $45/hour = $8,100/month. Additional cost of delays and missed steps: estimated $2,000/month (delayed productivity, compliance gaps).

Automation results: HR administrative time reduced to 2 hours per onboarding. Completion rate: 98 percent (up from 72 percent). Time-to-productivity reduced by 35 percent.

Savings: Direct: (15 x 10 hours x $45) + $2,000 delay elimination = $8,750/month. Indirect (faster productivity): 15 hires x 1 week earlier productivity x $1,200/week = $18,000/month. Total monthly value: $26,750. Implementation: $10,000. ROI after 12 months: ($321,000 - $10,000 - $3,600) / $13,600 x 100 = 2,261 percent. Payback period: under 1 month.

The KPI Dashboard: Twelve Metrics for AI Automation Success

Efficiency KPIs (Metrics 1-3)

1. Time Saved (hours/month): The most intuitive metric. Measure the total hours of manual labor eliminated by automation each month. Track at the individual workflow level and aggregate across the portfolio.

2. Cycle Time Reduction (percent): How much faster does the process complete end-to-end? Measure from trigger to completion. Important for customer-facing processes where speed directly affects experience.

3. Automation Rate (percent): What percentage of total process executions are fully automated without human intervention? Track this over time: a rising automation rate indicates maturing workflows and improving AI accuracy.

Quality KPIs (Metrics 4-6)

4. Error Rate (percent): The percentage of automated executions that produce incorrect results. Track absolute error rate and compare against the manual process baseline. Target: below 1 percent for mature automations.

5. Rework Rate (percent): The percentage of automated outputs that require manual correction or rework. Distinct from error rate: rework may include formatting adjustments or edge cases that are not errors but require human attention.

6. First-Time-Right Rate (percent): The percentage of executions that produce the correct result on the first attempt without any human intervention. The inverse of the rework rate. Target: above 95 percent for mature automations.

Cost KPIs (Metrics 7-9)

7. Cost per Transaction: The total cost (infrastructure, API, human oversight) divided by the number of automated transactions. Track over time: this should decrease as volume increases and workflows mature.

8. Total Cost of Ownership (monthly): All-in monthly cost including infrastructure, licenses, maintenance, and human oversight. Compare against the pre-automation cost baseline.

9. Savings vs. Baseline (monthly): The concrete monetary savings compared to the pre-automation cost structure. This is the number that executive leadership cares most about.

Value KPIs (Metrics 10-12)

10. Return on Investment (percent): The cumulative ROI calculated using the extended formula. Track monthly and present cumulatively to show the growing value of the investment.

11. Payback Period (months): Time from implementation to the point where cumulative savings exceed total investment. Report actual vs. Projected to build credibility for future projects.

12. Net Present Value (NPV): For multi-year assessments, discount future savings to present value. This provides a more accurate picture than simple ROI for investments with long-term impact.

Total Cost of Ownership: The Complete Calculation

One-Time Costs

Discovery and analysis: Process audit, requirements definition, vendor evaluation. Typical range: $2,000 to $8,000.

Implementation: Workflow development, AI configuration, integration setup, testing. Typical range: $5,000 to $40,000 depending on complexity.

Infrastructure setup: Server provisioning, platform deployment, security configuration. Typical range: $1,000 to $5,000 for self-hosted deployments.

Training: End-user training, administrator training, documentation. Typical range: $1,000 to $5,000.

Change management: Communication, stakeholder management, transition support. Typical range: $2,000 to $10,000.

Recurring Costs

Infrastructure: Server hosting, database, storage. Typical range: $100 to $1,500/month depending on scale.

AI model costs: API costs for external models or compute costs for self-hosted models. Typical range: $50 to $500/month.

Maintenance: Bug fixes, updates, monitoring. Typical range: 5 to 10 hours/month of technical time.

Support and training: Ongoing user support and refresher training. Typical range: 2 to 5 hours/month.

Hidden Costs to Account For

Opportunity cost of internal resources: Time that internal team members spend on the project could be spent on other initiatives. Quantify this at the fully loaded hourly rate.

Integration maintenance: External systems change their APIs, data formats, and authentication methods. Budget 2 to 4 hours per month for integration maintenance across the portfolio.

Scaling costs: As automation volume grows, infrastructure costs increase (though sub-linearly). Budget for 15 to 25 percent annual infrastructure cost increases to account for volume growth.

Advanced ROI Techniques: Beyond the Basics

Scenario Analysis: Conservative, Realistic, and Optimistic

Never present a single ROI number. Executive decision-makers distrust single-point estimates because they know the future is uncertain. Instead, present three scenarios:

Conservative scenario: Lower-bound savings estimates, upper-bound cost estimates. Assumes the automation achieves only 70 percent of the expected performance. This is the scenario you should be comfortable guaranteeing.

Realistic scenario: Best-estimate savings and costs based on comparable projects and validated assumptions. This is the most likely outcome with professional implementation.

Optimistic scenario: Upper-bound savings, lower-bound costs. Assumes everything goes well and the automation performs at the high end of expectations.

Present all three scenarios in a table format that clearly shows the investment, annual savings, payback period, and one-year ROI for each scenario. This approach demonstrates analytical rigor and builds credibility. If even the conservative scenario shows positive ROI within six months, the investment decision becomes straightforward.

Net Present Value for Multi-Year Assessments

For larger investments or strategic automation initiatives spanning multiple years, supplement ROI with Net Present Value (NPV) analysis. NPV accounts for the time value of money: a dollar saved next year is worth less than a dollar saved today.

NPV = Sum of (Annual Net Benefits / (1 + Discount Rate)^Year) - Initial Investment

Use your company's weighted average cost of capital (WACC) as the discount rate, typically 8 to 12 percent for mid-sized businesses. A positive NPV indicates the investment creates value beyond the cost of capital. For a typical AI automation project with a $15,000 initial investment, $60,000 annual savings, and $12,000 annual operating costs, the five-year NPV at a 10 percent discount rate exceeds $150,000.

Sensitivity Analysis: Identifying What Matters Most

A sensitivity analysis tests how changes in key assumptions affect the ROI. For each major assumption (labor cost per hour, error rate, automation rate, implementation cost), calculate the ROI at plus and minus 20 percent of the assumed value. This reveals which assumptions have the greatest impact on the business case: and therefore which ones require the most careful validation.

For most AI automation projects, the sensitivity analysis reveals that two factors dominate: the fully loaded hourly rate and the automation rate. A 20 percent change in either of these assumptions typically swings the ROI by 30 to 50 percent. Conversely, implementation cost variations have a much smaller impact because the ongoing savings quickly dwarf the one-time investment.

Portfolio ROI: Measuring the Automation Program

Beyond individual project ROI, track the aggregate ROI of your entire automation portfolio. The portfolio view reveals whether your automation investments are accelerating (each new project delivers higher ROI than the last, a sign of growing organizational capability), plateauing (ROI is stable, possibly indicating that easy wins are exhausted and more complex opportunities lie ahead), or declining (newer projects deliver lower ROI, a signal to reassess your project selection methodology).

Portfolio ROI also enables more sophisticated resource allocation: shifting investment toward project categories with the highest demonstrated returns and away from categories that underperform.

Common ROI Calculation Mistakes to Avoid

Mistake 1: Using Gross Salary Instead of Fully Loaded Cost

This is the most common mistake and systematically underestimates savings. The fully loaded cost of an employee is typically 1.4 to 1.7 times their gross salary. Using gross salary instead of fully loaded cost underestimates savings by 40 to 70 percent. Always use the fully loaded rate, and document the calculation transparently.

Mistake 2: Ignoring Error Costs

Manual processes generate errors, and errors generate costs, correction time, downstream impact, customer complaints, compliance violations. Many ROI calculations only count time savings and ignore the substantial savings from error elimination. In industries with high accuracy requirements (finance, healthcare, legal), error cost elimination often exceeds time savings as the primary benefit driver.

Mistake 3: Counting Task Elimination Instead of Time Reallocation

Automation rarely eliminates jobs entirely, it frees up time that employees then spend on other tasks. The ROI should reflect the value of the reallocation, not the elimination of the position. If automation frees an employee to spend 20 hours per month on higher-value activities (business development, customer relationships, strategic projects), the value may exceed the pure time-cost savings.

Mistake 4: Neglecting Change Management Costs

A technically perfect automation that nobody uses generates zero ROI. Budget 15 to 25 percent of the total project cost for change management activities: communication, training, coaching, and transition support. Include these costs in the ROI calculation for an honest assessment.

Mistake 5: Setting the Measurement Period Too Short

Measuring ROI after one month of operation often misses important dynamics: the learning curve (automation accuracy improves over the first weeks), the adoption curve (usage increases as employees become comfortable), and the scaling effect (volume increases as confidence grows). Measure ROI at 30, 90, and 180 days for a complete picture.

Selling the Business Case to Executive Leadership

Speaking the Language of Leadership

Executive leadership is not interested in technical details, workflow diagrams, or AI model names. They care about three things: How much does it cost? What does it deliver? How fast will I see results?

Structure your business case accordingly:

Page 1, Executive Summary: Investment amount (e.g., $12,000), expected annual savings (e.g., $48,000), payback period (e.g., 3 months), ROI after 12 months (e.g., 300 percent).

Page 2: Problem and Solution: Concretely describe the problem being solved. Not in AI jargon, but in business language. Example: Our accounting team spends 60 hours per month on manual invoice entry. Automation reduces this to 8 hours. Eliminates 95 percent of data entry errors.

Page 3: Detailed Calculation: The complete ROI calculation with all assumptions, costs, and savings. Conservative and optimistic scenarios.

Page 4: Risk Assessment and Mitigation: What risks exist? How are they addressed? This demonstrates professionalism and increases confidence.

The Pilot Approach as Door Opener

When leadership hesitates, the pilot approach is the most effective strategy. It minimizes risk and delivers hard facts. Choose a single, well-defined process with high ROI potential and manageable complexity. Define measurable success criteria upfront. Agree on a limited budget ($5,000 to $15,000) and a clear timeline (six to eight weeks). Conduct a baseline measurement, implement the automation, and measure results after four weeks of production operation. Present results to leadership with hard numbers, a comparison to projections, and a recommendation for follow-on projects.

When the pilot succeeds. With professional selection and execution, it does in over 90 percent of cases: the business case for further projects has made itself. Leadership then has proven results from their own organization, not theoretical ROI projections.

Handling Common Leadership Objections

Objection: We have other priorities right now. Response: AI automation creates capacity for exactly those priorities. Every hour freed through automation can be redirected to strategically important work. And a pilot project with a $5,000 to $15,000 investment and three-month payback is not a major resource commitment.

Objection: Our team has been doing it this way for years: why change? Response: The question is not whether the current process works, but whether it is optimal. Your team spends X hours per month on repetitive manual work that a machine can do faster and more accurately. That time is missing from value-creating activities.

Objection: Can we afford this? Response: The better question is: Can we afford not to do it? Every month without automation costs Y dollars in personnel time and error costs. The investment pays for itself in Z months.

Objection: What if it does not work? Response: That is exactly why we use the pilot approach. We invest a limited amount in a defined test. If it works: and experience strongly suggests it will, we scale. If not, the financial risk is minimal and we have gained valuable insights.

Presentation Template for the Business Case

For presenting to executive leadership, we recommend the following structure that can be presented in a maximum of 15 minutes:

Slide 1, Title and Summary: Project name, investment amount, expected ROI, payback period. Everything at a glance.

Slide 2: The Problem: Concrete description of the current pain point. Numbers and facts: How many hours, how many errors, what does the status quo cost per month?

Slide 3: The Solution: What is being automated? How does it work? Brief and understandable, without technical jargon. A simple before-and-after diagram.

Slide 4: The Numbers: Detailed cost-benefit analysis. Conservative, realistic, and optimistic scenarios. Break-even point clearly marked.

Slide 5: The Plan: Timeline, milestones, responsibilities. Clear and achievable.

Slide 6: The Next Step: Concrete recommendation. Budget approval for the pilot. Date for the results presentation.

This structure is proven and has been effective in numerous businesses to win executive leadership for AI automation projects.

Frequently Asked Questions (FAQ) on ROI Measurement for AI Projects

How do I calculate the ROI of an AI automation project?

Calculate ROI using the extended formula: ROI = ((Direct Savings + Indirect Savings + Revenue Increase - Total Costs) / Total Costs) x 100. Direct savings include saved labor time, eliminated error costs, and reduced license fees. Indirect savings include faster cycle times, improved employee satisfaction, and enhanced compliance. The critical detail: use the fully loaded hourly rate per employee, not the gross salary, and calculate conservatively.

How long does it take for an AI project to pay for itself?

Payback periods for professionally executed AI automation projects typically range from three to six months. Simple process automations like email classification or document extraction often pay for themselves within six to eight weeks. More complex projects like full workflow automation require four to six months. Compared to traditional IT projects that often take 18 to 24 months to break even, the payback period for AI automation is exceptionally short.

Which KPIs should I track for AI projects?

For a complete picture, monitor twelve core metrics across four categories: Efficiency KPIs like time savings in hours, cycle time, and automation rate; Quality KPIs like error rate, rework rate, and first-time-right rate; Cost KPIs like cost per transaction, total cost of ownership, and savings vs. Baseline; and Value KPIs like ROI, payback period, and net present value. Start with three to five KPIs and expand the dashboard gradually.

Can I prove ROI even for hard-to-measure benefits?

Yes, by quantifying indirect benefits through proxy metrics. Improved employee satisfaction can be measured through reduced turnover, each avoided new hire saves $15,000 to $45,000 in recruiting and onboarding costs. Faster customer service response times demonstrably correlate with higher customer retention, which can be quantified through Customer Lifetime Value. Even soft factors like employer attractiveness can be quantified through reduction in time-to-hire.

How do I convince leadership to approve an AI pilot project?

The most effective approach is the three-step business case: First, speak the language of leadership, costs, benefits, payback instead of technical details. Second, propose a limited pilot ($5,000 to $15,000 budget, six to eight week timeline) that minimizes financial risk. Third, define measurable success criteria upfront and present hard results after the pilot. Experience shows: over 90 percent of professionally executed pilot projects exceed their projected ROI values. Then the business case for follow-on projects practically sells itself.

How do I track ROI continuously after implementation?

Build an automated ROI tracking dashboard that updates in real time. The dashboard should show cumulative savings vs. Investment (the running ROI), monthly savings trends, quality metrics (error rate, first-time-right rate), and usage metrics (automation rate, volume processed). Schedule monthly review meetings where the automation team presents current ROI data to stakeholders. This continuous visibility maintains organizational support and provides the data foundation for justifying future automation investments. Most organizations find that actual ROI consistently exceeds initial projections by 20 to 40 percent as automations mature and volumes increase.

What does a typical AI automation project cost?

Total costs vary significantly by complexity and scope. A simple AI workflow for document processing ranges from $5,000 to $15,000 in implementation costs plus $200 to $500 monthly operating costs. More complex projects like full customer service automation range from $20,000 to $50,000 implementation plus $500 to $1,500 monthly. Critical for TCO calculation: in addition to direct costs, account for internal personnel costs for project management, training, and ongoing maintenance. Open-source solutions on your own infrastructure often provide a 40 to 60 percent cost advantage over SaaS platforms, especially at growing automation volumes.

Conclusion: ROI Measurement Is Not Optional: It Is Essential

Systematic ROI measurement for AI automation projects is not an optional add-on, it is a strategic necessity. Only organizations that can quantify the value of their automations will secure leadership support for continued investment, obtain the budgets needed for enterprise-wide scaling, prioritize the right projects, and overcome internal resistance to change with hard facts.

The good news: AI automation delivers above-average ROI values, typically 300 to 800 percent in the first year. No other IT investment offers a comparable cost-benefit ratio. Use the methodology described in this article to make this value transparent and convince your organization of the strategic importance of AI automation.

Sophera Consulting supports businesses with ROI calculation, business case development, and success measurement for AI automation projects, from initial potential analysis through implementation to continuous optimization. Every project is set up with clear KPIs and transparent ROI tracking from day one. Contact us for a complimentary ROI assessment and discover the concrete, measurable savings potential in your organization's processes and workflows today.