{"id":127,"date":"2026-04-27T03:07:38","date_gmt":"2026-04-26T23:07:38","guid":{"rendered":"https:\/\/blog.neomeric.com\/?p=127"},"modified":"2026-07-11T23:57:28","modified_gmt":"2026-07-11T19:57:28","slug":"how-to-calculate-ai-product-development-roi","status":"publish","type":"post","link":"https:\/\/neomeric.com\/blog\/how-to-calculate-ai-product-development-roi\/","title":{"rendered":"How to Calculate ROI on AI Product Development"},"content":{"rendered":"\n<p>Well-scoped AI product investments typically return 150\u2013300% over a 2\u20133 year timeline, based on Neomeric&#8217;s engagement benchmarks and published industry research. For mid-market companies investing $80,000\u2013$200,000 in an AI minimum viable product, break-even typically arrives within 12\u201318 months. But without a clear framework to calculate costs and returns before you build, most teams underestimate total investment \u2014 often by half or more \u2014 and miss the metrics that actually matter.<\/p>\n\n\n\n<p>This guide from Neomeric, a Melbourne-based AI product development consultancy, walks through exactly how to calculate ROI on AI product development \u2014 before you commit a dollar. Whether you are evaluating your first AI project or building a business case for a board, here is the framework that works.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"s-what-is-roi-in-the-context-of-ai-product-development\">What Is ROI in the Context of AI Product Development?<\/h2>\n\n\n\n<p>ROI for AI product development is the ratio of net returns generated by your AI product \u2014 cost savings, revenue uplift, efficiency gains, and risk reduction \u2014 to the total investment required to build, deploy, and operate it. Unlike traditional software ROI, AI product ROI must account for ongoing model training costs, data infrastructure, and iteration cycles that compound over time.<\/p>\n\n\n\n<p>The standard formula is:<\/p>\n\n\n\n<p><strong>ROI (%) = ((Total Returns \u2212 Total Investment) \u00f7 Total Investment) \u00d7 100<\/strong><\/p>\n\n\n\n<p>However, AI ROI has three distinct components that traditional software ROI often ignores:<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li><strong>Direct financial returns<\/strong> \u2014 revenue generated or costs eliminated by the AI product<\/li><li><strong>Operational efficiency gains<\/strong> \u2014 time saved, error reduction, and throughput improvements<\/li><li><strong>Strategic value<\/strong> \u2014 competitive differentiation, proprietary data assets, and organisational capability built<\/li><\/ol>\n\n\n\n<p>Organisations that measure AI ROI comprehensively \u2014 including operational and strategic value \u2014 are consistently more likely to report positive returns than those tracking direct financial returns alone. That gap is the cost of measuring ROI too narrowly.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"s-how-do-you-calculate-the-total-cost-of-building-an-ai-product\">How Do You Calculate the Total Cost of Building an AI Product?<\/h2>\n\n\n\n<p>The total cost of an AI product has four distinct categories. Missing any one of them is the single most common reason AI ROI calculations fall apart.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-category-1-development-costs\">Category 1: Development Costs<\/h3>\n\n\n\n<ul class=\"wp-block-list\"><li>Data collection, cleaning, and labelling: $15,000\u2013$80,000 depending on dataset size and quality<\/li><li>Model development or fine-tuning: $20,000\u2013$150,000<\/li><li>Infrastructure setup (cloud, compute, storage): $10,000\u2013$40,000 initial configuration<\/li><li>API integrations with existing systems: $15,000\u2013$60,000<\/li><li>UI\/UX and product layer: $20,000\u2013$60,000<\/li><\/ul>\n\n\n\n<p><strong>Typical AI MVP build total: $50,000\u2013$300,000<\/strong>, with most mid-market focused builds (12-week scope) landing between $80,000 and $150,000. If you are considering a focused AI MVP, our guide on <a href=\"https:\/\/neomeric.com\/blog\/how-to-build-an-ai-mvp-in-30-days\/\">how to build an AI MVP in 30 days<\/a> covers the week-by-week process.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-category-2-ongoing-operational-costs\">Category 2: Ongoing Operational Costs<\/h3>\n\n\n\n<p>Unlike a static SaaS product, AI products carry recurring infrastructure costs that scale with usage and require ongoing maintenance:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Cloud compute for inference: $500\u2013$10,000+\/month depending on query volume<\/li><li>Model retraining and fine-tuning: $5,000\u2013$30,000 per quarter<\/li><li>Monitoring, observability, and drift detection: $500\u2013$2,000\/month<\/li><li>Human-in-the-loop review (where required): $2,000\u2013$15,000\/month<\/li><\/ul>\n\n\n\n<p>Companies that fail to budget for operational costs consistently underestimate 3-year AI total cost of ownership \u2014 often by half or more. This is one of the most reliable ways to destroy a compelling business case after approval.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-category-3-team-and-talent-costs\">Category 3: Team and Talent Costs<\/h3>\n\n\n\n<p>Internal resourcing is often invisible in AI ROI models because it is absorbed into existing headcount budgets. But it is real cost:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Product\/project management: 0.25\u20130.5 FTE during build, 0.1 FTE ongoing<\/li><li>Data engineering support: 0.25\u20131.0 FTE ongoing<\/li><li>ML\/AI engineering: 1\u20133 FTEs during build, 0.25\u20130.5 FTE for maintenance<\/li><\/ul>\n\n\n\n<p>Senior US-based AI engineers routinely carry total compensation well north of US$300,000 per year. For most companies, using a consulting partner is significantly more cost-effective for early AI product builds \u2014 covered in detail in our <a href=\"https:\/\/neomeric.com\/blog\/build-vs-buy-ai\/\">build vs buy AI decision guide<\/a>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-category-4-change-management-and-adoption-costs\">Category 4: Change Management and Adoption Costs<\/h3>\n\n\n\n<p>Adoption costs are real and frequently underestimated:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Training and onboarding for end users: $5,000\u2013$20,000<\/li><li>Process redesign and workflow integration: $10,000\u2013$40,000<\/li><li>Governance and compliance review: $5,000\u2013$25,000<\/li><\/ul>\n\n\n\n<p><strong>Total Cost of Ownership Formula:<\/strong> TCO = Development Cost + (Monthly OpEx \u00d7 36 months) + Team Cost + Change Management. For a typical mid-market AI product with a $120,000 build, total 3-year TCO realistically lands between $280,000 and $420,000.<\/p>\n\n\n\n<div class=\"nm-cta-box\"><h4 class=\"wp-block-heading\">Free: The Australian AI MVP Cost Guide 2026<\/h4><p>Honest cost benchmarks, the hidden costs vendors don&#8217;t quote, and a 10-line scoping worksheet \u2014 everything you need before requesting quotes.<\/p><a class=\"nm-cta-btn\" href=\"https:\/\/neomeric.com\/blog\/mvp-cost-guide\/\">Get the free guide<\/a><\/div>\n<h2 class=\"wp-block-heading\" id=\"s-how-do-you-measure-the-returns-from-an-ai-product\">How Do You Measure the Returns from an AI Product?<\/h2>\n\n\n\n<p>Returns from AI product development fall into three categories. Each requires a different measurement approach, and all three belong in your ROI model.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-1-cost-reduction-returns\">1. Cost Reduction Returns<\/h3>\n\n\n\n<p>Cost savings are the most straightforward ROI driver:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><strong>Labour cost eliminated:<\/strong> (hours saved per week \u00d7 52 \u00d7 fully loaded hourly rate)<\/li><li><strong>Error and rework reduction:<\/strong> (error rate reduction \u00d7 cost per error \u00d7 annual volume)<\/li><li><strong>Infrastructure consolidation:<\/strong> compare new vs. old ongoing run costs<\/li><\/ul>\n\n\n\n<p>Example: An AI document review system saving 20 hours\/week at a $75\/hour loaded cost delivers $78,000 per year in direct labour value \u2014 a clear, auditable return figure for any business case.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-2-revenue-generation-returns\">2. Revenue Generation Returns<\/h3>\n\n\n\n<p>Revenue impact is harder to attribute precisely but frequently represents the largest pool of AI returns. According to <a href=\"https:\/\/www.mckinsey.com\/capabilities\/growth-marketing-and-sales\/our-insights\/the-value-of-getting-personalization-right-or-wrong-is-multiplying\" rel=\"noopener\">McKinsey research on personalisation<\/a>, personalisation most often drives 10\u201315% revenue lifts for consumer-facing products. For a $10M ARR business, that represents $1\u2013$1.5M in incremental revenue \u2014 dwarfing the build cost of most AI personalisation features. Key return drivers include conversion rate improvements, new products enabled by AI, faster time-to-market, and customer retention gains.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-3-risk-reduction-returns\">3. Risk Reduction Returns<\/h3>\n\n\n\n<p>Risk reduction creates real financial value that belongs in your ROI model even if it never appears on the P&amp;L: fraud losses prevented, regulatory fines avoided, downtime hours eliminated through predictive maintenance, and breach costs reduced through AI security tooling. The most accurate way to measure all returns is to define 3\u20135 specific KPIs before you build and instrument baseline measurement before development begins.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"s-what-roi-can-you-realistically-expect-from-ai-product-development-in-2026\">What ROI Can You Realistically Expect from AI Product Development in 2026?<\/h2>\n\n\n\n<p>AI product ROI varies by use case, scope, and organisational readiness. Here are the benchmarks Neomeric sees across consulting engagements and published industry research:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>AI Use Case<\/th><th>Typical 12-Month ROI<\/th><th>Payback Period<\/th><\/tr><\/thead><tbody><tr><td>Process automation (document\/data)<\/td><td>80\u2013200%<\/td><td>6\u201312 months<\/td><\/tr><tr><td>Predictive analytics for operations<\/td><td>100\u2013300%<\/td><td>9\u201318 months<\/td><\/tr><tr><td>Customer-facing AI features<\/td><td>40\u2013150%<\/td><td>12\u201324 months<\/td><\/tr><tr><td>Internal AI assistant\/copilot<\/td><td>60\u2013150%<\/td><td>8\u201314 months<\/td><\/tr><tr><td>Fraud detection \/ risk scoring<\/td><td>200\u2013500%<\/td><td>6\u201312 months<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Key patterns across industry research: companies in the top quartile of AI maturity consistently achieve multiples of the ROI of laggards, and small and medium businesses implementing well-targeted AI typically report positive ROI within their first year. AI ROI is front-loaded by efficiency gains and back-loaded by strategic value. Teams that only measure the first 6 months will systematically undervalue AI product investment.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"s-what-are-the-biggest-mistakes-teams-make-when-calculating-ai-roi\">What Are the Biggest Mistakes Teams Make When Calculating AI ROI?<\/h2>\n\n\n\n<p>Most AI ROI calculations fail before the product is built. These are the five mistakes Neomeric encounters most often during discovery engagements with founders, CTOs, and product leaders.<\/p>\n\n\n\n<p><strong>Using cost savings as the only return metric.<\/strong> Teams that exclude revenue uplift, risk reduction, and strategic capability routinely undercount returns by 50\u201370%, making projects appear marginal when they are actually compelling.<\/p>\n\n\n\n<p><strong>Excluding ongoing operational costs.<\/strong> A $100,000 AI build costing $8,000\/month to run costs $388,000 over 3 years \u2014 nearly 4\u00d7 the build cost. Teams that budget only for the build are surprised when operational costs extend payback timelines by 12\u201318 months.<\/p>\n\n\n\n<p><strong>Assuming 100% adoption from day one.<\/strong> Real-world AI adoption follows an S-curve. Most enterprise AI products reach 30\u201340% active usage in month 1 and full adoption by month 6\u20139. See our <a href=\"https:\/\/neomeric.com\/blog\/ai-product-scaling-checklist\/\">AI product scaling checklist<\/a> for the readiness steps that accelerate adoption.<\/p>\n\n\n\n<p><strong>Not establishing a baseline before building.<\/strong> Without measuring the current state \u2014 process duration, error rate, cost per unit \u2014 there is no basis for calculating improvement. Establish baseline measurements before development begins, not after launch.<\/p>\n\n\n\n<p><strong>Ignoring data quality costs.<\/strong> Data preparation overruns are among the most common budget blowouts in AI projects. Poor data quality is an ongoing operational cost. Budget for continuous data governance from the outset.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"s-how-do-you-build-a-compelling-business-case-for-ai-investment\">How Do You Build a Compelling Business Case for AI Investment?<\/h2>\n\n\n\n<p>A convincing AI business case must answer five questions with specific numbers \u2014 not directional claims:<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li><strong>What is the current cost of the problem?<\/strong> (Quantified in dollars per year)<\/li><li><strong>What specific improvement will the AI product create?<\/strong> (Measurable, tied to pre-agreed KPIs)<\/li><li><strong>What is the total investment required?<\/strong> (Build + 3-year operational + team + change management)<\/li><li><strong>What is the projected return timeline?<\/strong> (Month-by-month, with conservative\/base\/optimistic scenarios)<\/li><li><strong>What is the competitive cost of inaction?<\/strong> (What happens if you don\u2019t build this?)<\/li><\/ol>\n\n\n\n<p>Here is a simple 3-year ROI model for a typical mid-market AI project with a $120,000 build and $7,500\/month operational cost:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Year<\/th><th>Investment<\/th><th>Returns<\/th><th>Net<\/th><th>Cumulative<\/th><\/tr><\/thead><tbody><tr><td>Year 1<\/td><td>$210,000<\/td><td>$120,000 (ramp-up)<\/td><td>\u2212$90,000<\/td><td>\u2212$90,000<\/td><\/tr><tr><td>Year 2<\/td><td>$90,000<\/td><td>$240,000 (full adoption)<\/td><td>+$150,000<\/td><td>+$60,000<\/td><\/tr><tr><td>Year 3<\/td><td>$90,000<\/td><td>$300,000 (compounding)<\/td><td>+$210,000<\/td><td>+$270,000<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>3-year ROI (conservative): ($270,000 \u00f7 $390,000) \u00d7 100 = 69%<\/strong>. With realistic revenue impact and risk reduction included, 3-year ROI for this profile typically reaches 150\u2013250% in Neomeric\u2019s experience. Always present three scenarios \u2014 conservative, base, and optimistic \u2014 so stakeholders see the full range. A business case showing only the optimistic scenario loses credibility the moment early assumptions don\u2019t hold.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"s-frequently-asked-questions-about-ai-product-development-roi\">Frequently Asked Questions About AI Product Development ROI<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-what-is-a-good-roi-for-ai-product-development\">What is a good ROI for AI product development?<\/h3>\n\n\n\n<p>A good ROI for AI product development is 100\u2013300% over a 3-year period, with payback within 12\u201324 months. Quick-win use cases like process automation and fraud detection often achieve payback within 6\u201312 months. The key is defining ROI to include cost savings, revenue impact, risk reduction, and strategic value \u2014 not cost savings alone.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-how-long-does-it-take-to-see-roi-from-an-ai-product\">How long does it take to see ROI from an AI product?<\/h3>\n\n\n\n<p>Most AI products begin generating measurable returns within 3\u20136 months of launch, though full cost recovery typically takes 12\u201324 months. Early returns come from automation and efficiency gains; revenue and strategic returns compound over time. Projects with a narrow, clearly defined scope and pre-agreed baseline metrics reach payback fastest.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-what-is-the-average-cost-to-build-an-ai-product\">What is the average cost to build an AI product?<\/h3>\n\n\n\n<p>The average cost to build an AI product MVP ranges from $50,000 to $300,000, with most mid-market projects landing between $80,000 and $150,000 for a focused 12-week build. Total 3-year ownership cost is typically 2\u20134\u00d7 the build cost once operational infrastructure, model maintenance, and team time are included.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-how-do-you-justify-ai-investment-to-a-board-or-cfo\">How do you justify AI investment to a board or CFO?<\/h3>\n\n\n\n<p>Justify AI investment to a board or CFO by quantifying the current-state cost of the problem, projecting conservative\/base\/optimistic return scenarios across 3 years, including all cost categories, and showing the competitive cost of inaction. Tie projections to specific, pre-agreed KPIs that can be tracked from day one.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-what-is-the-roi-of-ai-for-small-businesses\">What is the ROI of AI for small businesses?<\/h3>\n\n\n\n<p>Small businesses that implement well-targeted AI typically report reaching positive ROI within 4\u20138 months, and small business surveys consistently link AI adoption to improved competitiveness and profitability. The most accessible entry points \u2014 AI customer service, document automation, and predictive inventory \u2014 typically cost $10,000\u2013$50,000 and deliver measurable returns within 6 months.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"s-should-i-use-a-consultant-or-build-an-in-house-ai-team-to-maximise-roi\">Should I use a consultant or build an in-house AI team to maximise ROI?<\/h3>\n\n\n\n<p>For most companies investing in their first or second AI product, a consulting partner delivers significantly better early ROI than building an in-house team. Senior AI engineers can cost upwards of US$300,000 per year in total compensation, with hiring timelines that stretch to six months. A consulting engagement can deliver an AI MVP in 10\u201312 weeks at lower total cost with no hiring risk. In-house teams become more cost-effective at the third or fourth product, once domain-specific AI expertise is established.<\/p>\n\n\n\n<script type=\"application\/ld+json\">{\"@context\": \"https:\/\/schema.org\", \"@type\": \"FAQPage\", \"mainEntity\": [{\"@type\": \"Question\", \"name\": \"What is a good ROI for AI product development?\", \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"A good ROI for AI product development is 100\u2013300% over a 3-year period, with payback within 12\u201324 months. Quick-win use cases like process automation and fraud detection often achieve payback within 6\u201312 months. The key is defining ROI to include cost savings, revenue impact, risk reduction, and strategic value \u2014 not cost savings alone.\"}}, {\"@type\": \"Question\", \"name\": \"How long does it take to see ROI from an AI product?\", \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"Most AI products begin generating measurable returns within 3\u20136 months of launch, though full cost recovery typically takes 12\u201324 months. Early returns come from automation and efficiency gains; revenue and strategic returns compound over time. Projects with a narrow, clearly defined scope and pre-agreed baseline metrics reach payback fastest.\"}}, {\"@type\": \"Question\", \"name\": \"What is the average cost to build an AI product?\", \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"The average cost to build an AI product MVP ranges from $50,000 to $300,000, with most mid-market projects landing between $80,000 and $150,000 for a focused 12-week build. Total 3-year ownership cost is typically 2\u20134\u00d7 the build cost once operational infrastructure, model maintenance, and team time are included.\"}}, {\"@type\": \"Question\", \"name\": \"How do you justify AI investment to a board or CFO?\", \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"Justify AI investment to a board or CFO by quantifying the current-state cost of the problem, projecting conservative\/base\/optimistic return scenarios across 3 years, including all cost categories, and showing the competitive cost of inaction. Tie projections to specific, pre-agreed KPIs that can be tracked from day one.\"}}, {\"@type\": \"Question\", \"name\": \"What is the ROI of AI for small businesses?\", \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"Small businesses that implement well-targeted AI typically report reaching positive ROI within 4\u20138 months, and small business surveys consistently link AI adoption to improved competitiveness and profitability. The most accessible entry points \u2014 AI customer service, document automation, and predictive inventory \u2014 typically cost $10,000\u2013$50,000 and deliver measurable returns within 6 months.\"}}, {\"@type\": \"Question\", \"name\": \"Should I use a consultant or build an in-house AI team to maximise ROI?\", \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"For most companies investing in their first or second AI product, a consulting partner delivers significantly better early ROI than building an in-house team. Senior AI engineers can cost upwards of US$300,000 per year in total compensation, with hiring timelines that stretch to six months. A consulting engagement can deliver an AI MVP in 10\u201312 weeks at lower total cost with no hiring risk. In-house teams become more cost-effective at the third or fourth product, once domain-specific AI expertise is established. Ready to calculate ROI for your specific AI product idea? Neomeric works with founders, CTOs, and product leaders at mid-market and enterprise companies across Australia and globally to build realistic AI business cases, scope focused MVPs, and deliver measurable returns. If you are evaluating an AI investment, talk to the Neomeric team \u2014 we will help you build the numbers before you commit.\"}}]}<\/script>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><strong>Ready to calculate ROI for your specific AI product idea?<\/strong> Neomeric works with founders, CTOs, and product leaders at mid-market and enterprise companies across Australia and globally to build realistic AI business cases, scope focused MVPs, and deliver measurable returns. If you are evaluating an AI investment, <a href=\"https:\/\/neomeric.com\/contact\">talk to the Neomeric team<\/a> \u2014 we will help you build the numbers before you commit.<\/p>\n\n<h2 id=\"s-sources\">Sources<\/h2><ul class=\"nm-sources\"><li><a href=\"https:\/\/www.mckinsey.com\/capabilities\/growth-marketing-and-sales\/our-insights\/the-value-of-getting-personalization-right-or-wrong-is-multiplying\" rel=\"noopener\">McKinsey \u2014 The value of getting personalization right or wrong is multiplying<\/a><\/li><li><a href=\"https:\/\/www.mckinsey.com\/capabilities\/quantumblack\/our-insights\/the-state-of-ai\" rel=\"noopener\">McKinsey \u2014 The State of AI: Agents, innovation, and transformation<\/a><\/li><li><a href=\"https:\/\/www.rand.org\/pubs\/research_reports\/RRA2680-1.html\" rel=\"noopener\">RAND \u2014 Why AI Projects Fail and How They Can Succeed<\/a><\/li><li><a href=\"https:\/\/sbecouncil.org\/2025\/10\/23\/new-sbe-council-survey-small-businesses-confident-about-2025-year-end-performance-ai-digital-tools-and-multi-channel-strategies-driving-growth-and-competitiveness\/\" rel=\"noopener\">SBE Council \u2014 2025 Small Business Check Up Survey<\/a><\/li><\/ul>\n<div class=\"nm-cta-box\"><h4>Building something? Get a straight answer on cost.<\/h4><p>Neomeric is a Melbourne AI product studio \u2014 7+ products shipped, including our own. Start with a free 15-minute scoping call, or a 2-week Build Sprint at A$6,900 fixed, fully credited toward your pilot.<\/p><a class=\"nm-cta-btn\" href=\"https:\/\/calendly.com\/haseeb-neomeric\/meeting?utm_source=blog&amp;utm_medium=cta&amp;utm_campaign=insights\">Book a free scoping call<\/a><a class=\"nm-cta-btn ghost\" href=\"https:\/\/neomeric.com\/blog\/mvp-cost-guide\/\">Download the cost guide<\/a><\/div>\n<div class=\"nm-disclaimer\"><strong>Disclaimer:<\/strong> This article is general information only, current at the time of writing, and is not legal, financial or professional advice. Regulatory obligations, pricing and market figures change and vary by circumstance &mdash; seek advice specific to your situation before acting. 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Learn how to calculate costs, returns, and break-even timelines before you invest. Complete framework from Neomeric.<\/p>\n","protected":false},"author":3,"featured_media":322,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[25,18,14],"class_list":["post-127","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-insights","tag-ai-development","tag-ai-strategy","tag-enterprise-ai"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How to Calculate ROI on AI Product Development | Neomeric<\/title>\n<meta name=\"description\" content=\"AI product development ROI averages 150\u2013300% over 3 years. Learn how to calculate costs, returns, and break-even timelines before you invest. 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