Enterprise GenAI ROI: Case Studies from 2025-2026 Deployments

GenAI investment accelerated sharply through 2025. Enterprise spending on generative AI consulting and implementation crossed $14B globally, and nearly every organization in the Fortune 1000 has at least one production deployment or active pilot. The problem is not adoption — it is accountability. When CFOs and board sponsors ask "what did we get for that $500K?", the answer is frequently vague. Productivity improvements are described anecdotally. Cost savings are modeled but not tracked to a line item. Competitive advantage is cited as a rationale that cannot be disproven or proven.

This is not a technology failure. It is a measurement and engagement-structure failure.

This article draws on DCF Research's ongoing evaluation of GenAI consulting engagements, three anonymized case studies from 2025-2026 deployments, and benchmark data aggregated from over 50 verified consulting firm assessments. The goal is to give CFOs, CIOs, and business sponsors of AI initiatives a clear-eyed view of what returns are actually achievable, what causes engagements to underdeliver, and how to structure a new engagement to maximize the probability of measurable ROI.

For a broader look at the AI projects landscape and firm specializations, see our AI projects directory. For context on what these engagements cost, see our companion piece on AI consulting pricing.

1. How to Measure GenAI ROI

DCF Research recommends four measurement frameworks for enterprise GenAI ROI: productivity metrics (time saved per task, at loaded employee cost), cost per transaction (before and after automation), revenue attribution (incremental revenue tied to AI-assisted workflows), and time-to-outcome (how quickly results materialize after go-live). Most engagements only track one of these. The strongest ROI stories track all four.

ROI measurement is where most GenAI engagements start failing — before a single line of code is written. The scope-of-work document describes deliverables (a RAG system, an MLOps platform, a fine-tuned model). What it rarely describes is how success will be measured six months after deployment.

According to DCF Research's 2026 analysis of over 50 GenAI consulting engagements, fewer than a third established a formal measurement framework before the engagement began. The consequences are predictable: post-project reviews become a negotiation over what the numbers mean, rather than a straightforward comparison to agreed targets.

The four frameworks that matter:

1. Productivity Metrics. The most common measurement approach. Track hours per task before and after deployment, multiply by loaded employee cost (salary plus benefits plus overhead, typically 1.25-1.4x base salary), and compare to total engagement cost. The weakness is that productivity gains are often distributed across many employees in small increments that do not translate to headcount reduction or redeployment — making the business case look good on paper but delivering limited bottom-line impact.

2. Cost Per Transaction. More rigorous. Define a discrete unit of work — a loan application processed, a support ticket resolved, a contract reviewed — and calculate the fully loaded cost before and after the AI deployment. This approach requires clean baseline data, which itself is often a project, but produces numbers that hold up to CFO scrutiny.

3. Revenue Attribution. The highest-upside but hardest-to-isolate framework. Applicable when AI is embedded in a customer-facing or sales-assist workflow. Requires controlled comparison (AI-assisted vs. non-AI-assisted cohorts) or a test-and-control rollout, both of which demand more design rigor than most engagements build in upfront.

4. Time-to-Outcome. Underused and underrated. How quickly does the organization realize value after go-live? A system that delivers a 40% efficiency gain starting in month two is meaningfully different from one that delivers the same gain starting in month fourteen. Time-to-outcome captures payback period and is particularly relevant for board-level reporting.

Buyer's Note: If a consulting firm's proposal does not include a measurement plan with defined baselines, targets, and a named owner of data collection, treat that as a red flag. The ROI conversation should start during scoping, not after delivery.

2. Case Study: Enterprise RAG Deployment

A Fortune 500 financial services firm engaged a boutique AI consultancy for $380K to build a retrieval-augmented generation system over internal research documents and regulatory filings. The result: a 60% reduction in analyst research time per report, with an 8-month ROI payback period. The critical factor was not the technology — it was the 6-week data preparation phase that preceded any model work.

Engagement overview:

• Client: Fortune 500 financial services firm (investment research division)
• Consulting firm: Boutique AI consultancy, 40 employees, Series A backed
• Engagement size: $380K fixed-fee, 5-month duration
• Use case: RAG system over 4 years of internal research documents, SEC filings, and earnings call transcripts

The problem before engagement: Equity analysts spent an estimated 12-15 hours per research report gathering context from internal archives. Document search was keyword-based and missed semantic relationships. A senior analyst at $180K base salary (approximately $252K fully loaded) spending 30% of time on document retrieval represented roughly $75K in annual productivity cost per person across a team of 12. Total addressable cost: approximately $900K per year.

What the consulting firm did: The first six weeks were dedicated entirely to data preparation — deduplication, chunking strategy, metadata tagging, and PDF extraction quality testing. This was billed as part of the fixed fee and represented a significant share of early effort. The firm deployed a RAG architecture using Azure OpenAI Service with a custom retrieval layer indexed against the firm's existing SharePoint and a legacy document repository. They ran a 4-week pilot with 3 analysts, collected structured feedback, and tuned retrieval parameters before full rollout. Post-deployment, they delivered a 3-month hypercare period with weekly optimization sessions.

Measured results (at 6-month post-go-live review):

• Average research preparation time per report: reduced from 13.5 hours to 5.4 hours (60% reduction)
• Annual productivity value recovered: approximately $540K across the analyst team
• Total engagement cost including infrastructure: $412K (engagement fee plus first-year Azure costs)
• ROI payback period: approximately 8 months
• Analyst satisfaction score: 4.2/5.0 (measured via quarterly internal survey)

What made it work: The fixed-fee structure forced the consulting firm to be efficient. The 6-week data preparation phase — which clients often resist funding because it produces no visible AI output — was non-negotiable. And the pilot cohort approach meant the system was tuned against real usage before it touched the full analyst team.

What did not go smoothly: the legacy document repository required a custom connector that consumed two additional weeks of engineering time. The firm absorbed this within the fixed fee, which was only possible because they had scoped a 15% contingency buffer into their estimate.

3. Case Study: MLOps Platform Transformation

A mid-market specialty retailer (approximately $2.1B revenue) invested $520K in an MLOps platform build that reduced model deployment time from 8 weeks to 3 days — a 4x increase in model deployment frequency. The productivity gain was not the primary ROI driver; faster experimentation cycles translated to a measurable lift in demand forecasting accuracy that reduced inventory write-downs by approximately $3.2M annually.

Engagement overview:

• Client: Specialty retailer, ~$2.1B annual revenue, 340 store locations
• Consulting firm: Data and AI consultancy, 200+ employees, Databricks Premier Partner
• Engagement size: $520K, 7-month build with 3-month embedded support
• Use case: MLOps platform to standardize model training, validation, CI/CD deployment, and monitoring across the ML team

The problem before engagement: The retailer's ML team of 8 data scientists was spending the majority of their time on manual deployment coordination rather than model development. Each new model release required 6-8 weeks of manual QA, environment parity checks, and stakeholder signoff on a process that was inconsistently documented. The team shipped approximately 6-8 model updates per year. The consequence was that demand forecasting models were perpetually stale — in some categories, operating on models last retrained 9 months prior.

What the consulting firm did: The engagement was structured in three phases. Phase 1 (6 weeks): audit of existing deployment process, gap analysis, and architecture design on Databricks with MLflow for experiment tracking and Unity Catalog for model governance. Phase 2 (14 weeks): platform build, CI/CD pipeline implementation using GitHub Actions, automated model validation test suite, and integration with the existing Snowflake data warehouse. Phase 3 (10 weeks): migration of the 4 highest-priority models to the new platform, documentation, and knowledge transfer to internal ML engineers.

Measured results (at 9-month post-go-live review):

• Model deployment cycle time: reduced from 8 weeks to 3 days
• Deployment frequency: increased from approximately 7 per year to 28+ per year (4x)
• Demand forecasting MAPE improvement: 18% reduction in mean absolute percentage error across the top 3 demand models
• Inventory write-down reduction attributed to improved forecasting accuracy: approximately $3.2M annually
• Total engagement cost: $520K
• ROI payback period: approximately 7 weeks (based on inventory reduction alone)

What made it work: The ROI here came from an indirect benefit — better forecasting — not from the platform itself. The consulting firm insisted on defining a "value realization" metric in the SOW that was tied to business outcomes, not just platform delivery milestones. This forced the internal team to provide access to write-down data and work with the consultants on attribution methodology.

What was harder than expected: The Unity Catalog governance implementation surfaced a permissions and data ownership dispute between the ML team and the central data platform team that had been simmering for two years. Resolving it consumed approximately 3 weeks of stakeholder management time. The consulting firm facilitated but could not force internal alignment — that required executive escalation.

4. Case Study: AI Strategy to Production

A regional healthcare system started with a $200K AI strategy engagement and progressed to a $1.2M implementation over 18 months. The deployed system reduced prior authorization processing time by 40%, from an average of 4.2 days to 2.5 days — measurably improving patient access metrics and reducing administrative labor costs by approximately $1.8M annually.

Engagement overview:

• Client: Regional healthcare system, 6 hospitals, approximately 22,000 employees
• Consulting firm: Healthcare-specialized AI consultancy with HIPAA compliance practice
• Engagement size: $200K strategy phase (4 months), $1.2M implementation phase (14 months)
• Use case: Intelligent prior authorization routing and automated documentation extraction

The problem before engagement: Prior authorization (PA) is one of the most administratively expensive processes in US healthcare. This system was processing approximately 18,000 PA requests per month. Average processing time was 4.2 business days. A meaningful percentage of denials were later overturned on appeal — indicating that initial determinations were often made on incomplete documentation rather than clinical grounds. The administrative team handling PAs was 34 FTEs, with annual fully loaded cost of approximately $4.9M.

What the consulting firm did: The strategy phase produced a prioritized use case roadmap and a detailed data architecture assessment. The firm identified that 62% of PA delays were caused by incomplete or incorrectly formatted supporting documentation submitted by providers — a problem addressable with a document extraction and completeness-checking layer before human review. The implementation phase built a multi-stage pipeline: an OCR and structured data extraction layer for incoming provider documentation, an LLM-based completeness checker that flagged missing information and auto-generated request letters to providers, and a routing engine that escalated genuinely complex cases while auto-processing straightforward approvals within defined clinical criteria. Crucially, the system was designed to augment the PA team rather than replace it — a deliberate choice driven by regulatory considerations and union contract constraints.

Measured results (at 12-month post-production review):

• Average PA processing time: reduced from 4.2 days to 2.5 days (40% reduction)
• Auto-processed (no human review required) PA volume: 34% of total submissions
• Administrative team size: held at 34 FTEs (no reduction), but capacity absorbed a 28% volume increase without adding headcount
• Annual administrative cost avoidance (avoided hiring for volume growth): approximately $1.8M
• Appeal overturn rate: reduced by 22%, indicating better initial decision quality
• Total engagement cost (strategy + implementation): $1.4M
• ROI payback period: approximately 9 months based on cost avoidance

The strategy-to-implementation progression: This is one of the cleaner examples of a strategy engagement that logically led to implementation work with the same firm. The strategy phase built organizational trust and produced a concrete roadmap that made the implementation scope defensible. The risk, which the client acknowledged, is that a single-firm relationship from strategy through implementation reduces the client's negotiating leverage on implementation pricing. Requesting a competitive bid on implementation even after a positive strategy engagement is reasonable practice.

For guidance on structuring phased engagements, see AI strategy vs. implementation consulting. For how proof-of-concept engagements typically transition to production, see GenAI consulting proof of concept.

5. Why Most GenAI Consulting Engagements Underdeliver on ROI

According to DCF Research's findings from evaluating 50+ GenAI consulting engagements, five root causes account for the majority of underdeliveries: success metrics were never defined, data quality issues were treated as out-of-scope, there was no production deployment plan embedded in the engagement, end-user adoption was not resourced, and scope creep consumed the contingency budget. Each is preventable.

The three case studies above represent outcomes in the top quartile of GenAI engagements. They are not representative of the median. According to DCF Research's 2026 analysis, approximately 60% of enterprise GenAI consulting engagements deliver materially below their projected ROI at the 12-month post-deployment mark.

Here are the five most common causes:

1. Unclear or absent success metrics. The engagement SOW describes deliverables — a deployed model, a platform, a prototype — but not how success will be measured. Without a baseline and a target, every post-project conversation becomes a negotiation. The fix is a measurement plan, agreed and signed before kickoff, that names the metric, defines the baseline, and sets a time-bounded target.

2. Data quality treated as out-of-scope. Every GenAI system is only as good as the data it retrieves or trains on. Consulting firms regularly underestimate data preparation effort during scoping because clients resist paying for it — it feels like they are being charged to fix their own mess. The result is that data cleanup happens during the engagement, consumes the contingency buffer, and leaves insufficient time for proper tuning and testing. Budget explicitly for data preparation. Expect it to represent 20-30% of total engagement effort on a first deployment.

3. No production deployment plan. A remarkable number of GenAI consulting engagements end with a working prototype in a sandbox environment and a handoff document. The path from prototype to production — security review, compliance sign-off, infrastructure provisioning, monitoring setup, user training — is a separate project that nobody scoped. The fix is to require a production deployment plan in the SOW before signing. If the consulting firm cannot describe the path to production, the engagement will end with a demo.

4. Staff adoption failure. Technology that employees do not use delivers zero ROI regardless of how well it works technically. GenAI tools often require new workflows, habit changes, and trust-building with skeptical end users. Adoption planning — including change management, training, and a named internal champion — is chronically underfunded in consulting engagements because it looks soft. It is not. The RAG case study above succeeded partly because the client assigned a senior analyst as the internal adoption lead who ran training sessions and collected feedback weekly during the pilot.

5. Scope creep consuming the contingency. Consulting engagements in complex enterprise environments routinely encounter unexpected integration requirements, permission issues, or stakeholder dependencies. These are normal. The problem is when the contingency buffer that should absorb them is instead consumed by scope additions that clients request mid-engagement — features that were not in the original SOW but seem cheap to add. Protecting the contingency buffer requires discipline from the client, not just the vendor.

6. ROI Benchmarks by GenAI Use Case

Across 50+ evaluated engagements, DCF Research has identified six GenAI use cases with consistent ROI patterns. Document processing and code generation show the fastest payback periods (typically 6-10 months) and the highest measurement reliability. Customer service automation shows the widest variance, driven by adoption rates. Supply chain optimization shows the highest potential ROI but requires the longest data maturity runway.

The table below represents benchmarks derived from our engagement evaluations. These are not projections — they reflect observed outcomes from completed deployments. Ranges are wide because results vary significantly based on data quality, adoption rates, and engagement structure.

Notes on interpreting this table:

The high end of each ROI range requires favorable conditions: clean baseline data, high user adoption, and a measurement framework established upfront. Engagements that lack any of these factors should plan on outcomes closer to the low end.

Customer service automation shows the widest variance because ROI is highly sensitive to containment rates — what percentage of interactions the AI resolves without human escalation. A containment rate of 35% and a containment rate of 70% in otherwise identical deployments produce dramatically different financials.

Supply chain optimization has the highest ceiling because the underlying cost pools (inventory, logistics, waste) are large. But the data requirements are also the most demanding. Organizations that do not have clean, historized supply chain data at sufficient granularity should expect to spend $200K-$400K on data foundation work before a forecasting model can deliver meaningful accuracy improvements.

7. How to Structure a GenAI Engagement for Maximum ROI

Five structural elements separate high-ROI GenAI engagements from the majority: success metrics defined and baselined before kickoff, phased delivery with go/no-go gates, a named internal champion with dedicated time allocation, a documented knowledge transfer plan that is executable by the internal team, and a post-go-live support SLA with defined response times. These are contractual requirements, not preferences.

Most of the factors that drive GenAI ROI are not technical. They are structural. Here is what to require before signing:

1. Defined success metrics with baselines. Before the engagement starts, agree on 2-3 primary metrics, capture the current baseline, and set a time-bounded target. Document this in the SOW. It should not be buried in an appendix — it should be on page 1.

2. Phased delivery with go/no-go gates. Break the engagement into phases of no more than 6-8 weeks each. Define what "done" means for each phase and establish explicit criteria for proceeding to the next phase. This creates natural checkpoints to assess whether the technical approach is working before committing the full budget.

3. Named internal champion with dedicated time. The single most common failure mode that cannot be fixed by the consulting firm is an internal champion who is nominally assigned but has no time allocated. A successful GenAI deployment requires 20-30% of a senior internal person's time during the engagement. This needs to be committed before the SOW is signed, not assumed.

4. Documented knowledge transfer plan. Require a KT plan that describes how the internal team will own and operate the system after the consulting engagement ends. The KT plan should be evaluated for executability — can the internal team realistically maintain what the consultants built, or does it require skills that do not exist internally? If the gap is too large, the KT plan should include a training component or a managed services arrangement.

5. Post-go-live support SLA. The 60 days after production deployment are the highest-risk period. User adoption is fragile, edge cases surface that were not caught in testing, and the internal team is not yet confident in their operational ownership. Require a defined support SLA from the consulting firm — at minimum, named response times for production issues and scheduled optimization check-ins for the first 90 days.

8. Evaluating Consulting Proposals for ROI Commitment

The most informative question you can ask a consulting firm during the proposal phase is: "Show me the measurement plan from a comparable engagement and what the client measured at 12 months." A firm that has delivered ROI will have this. A firm that has not will pivot to case study summaries that describe deliverables without outcomes.

When evaluating competing proposals for a GenAI engagement, look for these specific signals:

Proposals with high ROI probability:

• Include a measurement plan with named baselines and targets
• Describe a pilot or proof-of-concept phase before full deployment
• Identify data preparation as a discrete work stream with its own budget line
• Name a specific engagement model for post-go-live support
• Reference specific, verifiable client outcomes (even if anonymized)

Proposals with low ROI probability:

• Lead with technology stack and tool certifications rather than outcome methodology
• Define success as delivery of a working system, not business impact
• Do not mention adoption or change management
• Assume existing data is ready for AI without an assessment phase
• Describe an ROI that is implausibly high relative to engagement cost and timeline

Pricing for GenAI consulting engagements varies significantly by firm type and specialization. Our AI consulting pricing guide covers what to expect across boutique AI firms, national consultancies, and the major Big 4 practices. For a curated list of verified firms by specialization, see the AI projects directory.

Conclusion

The data from 2025-2026 GenAI deployments is clear: measurable ROI is achievable, but it is not the default. The engagements that deliver — the RAG deployment with 8-month payback, the MLOps platform that unlocked $3.2M in inventory savings, the PA automation that absorbed 28% volume growth without adding headcount — share structural characteristics that have nothing to do with which LLM was used or which cloud provider hosted the infrastructure.

They defined success before the engagement started. They addressed data quality as a first-class work stream. They built a path to production, not just a prototype. They invested in adoption. And they held the consulting firm accountable to a post-go-live period that caught problems before they became permanent.

For CFOs reviewing a GenAI consulting proposal: the proposal document tells you what will be built. The measurement plan — or its absence — tells you whether the firm expects to be accountable for what it delivers. That is the document worth scrutinizing.