AI investments need to be justified — to boards, finance committees, and skeptical business units. RLM builds defensible ROI models for enterprise AI initiatives that quantify expected value, model the cost structure, and establish the measurement framework that proves the investment paid off.
Most AI ROI estimates are either too optimistic to be credible or too vague to be actionable. Either outcome undermines the investment decision before it's made.
Vendor-provided ROI calculators consistently overestimate benefits by assuming best-case adoption rates, ignoring change management costs, and using theoretical productivity gains that rarely materialize at modeled levels.
Infrastructure, integration, fine-tuning, ongoing model maintenance, security controls, governance overhead, and employee training are routinely omitted from AI cost models — making the true TCO 2-4x what was budgeted.
Without pre-defined metrics and baseline measurements, there's no way to demonstrate that promised ROI was actually achieved — leaving AI investments perpetually "under review" for the next budget cycle.
We build models that finance teams find credible because they're built on documented assumptions, not vendor talking points — and because we model conservatively.
Labor hour reduction, handle time improvement, error rate reduction, revenue uplift, cost avoidance — each benefit line is tied to a specific data point, a realistic adoption assumption, and a confidence level. We present low, base, and high scenarios.
Platform licensing, infrastructure (compute, storage, network), integration development, fine-tuning and model operations, security controls, governance overhead, training, and ongoing vendor management — all modeled over a 3-year horizon.
Time-to-value curves based on realistic deployment timelines, adoption ramp assumptions, and benefit realization schedules. IRR and NPV calculated at your cost of capital for capital committee presentation.
KPIs, baseline measurements, data collection methods, and review cadences defined before deployment — so you can demonstrate actual ROI at 90 days, 6 months, and 12 months post-launch.
"RLM brought structure to a process we didn't know how to start. They asked the right questions, surfaced the right vendors, and kept us from making decisions we would have regretted."
"What set RLM apart was that they didn't have a preferred answer. They evaluated our options honestly and told us what they actually thought — even when that meant recommending a smaller vendor."
RLM's AI advisors help enterprises move from uncertainty to a clear, actionable strategy — with no vendor agenda and no technology stack to sell.
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