Building the business case: costs, timelines and realistic ROI

# Building the business case: costs, timelines and realistic ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète →

A vendor slide promises "50% reduction in molecule triage time." Eighteen months later, the same lab is still running parallel manual review because nobody budgeted for the retraining, the validation cycle, or the chemist hours needed to babysit the model. This is the single most common failure pattern in pharma AI deployments: real capability, unrealistic business case.

This lesson uses a molecule triage AI (a system that screens candidate compounds computationally before committing them to wet-lab synthesis and testing) as a worked example to build a defensible total cost of ownership (TCOTCOTotal Cost of Ownership, coût total de possession incluant acquisition, implémentation, maintenance, formation et évolution d'un outil sur sa durée de vie.) model against a realistic timeline.

Why vendor ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → numbers mislead

Vendor benchmarks typically measure one thing: model inference speed or accuracy on a held-out dataset. They rarely measure:

• Integration cost: connecting the model to your electronic lab notebook (ELN), compound registration system, and existing computational chemistry pipelines.
• Validation burden: pharma is a regulated industry; any tool influencing a decision that ends up in a regulatory submission may need documented validation under frameworks like GxP (Good Practice quality guidelines covering manufacturing, laboratory, and clinical work) or, for AI specifically, guidance emerging from the FDA's discussion paper on AI/ML in drug development (US) and the EMA's reflection paper on AI in the medicinal product lifecycle (Europe).
• Change management: chemists and biologists trusting (or not) a model's ranking enough to deprioritize compounds they'd have tested by intuition.
• Ongoing model maintenance: retraining as new assay data arrives, monitoring for drift.

None of this appears in a demo. All of it appears in year one of the budget.

Building the TCOTCOTotal Cost of Ownership, coût total de possession incluant acquisition, implémentation, maintenance, formation et évolution d'un outil sur sa durée de vie. model: five cost buckets

For a molecule triage AI, structure costs into five buckets. Figures below are illustrative estimates for a mid-size biotech or pharma R&D unit deploying a licensed platform (not building foundation models in-house), based on commonly cited industry ranges as of early 2026. Treat them as planning anchors, not quotes.

| Bucket | What it covers | Rough range (year 1, estimate) |

|---|---|---|

| Licensing / platform | Software license or APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → usage fees | $150K to $1.5M/year depending on scale and vendor (Schrödinger, BenevolentAI, Insilico Medicine, or in-house on AWS/Azure ML infra) |

| Data preparation | Cleaning, structuring, and rights-clearing proprietary assay and compound data | $100K to $400K, often underestimated by 2 to 3x |

| Integration & IT | Connecting to ELN, LIMS (Laboratory Information Management System), compute infrastructure | $50K to $250K |

| Validation & compliance | Documentation, cross-validation against historical decisions, QA sign-off | $75K to $300K, recurring at lower cost annually |

| Change management & training | Chemist/biologist training, workflow redesign, pilot oversight | $50K to $150K |

A realistic first-year TCOTCOTotal Cost of Ownership, coût total de possession incluant acquisition, implémentation, maintenance, formation et évolution d'un outil sur sa durée de vie. for a mid-size deployment often lands in the $500K to $2.5M range, before any productivity gain is realized. This is the number missing from the vendor slide.

Worked calculation: is the case real?

Suppose the AI triage tool claims it can cut the number of compounds sent to wet-lab synthesis by 30%, from 1,000 candidates/year to 700, by filtering out low-probability structures earlier.

Step 1: Baseline synthesis cost.

Assume synthesis and initial assay of one compound costs an estimated $15,000 (a commonly cited planning figure for early discovery; actual costs vary widely by modality and vary by 3 to 5x between simple small molecules and complex biologics).

• Baseline: 1,000 compounds × $15,000 = $15,000,000/year

Step 2: Post-AI cost.

• 700 compounds × $15,000 = $10,500,000/year
• Gross saving: $4,500,000/year

Step 3: Net against TCO.

• Year 1 TCOTCOTotal Cost of Ownership, coût total de possession incluant acquisition, implémentation, maintenance, formation et évolution d'un outil sur sa durée de vie. (license + integration + validation + training): assume $1,200,000
• Net year 1 benefit: $4,500,000 − $1,200,000 = $3,300,000

On paper, compelling. But two adjustments matter:

1. Ramp-up discount. Realistically, the model doesn't hit 30% filtering accuracy on day one. Most deployments see 40 to 60% of claimed performance in year one while chemists validate and trust builds. Apply a 0.5 realization factor to year one: gross saving becomes $2,250,000, net becomes $1,050,000.

2. False negative risk. If the model filters out a compound that would have been a hit, that's an invisible cost, a missed drug candidate, not a line item, but the single largest real risk in triage AI. This is why most serious deployments run a "shadow mode" period (AI recommendations logged but not acted on) for 3 to 6 months before the model touches real go/no-go decisions.

Realistic timelines, not vendor timelines

A defensible deployment timeline for molecule triage AI looks closer to this:

• Months 1 to 3: Data audit and cleaning, vendor selection, contract and data-rights negotiation.
• Months 3 to 6: Integration with ELN/LIMS, initial model tuning on internal data.
• Months 6 to 9: Shadow mode, running in parallel with existing manual triage, no decisions changed yet.
• Months 9 to 12: Limited live pilot on a subset of programs, with chemist override rights logged and reviewed.
• Months 12 to 18: Broader rollout if pilot metrics hold, plus first validation refresh cycle.

Vendors often present a 3 to 6 month "go-live." That's technically true for switching the software on. It is not the same as the organization deriving trustworthy, decision-grade output. Budget and stakeholder expectations should be set against the 12 to 18 month curve, not the go-live date.

A minimal way to track realized vs. promised gains

Even a simple spreadsheet-level tracker, updated monthly, disciplines the conversation:

month, compounds_screened, ai_flagged_low_priority,
chemist_overrides, compounds_synthesized,
cumulative_cost, cumulative_savings_estimate

The chemist_overrides column matters most. A high override rate late into the pilot signals a trust or accuracy problem, not just a change-management lag. Low override rate too early can signal the opposite problem: over-trust before validation is complete.

Where the real ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → usually shows up

For molecule triage specifically, the honest ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → story is less about headcount reduction and more about:

• Cycle time compression: getting from candidate list to synthesis decision in days rather than weeks, which matters more for pipelinepipeline velocity than raw cost.