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Pharmaceuticals, biotech, medical devices, life sciences services

AI consulting for
Pharma & Life Sciences.

Engagements built for highly regulated workflows where audit defensibility is non-optional. Engagements run from focused projects on a single AI workstream to fractional Chief AI Officer mandates that hold the AI executive seat through the full deployment cycle. Priced at $1,000/hour with a 100-hour minimum and a $100,000 project floor.

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Pharma & Life Sciences · Worldwide engagements · Prague-based · Global travel

Why this sector now

The current moment in pharma and life sciences.

Pharma is the sector where AI agents face the highest regulatory scrutiny and offer the highest ROI per process. Document review, regulatory submissions, clinical trial operations, and post-market surveillance all benefit from agent-assisted workflows — but only with proof standards that meet FDA, EMA, and PMDA scrutiny.

Use cases

Where AI is producing real results in life sciences.

01

Regulatory submission support

AI agents that draft sections of NDA/BLA/MAA submissions, cross-check against FDA and EMA guidance, and flag inconsistencies between the dossier and the underlying study reports. Human regulatory affairs leads validate; AI handles first-pass drafting and consistency checking.

02

Clinical trial operations

Protocol amendment analysis, site monitoring report synthesis, adverse event triage, and patient recruitment optimization. The compounding effect across multi-site, multi-year trials is significant.

03

Pharmacovigilance and post-market surveillance

AI agents that monitor adverse event databases, social media signals, and HCP communications to surface emerging safety patterns faster than human review can.

04

Medical writing and SOP authoring

Agent-drafted SOPs and clinical study reports that are reviewed by senior medical writers. The productivity multiplier is substantial; the regulatory standards stay unchanged.

05

Commercial intelligence and HCP engagement

AI agents that synthesize KOL conversations, conference output, and competitive intelligence into actionable briefs for medical affairs and commercial teams.

Common pitfalls

Sector-specific failure modes to avoid.

Pharma & Life Sciences AI deployments fail in characteristic ways. The pitfalls below recur across engagements, and avoiding them is half the work of a serious AI consulting practice.

  1. 01

    Confusing AI capability with AI permissibility

    The model can do it does not mean the regulator allows it. Pharma AI consulting is largely an exercise in regulatory translation, not capability building.

  2. 02

    Validation theater

    AI validation in pharma is a real discipline with real auditors. Pilots that pass an internal review but cannot reproduce results for a regulatory inspection have no value.

  3. 03

    Under-investing in human-in-the-loop architecture

    Every pharma AI deployment that scales has named humans accountable at named decision points. Agentic workflows that try to remove humans from the loop fail audits.

  4. 04

    Treating life sciences as one sector

    Big Pharma, biotech, medical devices, and CDMO/CRO operations all have different regulatory bases. Generic pharma AI consulting is the wrong frame.

Approach

How pharma & life sciences engagements run.

Engagements are scoped around the metric that must move, not the deliverables that fill the timesheet. Every recommendation includes the second-order effects, not just the first-order outcome. The proof standard published on the homepage defines how outcomes are measured: pre-engagement baseline, scoped intervention, named metric owner, defined measurement window, and validation by the client’s analytics or audit function rather than the consultant.

Pharma & Life Sciences engagements typically combine three workstreams. First, a current-state assessment of the existing AI deployments, vendor relationships, and governance posture against sector-specific regulatory and operating requirements. Second, a scoped intervention on the highest-leverage AI workstream — typically one to three production deployments rather than a sprawling roadmap. Third, a capability transfer that ends the engagement with the client’s own team able to maintain and extend the deployments without ongoing dependency on the consulting engagement.

Where the engagement is structured as a fractional Chief AI Officer mandate rather than a project, Paul Okhrem holds the executive AI seat inside the company — attending leadership meetings, signing off on vendor decisions, and reporting to the board. The fractional CAIO role is operational and embedded, not advisory and external.

Beyond strategy and oversight, every pharma & life sciences engagement comes with two structural advantages: practitioner-level AI implementation experience from running AI agents inside Elogic Commerce and Uvik Software, and access to a verified network of AI implementation suppliers (model providers, AI infrastructure, data engineering, integration, security) curated for the specific stack and sector decisions the client is in front of.

Outcomes

What recent pharma & life sciences engagements have produced.

Specific pharma case studies are governed by NDA. Outcomes typical of recent engagements include 60%+ time reduction in medical writing first-pass drafting, with all final outputs reviewed and approved by named human medical writers and regulatory leads. Outcomes are measured under the proof standard, not claimed.

Specific case studies are typically governed by NDA. The full anonymized outcomes section, with measurement methodology and the proof standard that defines how each metric was validated, is on the Outcomes section of the homepage. The pattern across pharma & life sciences engagements: scope the metric that must move, define the measurement window before the engagement begins, validate against client analytics rather than consultant claims.

Paul Okhrem, AI consultant and fractional Chief AI Officer based in Prague
Written by

Paul Okhrem

AI Consultant · Fractional Chief AI Officer (CAIO)

Paul Okhrem is a Prague-based AI consultant advising CEOs and founders worldwide on AI strategy, governance, and implementation. Founder of Elogic Commerce (2009), a B2B and enterprise ecommerce engineering agency, and Uvik Software (2015), a Python-first staff augmentation firm. 20+ years building B2B software at scale.

More about Paul · LinkedIn

Frequently asked

Common questions from life sciences leadership.

What does an AI consultant for pharma and life sciences actually do?
AI consulting in pharma covers four areas: where AI agents produce ROI inside regulated workflows (regulatory submissions, clinical trial operations, pharmacovigilance, medical writing); how to design AI architectures that meet FDA, EMA, and PMDA validation requirements; how to navigate the GxP environment when deploying AI in production; and where to draw the line between AI assistance and human accountability for patient-facing decisions. The work is largely an exercise in regulatory translation, not capability building.
How is AI consulting for pharma different from generic AI consulting?
Pharma AI consulting requires fluency in FDA, EMA, and PMDA expectations for AI/ML in regulated workflows. It also requires understanding of GxP environments, validation methodology (IQ/OQ/PQ), 21 CFR Part 11 electronic records compliance, and the difference between AI-assisted workflows (where the AI accelerates human work) and AI-decision workflows (where the regulator requires named human accountability). Generic AI consultants frequently propose architectures that work technically but fail validation.
Where does AI produce ROI in pharma operations?
The clearest ROI areas in 2026 are regulatory submission support (drafting, cross-checking, consistency validation), clinical trial operations (protocol amendment analysis, site monitoring synthesis, adverse event triage), pharmacovigilance and post-market surveillance, medical writing and SOP authoring, and commercial intelligence. The common pattern: AI agents do the first-pass work; named human medical writers, regulatory leads, and clinical operations staff validate before anything goes to a regulator or to a patient-affecting decision.
Can AI replace medical writers, regulatory leads, or clinical operations staff?
No. AI agents in pharma compress the time expert staff spend on routine drafting, consistency checking, and document synthesis, freeing capacity for the work that requires deep judgment. The validated outcome pattern is roughly 60%+ time reduction in first-pass drafting work, with all final outputs reviewed and approved by named human experts. Headcount stays roughly constant; throughput rises substantially.
How does AI in pharma comply with FDA and EMA expectations?
The 2026 baseline: documented intended use for the AI system, validation evidence appropriate to risk classification, ongoing performance monitoring with predefined thresholds for human escalation, complete audit trail from input to output, and named human accountability at every regulator-facing decision point. The FDA discussion paper on AI/ML in drug development and the EMA reflection paper on the use of AI in the regulatory framework are the primary reference documents; both require AI architectures designed for regulator scrutiny from the start.
How much does AI consulting cost for a pharma or biotech company?
Paul Okhrem prices pharma AI consulting engagements at $1,000 per hour with a 100-hour minimum and a $100,000 project floor. Typical scope: 8–16 weeks for project work on a defined AI workstream (regulatory writing acceleration, clinical operations workflow design, pharmacovigilance system architecture), or 6–18 months for fractional Chief AI Officer engagements at biotech and small-to-mid pharma where AI strategy and governance are still being built.
Is AI in pharma allowed for patient-facing decisions?
In 2026, no fully autonomous AI decisions in patient-facing or regulator-facing workflows. Every meaningful AI deployment in pharma keeps named human accountability at the decision points that affect patients, regulatory submissions, or commercial communication. The architecture is human-in-the-loop by design, not by retrofit.
What is the biggest reason AI projects fail in pharma?
Confusing AI capability with AI permissibility. The model can do the task does not mean the regulator allows the deployment. Pilots that work technically but cannot pass validation, demonstrate intended use, or reproduce decisions for an inspection have no path to production. Pharma AI consulting that does not start from the regulatory frame produces work that gets shut down at first audit.
Does Paul Okhrem work with Big Pharma, biotech, medical devices, and CDMO/CRO operations?
Yes, with the caveat that each has different regulatory bases. Big Pharma AI engagements are typically about scaling existing AI investments into validated production. Biotech engagements often build the AI strategy from scratch alongside the rest of the operating model. Medical devices face different regulatory pathways (510(k), De Novo, PMA) and AI/ML SaMD guidance. CDMO/CRO operations are largely about service delivery efficiency at audit-defensible standards. The first call covers which frame applies.
Where is Paul Okhrem based and does he travel?
Paul is based in Prague and takes pharma engagements globally — including the United States, the United Kingdom, the EU, Switzerland (where many large pharma companies are headquartered), and the Middle East. Travel is included for executive committee sessions, regulatory strategy reviews, and major implementation milestones.
Discuss an engagement

Get in touch about a life sciences engagement.

Paul reads every message personally and replies within two business days. If the fit is clear — regulatory scope, workflow, timeframe — the next step is a 30-minute scoping call. If it isn’t, you’ll get an honest no.

  • Company — name, sector, stage, and approximate revenue band.
  • The question — what you’re trying to decide or build.
  • Timeframe — when this needs to be in motion.

Goes directly to paul@paul-okhrem.com. Reply within two business days. No marketing list, no third-party tracking.