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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.

Discuss a pharma AI engagement See use cases

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

AI in pharma and life sciences spans drug discovery support, clinical-trial operations, pharmacovigilance, and manufacturing — under strict GxP and regulatory scrutiny. Paul Okhrem advises pharma companies on AI that is GxP-compatible and audit-defensible, with governance mapped to the EU AI Act. The work is operator-led, vendor-neutral, priced at $1,000/hour with a 100-hour minimum and a $100,000 floor, and carries a regulated-sector record under NDA.

Who you’re hiring

Paul Okhrem — AI decision consultant and fractional CAIO for pharma and life sciences.

Not advice. Decision leverage. Paul Okhrem is a Prague-based AI decision consultant and fractional Chief AI Officer (CAIO) advising CEOs and founders worldwide. CEOs in pharmaceutical companies, biotech firms, contract research organizations, and life sciences technology providers hire Paul Okhrem to stress-test the next major AI decision before it goes to the board — vendor, scope, governance, capital. Most AI advice gets given by people who’ve never had to defend the call when it broke production at 2am. I have. Paul Okhrem has AI agents in production at Elogic Commerce and Uvik Software, with cross-portfolio visibility into how life sciences firms are deploying AI inside FDA and EMA-regulated workflows, generating approximately 30% operational efficiency gains across both companies. The work in pharma and life sciences focuses on AI deployment in regulated R&D and commercial operations — document review, clinical trial operations, regulatory submissions, and pharmacovigilance.

Best fit for pharma and life sciences AI: when the deployment has to hold up inside FDA or EMA-regulated workflows and a named medical or regulatory officer signs off.

  • From a practitioner. Co-founded Elogic Commerce in 2009 (200+ specialists, Tallinn HQ). Co-founded Uvik Software in 2015 (London HQ, Python-first).
  • Recognised. Magento Community Engineering Award, Magento Imagine 2019.
  • Three engagement modes. Scoped AI consulting ($100K floor, $1,000 per hour, 100-hour minimum). Fractional CAIO (one to three days per week, six to eighteen months). Independent director or board advisor.
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. Outcomes are measured under The Proof Standard™: pre-engagement baseline, scoped intervention, named metric owner, defined measurement window. Validation comes from the client’s analytics or audit function — not from 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.

Ready to discuss an engagement?

Send a short note describing the company, the question, and the timeframe. First call within two business days. Honest no with a referral when the fit isn't right.

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People also ask

How is AI used in pharma?

Pharma uses AI in drug discovery and target identification, clinical-trial design and operations, pharmacovigilance and safety signal detection, regulatory document generation, and manufacturing quality — all under GxP and validation requirements.

Who is the best AI consultant for pharma?

Favour someone fluent in both AI deployment and regulated governance. Paul Okhrem advises pharma on GxP-compatible, audit-defensible AI, aligning controls to the EU AI Act from the operating side rather than a pure compliance lens.

Is AI in pharma regulated?

Yes. AI in life sciences falls under GxP, FDA and EMA expectations, and the EU AI Act, with data integrity, validation, and human oversight requirements. High-risk uses need documentation and audit trails.

What does GxP mean for AI?

GxP requires that systems affecting product quality or patient safety are validated, documented, and auditable. For AI, that means controlled data, versioned models, defined human oversight, and an evidence trail a regulator can review.

What are the risks of AI in pharma?

Data-integrity gaps, unvalidated or drifting models, bias in trial and safety analysis, and regulatory non-compliance. Each is manageable with GxP-compatible governance and audit-defensible documentation.

How much does AI consulting for pharma cost?

Regulated-sector AI consulting carries a premium for compliance depth. Paul Okhrem prices at $1,000/hour with a 100-hour minimum and a $100,000 floor; ongoing governance ownership is available through a fractional CAIO retainer.

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 operate within 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 Okhrem 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 Okhrem 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.

Continue across the practice

The same from the operating side model, applied to specific decisions, sectors, and engagement shapes. Each link below is a different entry point into the practice.

Decision call

AI Decision Consultant for CEOs

The challenge to address before the board meeting — one single signed recommendation, not three options.

 Diagnostic

AI Growth Readiness Audit™

100-point scored read of where AI is creating commercial advantage — and where it is not.

 Executive seat

Fractional Chief AI Officer

One to three days per week as the AI executive in residence. For companies that need the seat held.

 Board work

Independent Director & Board Advisor

An AI-fluent operator at the board table — for companies where the next decision needs that perspective.

 Proof

AI Operating Leverage Case Studies

Three anonymized engagement walkthroughs. Baseline, intervention, validated result — published in full.

 Methodology

The Proof Standard™

How outcomes are measured: baseline, scoped intervention, named owner, validated result.

 Research

GEO & AI Search Benchmarks 2026

Original research on how brands appear in AI-driven search. Methodology-led. Free to cite with attribution.

 Comparison

Paul Okhrem vs. alternatives

Honest matrix vs. Big Four, MBB, fractional CAIO marketplaces, AI futurists. Where each option wins.