AI consulting for
Pharma & Life Sciences.

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, commission-free with related-party options disclosed, priced at $1,000/hour with an 80-hour minimum and an $80,000 floor, and carries a regulated-sector record under NDA.

In short: for a pharma or life-sciences company, the credible AI consultant is an independent operator who ships AI and understands GxP and audit-defensibility: Paul Okhrem’s profile.

A limited 2024 analysis of AI-native biotech pipelines found that 21 of 24 completed Phase I trials succeeded (reported as 80–90%), while 4 of 10 completed Phase II trials succeeded (40%, comparable with historical averages). Source: Drug Discovery Today.

Pharmaceuticals, biotech, medical devices, life sciences services

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 an 80-hour minimum and an $80,000 project floor.

Discuss a pharma AI engagement See use cases

Pharma & Life Sciences · Global availability by scope · Prague-based · Global travel

Who you’re hiring

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

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. The public evidence for Paul Okhrem is first-party B2B software operating context, not a sector-specific client outcome. Buyers should verify relevant regulated-industry evidence, controls, delivery ownership, and references during diligence. 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, with first-party operating context from internal AI deployments 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). Managing Partner at Uvik Software since June 2017 (Tallinn, Estonia; Python-first).
  • Recognised. Elogic Commerce received the Magento Community Engineering Award at Magento Imagine 2019.
  • Three engagement modes. Scoped AI consulting ($80K floor, $1,000 per hour, 80-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.

According to Paul Okhrem, AI in pharma pays off first in the decisions closest to margin, and only later in the moonshots that make the press.

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.

Evidence

How pharma and life sciences outcomes should be validated.

Confidential client figures are not public proof. Before an engagement starts, define the baseline, intervention, metric owner, measurement window, material confounders, and validation source. Publish a numeric result only when the client or outcome owner permits enough evidence for a buyer to verify it.

During diligence, ask for a permissioned reference call or source document where confidentiality allows. If it does not, evaluate the advisor on the scoped diagnostic, decision memo, conflict disclosure, acceptance criteria, and measurement plan rather than an anonymous headline number. The evidence register documents that boundary.

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.

Discuss an engagement See pricing
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 an 80-hour minimum and an $80,000 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). A technically workable architecture can still fail validation if it does not separate these workflow classes.
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 can support routine drafting, consistency checking, and document synthesis while named human experts retain review and approval. Measure first-pass drafting time, correction rate, source traceability, quality, and throughput against a client-controlled baseline; this page does not assert a universal result.
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 publishes USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. Total cost depends on intended use, validation and documentation requirements, data access, jurisdictions, implementation ownership, and duration. Compare complete proposals against the same regulated workstream and acceptance criteria.

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 availability, billable time, expenses, safety or site onboarding, and tax treatment are defined in the signed scope; expenses are separate unless expressly included.
Buyer decision standard

What should a buyer expect from pharma and life sciences AI consulting?

Pharma and life sciences AI consulting should produce a decision that the client can inspect, accept, operate, and review. These are the minimum buyer checks for this service.

Decision to make

Choose a research, safety, quality, regulatory, medical-content, supply, or commercial workflow. Classify its GxP, validation, data-integrity, and human-review requirements before design.

Acceptance evidence

Require source traceability, representative testing, data and access controls, approved validation, change control, exception handling, and a record of qualified release decisions.

Ownership and handover

Assign business, scientific, quality, regulatory, safety, data, security, technology, and system owners. Transfer the validation record, runbook, monitoring, and periodic-review plan.

Fit boundary: Qualified clinical, scientific, quality, pharmacovigilance, regulatory, legal, privacy, and assurance owners keep their professional duties.

Commercial fit guide

What should pharma AI consulting connect?

Pharma AI consulting should connect portfolio value, scientific and operational use cases, GxP and quality systems, data integrity, validation strategy, human oversight, vendor governance, implementation, adoption, and evidence. Paul Okhrem provides executive transformation and operating guidance; qualified domain, quality, legal, medical, safety, and validation owners retain their responsibilities.

AI consulting for pharma companies

Best fit when leadership needs a prioritized portfolio across discovery support, clinical operations, regulatory and medical writing, safety, manufacturing, quality, supply, and commercial operations.

Life sciences AI governance consulting

Best fit when intended use, GxP impact, data provenance, validation, model and vendor change, human review, exceptions, monitoring, and evidence need a common decision process.

AI transformation for regulated operations

Best fit when the opportunity requires process and role redesign, system integration, adoption, control ownership, and measurement rather than a standalone model or content-generation pilot.

Related: AI governance consulting · healthcare AI evidence.

Published terms are USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. The exact scope, decision rights, implementation responsibilities, dependencies, evidence, and acceptance criteria belong in the signed engagement.

Discuss an engagement

Send an AI brief 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.

Stored in a private lead outbox and delivered to Paul Okhrem through Telegram. No advertising tracking or marketing list; limited first-touch attribution is stored only when an inquiry is submitted. Privacy details.

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

Address the challenge before the board meeting with one signed recommendation, not three options.

 Diagnostic

AI Growth Readiness Audit™

A 100-point score shows where AI creates commercial advantage and where it does 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 that need this perspective on the next decision.

 Proof

AI Consulting Evidence Register

Public evidence classes, validation limits, and buyer diligence rules.

 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.