AI CONSULTING · LIFE SCIENCES · GxP-AWARE

AI consulting for Pharma and life sciences — with a setup that fits your processes.

DHC connects AI strategy with operational implementation for Pharma, Biotech and MedTech. We prioritise real use cases, define governance and policies, assess providers and technical options, establish reliable prompt and workflow standards, and design usage and cost controls. GxP system validation remains a separate service with its own decision and evidence path.

For organisations that want to use AI purposefully but still lack a coherent operating model, technical setup or governance framework.

  • AI readiness
  • Use-case portfolio
  • AI governance & policy
  • Provider & setup selection
  • Prompt & workflow engineering
  • Usage & cost controls
DIRECT ANSWER

What does AI consulting for Pharma and life sciences cover?

AI consulting for Pharma and life sciences connects business priorities, real use cases, data boundaries, technical architecture, provider selection, governance, AI policies, privacy coordination, prompt and workflow standards, usage controls and team enablement. The goal is a clear operating model for useful AI — before a defined GxP use case enters the separate validation lifecycle.

01 Buying situation

When does specialist AI consulting make sense?

The need usually appears before a validation project: AI is already being tested, but priorities, guardrails and technical decisions do not yet form one operating model.

01

Experiments without a portfolio

Teams test tools independently, but no one can say which use cases deserve investment and ownership.

02

Unclear technical setup

Model, provider, hosting, integrations and data access are discussed separately instead of as one architecture decision.

03

Policies without operating rules

A high-level policy exists, but roles, approvals, permitted data, exceptions and escalation paths are not usable day to day.

04

Inconsistent output quality

Prompts remain personal craft. Reliable workflows, reusable skills, review rules and acceptance criteria are missing.

05

Usage and costs are opaque

Access, model choice, consumption and value are not connected, making governance and budget decisions difficult.

06

Regulated boundaries are unclear

Business, IT, data protection and quality need a shared route for non-GxP, GxP-relevant and validation-bound use cases.

02 Consulting modules

Six modules — combined around your actual decision.

The engagement starts with the business and operating context. Technology, governance and enablement follow the intended use — not the other way around.

01

AI readiness and target picture

We assess objectives, current usage, capabilities, decision rights, data conditions and operational constraints, then define a realistic target picture.

02

Use-case portfolio and prioritisation

Ideas are compared by business value, feasibility, data readiness, process impact, risk and ownership. The result is an ordered decision backlog.

03

AI governance and policies

We define roles, permitted use, data classes, approval paths, documentation, human review, exception handling and escalation in a form teams can apply.

04

Provider, model and setup selection

Requirements become a criteria-based comparison of providers, deployment options, integrations, privacy boundaries, controllability and operating effort. Relationships and possible conflicts are disclosed.

05

Prompt and workflow engineering

We turn individual prompts into reusable instructions, repository skills, quality checks and workflows that fit roles, data and approval steps.

06

Enablement, usage and cost control

Teams receive practical operating rules, role-specific learning paths and transparent controls for access, model choice, consumption and review.

03 Engagements

Three clear entry points instead of a generic AI transformation programme.

The right format depends on whether you still need to choose the use cases, establish the operating model or prove one workflow in practice.

01 · STARTING POINT

AI use-case & governance assessment

For organisations with many ideas but no common priorities or guardrails.

  • Readiness and current-use picture
  • Prioritised use-case portfolio
  • Governance, data and setup gaps
  • Decision and implementation path
02 · OPERATING MODEL

AI operating model & policy

For organisations that need enforceable rules, roles and decision paths around existing AI use.

  • Roles, RACI and approvals
  • Permitted use and data boundaries
  • Provider, model and cost controls
  • Exceptions, escalation and review
03 · PRACTICAL PILOT

Controlled AI pilot & workflow enablement

For a selected use case that must become a reliable workflow before a wider rollout decision.

  • Configured prompt, skill or workflow
  • Quality criteria and evaluation set
  • Human review and stop rules
  • Operating handover or validation routing
04 Decision outputs

What remains after the consulting engagement?

Not a generic trend deck, but working decisions and artefacts that your business, IT, data protection and quality functions can continue to use.

01

Readiness and use-case map

Current state, target picture, prioritised use cases, dependencies, owners and decision criteria.

02

Governance and policy framework

Roles, permitted use, data boundaries, approvals, human review, documentation, exceptions and escalation.

03

Provider and architecture decision matrix

Weighted requirements for models, hosting, integrations, access, privacy coordination, controllability and operations.

04

Prompt, skill and workflow standards

Reusable patterns, quality checks, review responsibilities and repository conventions for reliable operational use.

05

Usage and cost-control model

Access principles, model routing, consumption transparency, budget ownership and exception paths.

06

Implementation and validation routing

A clear next-step plan and a defined handover when a selected use case enters a GxP validation lifecycle.

05 Clear ownership

AI consulting and AI validation solve different buyer problems.

WHEN THE PATH IS NOT YET CLEAR

AI consulting for Pharma and life sciences

You need to decide where AI creates value, which setup fits, which rules apply internally and how teams work with it reliably.

  • Use cases and priorities
  • Governance, policy and roles
  • Provider, architecture and data boundaries
  • Prompts, workflows and enablement
Discuss AI consulting
WHEN THE GxP USE IS DEFINED

AI system validation for GxP

A defined AI-enabled system affects a GxP process or regulated data and needs a risk-based validation package for release and controlled operation.

  • Intended use and GxP impact
  • Risk, data and supplier assessment
  • Requirements, testing and traceability
  • Release, monitoring and change control
See AI validation
PRACTICAL AI EXPERIENCE

Advice grounded in our own operating and product experience.

DHC applies the same questions internally that clients must solve: reusable repository skills, usage and cost rules, an AI policy, privacy boundaries, provider choices and quality controls for real workflows.

Daniel Herrmann is the founder of Daniel Herrmann Consulting and Co-Founder & CEO of the independent traqx GmbH. The entities, offers and responsibilities remain separate; the practical software and AI experience strengthens DHC's consulting perspective.

This evidence reflects DHC's own operating and product work. It is not presented as a client track record for a generic AI transformation programme.

06 Fit

Where this service fits — and where it does not.

A good fit

  • Pharma, Biotech or MedTech with concrete operational questions
  • Management, business, IT, data protection and quality need one decision path
  • Use cases, setup, governance or adoption are not yet coherent
  • The organisation is willing to assign owners and make explicit choices

Not this service

  • Pure legal advice on the AI Act or GDPR
  • Development of a proprietary foundation model
  • A provider recommendation without process, data or risk context
  • Evidence and release for an already defined GxP system — that belongs in AI validation
07 FAQ

Frequently asked questions about AI consulting for Pharma and life sciences.

What is the difference between AI consulting and AI validation?

AI consulting clarifies use cases, target state, governance, policies, providers, technical setup, workflows and enablement. AI validation starts with a defined AI-enabled system, intended use and GxP impact, then builds the risk-based evidence for testing, release and controlled operation.

Does DHC support AI governance and AI policies?

Yes. DHC translates objectives and risks into roles, permitted use, data classes, approvals, human review, documentation, exceptions and escalation. The policy is connected to a practical operating model.

Is prompt engineering a standalone DHC service?

Prompt and workflow engineering is a module within AI consulting. The relevant outcome is not one prompt but a reusable, reviewable workflow with context, data rules, quality controls, roles and approval steps.

How does DHC assess providers and potential conflicts of interest?

Providers, models, hosting and integration options are compared against your use cases, data, control needs, operating capability and cost logic. Daniel Herrmann’s relationship with the independent traqx GmbH is disclosed transparently; there is no blanket provider recommendation without context.

Does the consulting also cover GDPR and the EU AI Act?

DHC structures technical and organisational privacy questions, roles, data flows and AI-Act-related governance topics for joint assessment with privacy, legal and compliance functions. DHC does not replace legal counsel.

When does an AI initiative move into GxP system validation?

Once a specific system and intended use are defined and the use affects a GxP process, quality-relevant decisions or regulated data, the validation path is scoped separately. Consulting and validation can build on each other while retaining distinct objectives and deliverables.

Clarify the AI need.
Then decide setup and governance.

In the initial conversation, we classify your current state, priority use cases and the right entry point for an AI use-case and governance assessment.

Book a no-strings strategy call

AI consulting · Pharma · Biotech · MedTech