INDUSTRY BENCHMARK · 2026

The 2026 Life Sciences Technical Delivery Benchmark

How do teams in pharma, biotech, clinical research, and digital health actually get technical work delivered, and what slows it down? We are asking senior product, data, and engineering people across the sector.

Five minutesYour personal view, not your organization's official positionIndividual answers stay inside DataDrill

What you receive

Early access to the findings

Every participant receives the 2026 Life Sciences Technical Delivery Report before it is published, so you can see where your team sits against the wider sample on delivery speed, resource allocation, and prioritization.

A small thank you

We send a small thank you in the post to everyone who takes part, where your organization's policies allow. We will email you to arrange it, and you are welcome to decline.

Your individual answers are seen only by the DataDrill team. Nothing identifying you or your employer is ever published or shared outside DataDrill. Please leave out proprietary or project-specific confidential information.

The benchmark

About you

So we know who to send your findings to.

NameSo we know who to address your findings and thank you to.

Work emailWhere we send the findings. We will also use it once to arrange your thank you. Nothing else.

Which area best describes your work?

Digital product or platform management

Data, analytics, or AI

IT, engineering, or architecture

Digital strategy or transformation

Operations or program delivery

Clinical or R&D technology

Other

Delivery reality

How technical work actually gets done in your team right now.

Which of these is your team working toward right now?Select all that apply.

Launching or scaling a digital product or platform

Consolidating or integrating data across systems

Migrating or modernizing infrastructure

Building analytics or reporting capability for internal users

Putting AI or machine learning into a real workflow

Improving data quality or governance

Automating a manual operational process

Integrating systems or teams after a merger or acquisition

Replacing or retiring a legacy system

Other

Where does delivery most often slow down for you?Select up to three.

0 of 3 selected

Engineering or data capacity is not available when needed

Requirements shift faster than the build can absorb

Data is scattered across systems that do not talk to each other

Data quality is not good enough to build on

Internal dependencies on other teams

Approval, review, or governance cycles

Legacy systems that resist change

Unclear ownership between business and technical teams

Procurement or contracting timelines

Knowledge sits with too few people

Infrastructure or environment constraints

Delivery does not usually slow down significantly

Other

What sits in your backlog because there is no capacity to start it?Select all that apply.

Data integration or pipeline work

Automating something still done manually

A prototype or proof of concept we cannot staff

Analytics or reporting we know users want

An AI or machine learning idea we have not tested

Cloud or infrastructure modernization

Documentation, knowledge transfer, or technical debt

Data quality or governance work

Nothing significant, we are keeping up

Prefer not to say

Over the past 12 months, how has demand for data and engineering work in your area changed relative to the capacity available to you?

Demand has grown much faster than capacity

Demand has grown somewhat faster than capacity

Demand and capacity have grown roughly in line

Capacity has grown faster than demand

Both have been broadly stable

Prefer not to say

When capacity is short, what actually happens?Select all that apply.

The work is deprioritized or deferred

Timelines are extended

Scope is reduced

Internal teams absorb the overflow

We hire

We bring in an external partner or contractor

We escalate for more budget or headcount

It depends entirely on the item

Prefer not to say

How confident are you that your delivery capacity matches your roadmap for the next 12 months?

Not confident, there is a clear gapFully confident, capacity matches the plan

What is the longest-standing item on your list that you have not been able to start?Please keep it general. No project names or confidential detail.

If one constraint disappeared tomorrow, which would change the most for your team?

Engineering and data capacity

Data quality

Data accessibility across systems

Internal alignment on priorities

Budget

Governance and approval speed

Legacy system constraints

Specialist skills we do not have in house

Other

Working with external teams

How your organization works with external technical partners.

When your organization brings in an external technical team, which five factors matter most to you?Select up to five.

0 of 5 selected

Technical competence

Understanding of life sciences workflows

Access to senior technical people

Clarity and consistency of communication

Clear scope and clear ownership

Reliability against agreed timelines

Commercial transparency and value for cost

Flexibility when requirements change

Proactive identification of risks

Documentation and knowledge transfer

Ability to work with our existing teams

Ability to work with our existing systems

Ability to start small and prove value before scaling

Security and compliance awareness in regulated environments

Overlap in working hours and responsiveness

Geographic proximity

Relevant evidence or references

If you had to choose only one, what creates the most confidence in an external team early on?

Technical competence

Understanding of life sciences workflows

Access to senior technical people

Clarity and consistency of communication

Clear scope and clear ownership

Reliability against agreed timelines

Commercial transparency and value for cost

Flexibility when requirements change

Proactive identification of risks

Documentation and knowledge transfer

Ability to work with our existing teams

Ability to work with our existing systems

Ability to start small and prove value before scaling

Security and compliance awareness in regulated environments

Overlap in working hours and responsiveness

Geographic proximity

Relevant evidence or references

Which three things most often go wrong with external technical teams?Select up to three.

0 of 3 selected

Scope was vaguer than it appeared

Capability was oversold

Deadlines slipped

Costs came in above expectation

Communication was slow or inconsistent

The delivery team kept changing

Documentation and knowledge transfer were weak

They did not understand the industry well enough

No clear ownership when something broke

Process was too rigid when requirements changed

Poor fit with our internal teams

Not enough overlap in working hours

Nothing significant has gone wrong

Other

If your team needed outside help on something technical, what shape of engagement would be easiest to get approved?

A short assessment or scoping exercise

A small time-boxed piece of work with a defined output

A fully specified fixed-scope project

Specialists working inside our existing team

An external team owning a defined workstream

Advisory input without delivery

It would depend on the item

I would not be the person to judge

What do external technical teams most often misunderstand about how organizations like yours work?In your own words. This is the question we learn the most from.

The 2026 Life Sciences Technical Delivery Benchmark

Five minutesYour personal view, not your organization's official positionIndividual answers stay inside DataDrill

What you receive

Early access to the findings

A small thank you

We send a small thank you in the post to everyone who takes part, where your organization's policies allow. We will email you to arrange it, and you are welcome to decline.

The benchmark

About you

So we know who to send your findings to.

NameSo we know who to address your findings and thank you to.

Work emailWhere we send the findings. We will also use it once to arrange your thank you. Nothing else.

Which area best describes your work?

Digital product or platform management

Data, analytics, or AI

IT, engineering, or architecture

Digital strategy or transformation

Operations or program delivery

Clinical or R&D technology

Other

Delivery reality

How technical work actually gets done in your team right now.

Which of these is your team working toward right now?Select all that apply.

Launching or scaling a digital product or platform

Consolidating or integrating data across systems

Migrating or modernizing infrastructure

Building analytics or reporting capability for internal users

Putting AI or machine learning into a real workflow

Improving data quality or governance

Automating a manual operational process

Integrating systems or teams after a merger or acquisition

Replacing or retiring a legacy system

Other

Where does delivery most often slow down for you?Select up to three.

0 of 3 selected

Engineering or data capacity is not available when needed

Requirements shift faster than the build can absorb

Data is scattered across systems that do not talk to each other

Data quality is not good enough to build on

Internal dependencies on other teams

Approval, review, or governance cycles

Legacy systems that resist change

Unclear ownership between business and technical teams

Procurement or contracting timelines

Knowledge sits with too few people

Infrastructure or environment constraints

Delivery does not usually slow down significantly

Other

What sits in your backlog because there is no capacity to start it?Select all that apply.

Data integration or pipeline work

Automating something still done manually

A prototype or proof of concept we cannot staff

Analytics or reporting we know users want

An AI or machine learning idea we have not tested

Cloud or infrastructure modernization

Documentation, knowledge transfer, or technical debt

Data quality or governance work

Nothing significant, we are keeping up

Prefer not to say

Over the past 12 months, how has demand for data and engineering work in your area changed relative to the capacity available to you?

Demand has grown much faster than capacity

Demand has grown somewhat faster than capacity

Demand and capacity have grown roughly in line

Capacity has grown faster than demand

Both have been broadly stable

Prefer not to say

When capacity is short, what actually happens?Select all that apply.

The work is deprioritized or deferred

Timelines are extended

Scope is reduced

Internal teams absorb the overflow

We hire

We bring in an external partner or contractor

We escalate for more budget or headcount

It depends entirely on the item

Prefer not to say

How confident are you that your delivery capacity matches your roadmap for the next 12 months?

Not confident, there is a clear gapFully confident, capacity matches the plan

What is the longest-standing item on your list that you have not been able to start?Please keep it general. No project names or confidential detail.

If one constraint disappeared tomorrow, which would change the most for your team?

Engineering and data capacity

Data quality

Data accessibility across systems

Internal alignment on priorities

Budget

Governance and approval speed

Legacy system constraints

Specialist skills we do not have in house

Other

Working with external teams

How your organization works with external technical partners.

When your organization brings in an external technical team, which five factors matter most to you?Select up to five.

0 of 5 selected

Technical competence

Understanding of life sciences workflows

Access to senior technical people

Clarity and consistency of communication

Clear scope and clear ownership

Reliability against agreed timelines

Commercial transparency and value for cost

Flexibility when requirements change

Proactive identification of risks

Documentation and knowledge transfer

Ability to work with our existing teams

Ability to work with our existing systems

Ability to start small and prove value before scaling

Security and compliance awareness in regulated environments

Overlap in working hours and responsiveness

Geographic proximity

Relevant evidence or references

If you had to choose only one, what creates the most confidence in an external team early on?

Technical competence

Understanding of life sciences workflows

Access to senior technical people

Clarity and consistency of communication

Clear scope and clear ownership

Reliability against agreed timelines

Commercial transparency and value for cost

Flexibility when requirements change

Proactive identification of risks

Documentation and knowledge transfer

Ability to work with our existing teams

Ability to work with our existing systems

Ability to start small and prove value before scaling

Security and compliance awareness in regulated environments

Overlap in working hours and responsiveness

Geographic proximity

Relevant evidence or references

Which three things most often go wrong with external technical teams?Select up to three.

0 of 3 selected

Scope was vaguer than it appeared

Capability was oversold

Deadlines slipped

Costs came in above expectation

Communication was slow or inconsistent

The delivery team kept changing

Documentation and knowledge transfer were weak

They did not understand the industry well enough

No clear ownership when something broke

Process was too rigid when requirements changed

Poor fit with our internal teams

Not enough overlap in working hours

Nothing significant has gone wrong

Other

If your team needed outside help on something technical, what shape of engagement would be easiest to get approved?

A short assessment or scoping exercise

A small time-boxed piece of work with a defined output

A fully specified fixed-scope project

Specialists working inside our existing team

An external team owning a defined workstream

Advisory input without delivery

It would depend on the item

I would not be the person to judge

What do external technical teams most often misunderstand about how organizations like yours work?In your own words. This is the question we learn the most from.