Microsoft 365 & Copilot
More than two decades around the Microsoft productivity ecosystem, with current experience translating Microsoft 365, Copilot and AI capabilities into practical enterprise adoption.
Turning Microsoft 365 Copilot and AI into better work. And measurable business value.
Currently at dentsu · EMEA Microsoft 365 & AI adoption · 46 countries · ~20,000 users
I help international organisations turn Microsoft 365, Copilot and AI capabilities into practical ways of working that people can adopt, organisations can scale and leaders can connect to business value.
With 25 years across Microsoft productivity, consulting, entrepreneurship and enterprise change, I work at the intersection of technology, people, workflows and business outcomes. I currently lead Microsoft 365 and AI adoption across an EMEA environment spanning 46 countries and approximately 20,000 users at dentsu.
The goal is not adoption for its own sake. It is making new capabilities useful in real work, scaling what works responsibly, and building evidence that it makes a difference.
Microsoft 365 · Copilot · Digital Workplace · AI adoption · Work redesign · Business value
01 / What I bring
Deep Microsoft experience. A wider view of transformation.
I connect Digital Workplace technology with the business scenarios, people, governance and evidence needed to turn new capabilities into sustainable ways of working.
Enterprise adoption & enablement
Design adoption approaches that connect onboarding, communications, learning, champions, managers and ongoing support, rather than treating adoption as a one-off launch.
Business scenarios & work redesign
Work with teams to identify where AI can improve real tasks and workflows, translate those opportunities into practical scenarios, and determine what needs to change around the technology.
International stakeholder orchestration
Bring business leaders, functions, markets, technology teams and change partners into a shared conversation about priorities, adoption and next decisions across complex matrix organisations.
Responsible AI in practice
Help people understand where AI can assist, where outputs need checking, where human judgement remains essential and how accountability works in day-to-day use.
Measurement & value realisation
Use adoption, readiness and work-level evidence to understand what is working, where constraints remain and whether changes in tasks and workflows are translating into meaningful business outcomes.
02 / My point of view
Adoption matters when work improves.
Usage is useful evidence. It is not the outcome.
The important question is whether AI helps people perform relevant work better and whether that improvement survives the rest of the workflow and creates something the organisation values.
Who is using AI, and for what?
Reach · frequency · applicationIs a specific task performed better?
Quality · net effort · errorsDoes the end-to-end workflow improve?
Lead time · rework · outputWhat does the improvement mean for the organisation?
Capacity · service · quality · cost · margin
Progress at one level does not guarantee progress at the next.
A faster draft may create more review work. Time saved only creates value when that capacity is used differently. And some of the most important benefits, including better quality, stronger decisions or improved customer experience, may not initially be financial.
That is why I connect adoption data with evidence about the work itself.
03 / How I approach AI adoption
Start with the work. Find the constraint. Test what changes.
Enterprise AI adoption rarely fails for one reason. The constraint may be the opportunity selected, access, skills, willingness and trust, management behaviour, workflow design, governance, reuse or measurement.
The task is to understand what is actually limiting the outcome before deciding what intervention to scale.
Better work
& business value
& business value
Eight perspectives. One outcome.
These perspectives coexist. An organisation can be strong in one and constrained in another.
Perspective 01
Opportunities in the Work
Are we selecting AI applications that can improve a relevant outcome?
Select an area to explore it.
Diagnostic perspectives, not sequential stages.
No combined maturity score.
The bottleneck principle
The constraint that matters most is not automatically the lowest-scoring area. It is the constraint most plausibly preventing the desired outcome.
Unsuitable work, missing access, insufficient capability, low willingness or trust, management decisions, workflow constraints, governance friction, limited reuse or weak measurement can each create different problems.
From evidence to the next decision
- 01
Frame the question
Start with a business outcome, workflow, owner and decision.
- 02
Diagnose the constraint
Combine work examples, conversations, observation and data to understand what is limiting progress.
- 03
Design the smallest coherent change
Address the likely constraint and define what evidence would indicate improvement.
- 04
Test in practice
Measure what happens to quality, effort, errors, workflow performance and user experience.
- 05
Decide and learn
Continue, adapt, scale, transfer or stop according to the evidence.
The principle: the intervention follows the diagnosis.
Training fits a demonstrated skills problem. Governance has to work in real scenarios. Workflow problems may require redesign rather than more enablement. And sometimes the right decision is not to scale.
Explore possible interventions
Opportunity mapping
Identify tasks where AI could improve a meaningful outcome, using work examples and conversations with the people doing the work. Useful when the team needs to decide where to start.
Role-based enablement
Help people practise AI use on tasks relevant to their role, including checking outputs and recognising limits. Useful when skills or confidence are holding back appropriate use.
Management activation
Help managers set clear expectations, create room for learning and experimenting, address concerns transparently and explain how AI may affect work, capacity and roles. Useful when people need permission, direction, trust or support to change how they work.
Work redesign
Adjust tasks, handovers and review steps so an AI-assisted task improves the whole workflow. Useful when local time savings create extra work elsewhere.
Operational governance
Turn policies into practical guidance on data use, output checks, accountability and escalation. Useful when people are unsure what is allowed or controls are difficult to apply.
Learning & scale
Capture working examples and help other teams test and adapt them in their own context. Useful when promising practices remain isolated or have not yet been shown to transfer.
Value measurement
Compare results with a useful baseline, including quality, effort, rework and business outcomes. Useful when usage figures alone cannot support a decision to continue, adapt or stop.
Adoption-friction remediation
Find and remove practical barriers such as missing permissions, unclear guidance or tools that interrupt work. Useful when people have the skills and motivation but still struggle to use AI.
Workforce & role redesign
Work with employees and managers to understand how AI changes tasks, responsibilities, expertise and career development, and make workforce consequences explicit rather than leaving people to infer them. Useful when AI creates uncertainty about roles, workload, skills or future opportunities.
Agentic operating models
Design how people supervise AI agents that carry out multi-step work, including approval boundaries, monitoring and recovery. Useful when moving beyond individual assistance towards delegated tasks.
04 / Experience
25 years of making Microsoft technology useful in real work.
My career has moved from Microsoft productivity and training through cloud entrepreneurship and senior consulting into enterprise Microsoft 365 and AI adoption.
The constant has been connecting technology with people, work and business outcomes.
September 2016 - presentdentsu
Technology Adoption Manager, EMEA
Microsoft 365 & AI adoption
I lead Microsoft 365 and AI adoption across an EMEA environment spanning 46 countries and approximately 20,000 users, connecting regional priorities and Microsoft capabilities with practical ways of working across markets and functions.
My role spans stakeholder partnership, Copilot enablement, role-based scenarios, communications, learning and champion communities, alongside adoption, readiness and usage insight across a complex, distributed organisation.
46 countries · ~20,000 users · EMEA scope
- Shape role-based Copilot adoption approaches that connect onboarding, ongoing enablement and business scenarios to real work.
- Work across regional and local stakeholders to translate enterprise priorities into practical adoption activity.
- Help teams use AI responsibly by making clear where outputs need verification, where human judgement matters and who remains accountable.
- Use usage, readiness and adoption evidence to identify friction, focus stakeholder conversations and determine where additional intervention is needed.
- Connect individual AI use with the broader question of work redesign, including how tasks and workflows need to change for value to emerge.
July 2014 - July 2016Breinwave
Senior Consultant, Cloud Productivity
Led Microsoft Office 365 programmes for organisations of up to 5,000 end users.
Combined technology adoption, business analysis and stakeholder engagement across pre-sales and delivery, including business-scenario workshops, adoption programmes and C-level conversations.
This work built the foundation for how I approach AI today: start with what people are trying to accomplish, not with the feature being deployed.
2008–2014 · Netherlands & AustraliaEntrepreneurship & market development
Founded Worktivity Solutions during the early Microsoft cloud market, later developed a Sydney business unit for Paradyne and created the Australian market-entry platform for Storgrid.
The work combined technology, commercial strategy, partnerships, delivery capability and the practical challenge of creating demand for unfamiliar technology.
It also gave me first-hand experience of operating internationally, building capability and making decisions with limited structure around them.
2001–2008 · NetherlandsNew Horizons Learning
Training & operational leadership
Progressed from Microsoft Office Trainer through senior training and product roles to Operations & Training Manager.
Managed 15 employees, served on the management team and used Microsoft technology and SharePoint to improve internal processes and workflows.
View career history
September 2016 - present
Technology Adoption Manager, EMEA
dentsu · EMEA
July 2014 - July 2016
Senior Consultant, Cloud Productivity
Breinwave · Netherlands
April 2013 – April 2014
Channel Manager, Australia / New Zealand
Storgrid · Sydney
August – December 2011
Co-owner / Senior Consultant
Paradyne · Sydney
November 2008 – July 2011
Founder / Consultant, Cloud Productivity
Worktivity Solutions · Amsterdam
December 2006 – December 2008
Operations & Training Manager
New Horizons Learning · Utrecht
April – November 2006
Products & Programs Manager
New Horizons Learning
September 2003 – March 2006
Senior Microsoft Office Trainer / Consultant
New Horizons Learning
November 2001 – August 2003
Microsoft Office Trainer
New Horizons Learning
05 / About & contact
Technology changes. The connection to people, work and value remains.
I’m Paul van Lingen, a Netherlands-based Digital Workplace and AI adoption leader.
Over approximately 25 years, my work has evolved alongside Microsoft productivity technology, from Office and SharePoint through cloud collaboration and Microsoft 365 to Copilot and enterprise AI.
Today, I am particularly interested in senior in-house roles where I can help an international organisation scale Microsoft 365 Copilot and AI from initial adoption towards business scenarios, changed workflows, responsible use and measurable value.
I am based in the Netherlands and particularly interested in EMEA or global roles that can be operated from the Netherlands in an international hybrid or remote model.
Languages
Dutch · native
English · fluent
Education
Industrial Engineering and Management
Technical University Eindhoven · Propedeuse
Let’s talk about better work.
If you are building an enterprise Digital Workplace, Copilot or AI adoption capability and the challenge is no longer simply deploying the technology, I’d be interested in the conversation.
Connect on LinkedInContact me through LinkedIn to discuss a role or request my CV.