AI Adoption
Playbook
An introduction to
successful AI transformation
with hands-on insights
AI will change
the way we work
now forever
02
Dear
reader,
03
When we started Langdock, one thing
quickly became clear: AI is already a top
priority for many forward-thinking
companies, but there’s still a lot of
uncertainty about how to actually make
it work. Many pilot projects don’t lead
to the adoption and impact that
companies hope for.
Over the last two years, we've helped
hundreds
of
organizations
to
successfully
adopt AI
and
drive
business results – not just as a software
provider, but as a a trusted partner on
their AI journey. We’ve learned that
while innovation is tech-driven, change
is people driven. Adopting AI isn’t just a
technical project – it’s a transformation
that
affects
every
part
of
an
organization:
leadership,
strategy,
culture, and processes.
Earlier this year, we organized our first
conference in Berlin, and we are excited
to welcome you
to
our
second
conference
today.
Today,
you’re
surrounded by people facing similar
questions. Take this chance to connect,
share, and learn from each other in
unfiltered, in-person conversations.
To get things started, we tried to
compile all our learnings into this
workbook that brings together lessons
from all the AI rollouts we’ve supported
since starting Langdock. It’s practical,
hands-on, and filled with real examples
from our customers - so you can find
ideas and insights you can use in your
own organization.
Welcome to the Dock –
We’re happy to have you!
Lennard Schmidt
Founder & CEO, Langdock
04
Innovation is
tech-driven,
AI Adoption Playbook
05
but change is
people-driven.
06
The AI
Adoption Path
Preparation
Pilot
Define Your AI Strategy
Establish a Steering Group
Discover AI Champions
Invite Champions to the Platform
Experiment, Experiment, Experiment
Integrate Deeply into Existing Workflows
Prepare Your Workspace for Larger Rollout
07
Rollout
Longterm Success
Roll Out Broadly
Manage Change Actively
Organize In-Person Events
Establish a Company-Wide AI Culture
Measure Success with KPIs and Soft Metrics
Move into Advanced Use Cases and Automation
08
Phase 1
Merck Innovation Center,
Darmstadt
09
Preparation
Define Your AI Strategy
Establish a Steering Group
Discover AI Champions
10
Pilot
Rollout
Longterm success
Preparation
Knowing your "why"
Best practice:
Go broad first, add depth later
Define Your
AI Strategy
Every successful AI journey starts with a clear
purpose. Before you dive in, take a moment to define
what AI means for your organization.
Before launching any AI initiative, ask yourself: What is
our vision for AI?
Different organizations have different drivers. Some aim
to scale operations without expanding headcount. Others
seek to preserve institutional knowledge as experienced
employees retire. Many focus on elevating the quality of
work by automating tedious tasks that drain their teams'
time and energy. Still others want to attract and retain top
talent by offering cutting-edge tools that make work more
meaningful and innovative. We see all of these
motivations succeed, but the critical factor is clarity.
Know your "why" and align your AI strategy with your
broader company goals. Without this, AI initiatives
become disconnected experiments rather than strategic
investments that move the business forward.
Expecting to fully automate complex workflows on day
one usually ends in frustration. Organizations
underestimate the undocumented institutional
knowledge and edge cases and embedded in their
processes.
Start by becoming AI-native as an organization. Introduce
AI tools widely for everyday tasks. Let people build
intuition for what AI can and cannot do. As your
organizations’ AI fluency grows, you'll naturally identify
high-impact opportunities for deeper integration. Teams
that understand AI's strengths and limitations will design
better automation and extract more value.
11
Longterm success
Rollout
Pilot
Preparation
If we don’t adopt AI,
We believe AI will allow us to
Secure C-level buy-in
AI adoption is a fundamental organizational change, not
just an IT project. C-level buy-in is essential. It signals that
AI is a strategic priority and an enabler for efficiency,
quality, and innovation.
Executive sponsorship should go beyond budget approval.
It requires visible, ongoing support that removes
organizational barriers, ensures adequate resources for
training and implementation, and keeps AI prominent in
company communications.
Without strong support from
management, 95% of AI initiatives
fail during the implementation
phase.
Top-down vs bottom-up?
From our experience, the answer is both: leadership buy-
in provides direction and resources, while champions
within teams drive adoption through hands-on use cases.
The most successful organizations create a pincer
movement: leaders set the vision and remove barriers,
while frontline advocates demonstrate value and build
momentum.
Questions to ask yourself
– The GenAI Divide, MIT 08/2024
AI Leader
C4Level Sponsor
12
Pilot
Rollout
Longterm success
Preparation
Leadership
roles
Establish a
Steering Group
Clear role definitions prevent friction and ensure
accountability throughout the AI adoption journey.
These roles have proven essential for successful
implementation across organizations.
A C-level sponsor creates the
conditions for success by providing
authority, resources, and consistent
messaging that connects AI
initiatives to broader organizational
objectives.
They take full responsibility for the AI
transformation by approving
necessary budgets for licenses and
training and by regularly sharing
progress and achievements.
Their visible backing provides the
mandate and resources needed to
move AI initiatives forward.
The AI leader can be an individual or
small core team who owns strategy,
cross-functional coordination, and
execution throughout the entire
adoption initiative.
They develop and execute the rollout
plan. Their responsibilities span
managing the champions program,
tracking adoption metrics, and
designing training initiatives. They
work closely with IT on technical
integration while maintaining a direct
line to C-level to ensure rapid
alignment and decision-making when
obstacles arise.
Beyond the functional impact, this
role can offer significant career
advancement. AI leaders and
champions gain exposure across the
entire company, often interacting
directly with senior leadership and
the board, making it a strategic move
for anyone looking to position
themselves as an internal AI expert.
Communications Team
IT & Infrastructure Team
13
Longterm success
Rollout
Pilot
Preparation
Supporting
roles
The Communications Team drives
awareness and engagement by
sharing internal campaigns like
newsletters, intranet updates
featuring informational contents and
success stories. Their messaging
helps sustain momentum by
celebrating wins and reinforcing why
adoption matters. When done well,
these communications transform AI
from an abstract initiative into
something tangible that people
understand and want to be part of.
IT and Infrastructure Managers
ensure a secure and reliable
foundation by installing and
operating the AI platform including
Single Sign-On, network and security
configuration and integrating it with
existing systems such as ERP and in-
house tools.
Beyond the technical setup, their
involvement builds employee
confidence by signaling that the
platform meets the organization's
security standards.
Their role evolves throughout the
implementation journey: during the
pilot phase, they experiment with
department-specific use cases. During
the rollout, they deliver training and
serve as the first point of contact for
colleagues. In later stages, they lead
initiatives towards more advanced
use cases.
This approach works because people
trust their peers. When colleagues see
champions successfully using AI to
solve
real
problems,
it
creates
authentic inspiration that top-down
directives rarely achieve. The most
effective
champions
are
not
necessarily
the
most
senior
or
technical team members, but rather
individuals with genuine enthusiasm
for trying new tools and natural
problem-solvers.
To
discover
champions,
identify
people
who
already work extensively with AI
privately and/or at work, or check your
usage analytics to find power users if
you have an existing solution. Beyond
data, simply ask who would be
interested - you'll often be surprised
by who steps forward with fresh
energy and perspectives.
Establishing “Champions” is our top recommendation
for driving AI adoption. These early adopters serve as
internal experts within their departments, ideally with
at least one person per team.
Discover AI
Champions
14
Pilot
Rollout
Longterm success
Preparation
C-level sponsor with board mandate; Sets
AI adoption as a top priority and secures
budget and resources; Communicates
progress and aligns AI initiatives with
business goals
C-level sponsor with board mandate; Sets
AI adoption as a top priority and secures
budget and resources; Communicates
progress and aligns AI initiatives with
business goals
Pushes for a “data first” culture; Owns
internal storytelling & upskilling
Manages rollout, technical integration,
and ongoing operation of Langdock;
Coordinates stakeholders across the
organization; Owns user feedback and
feature prioritization; Maintains regular
exchange with the Langdock team
Chief Data & AI Officer
Product Owner, myGPT Suite
Global Head of Data Culture
AI Leader
C4Level Sponsor
How Merck does it
Longterm success
Rollout
Pilot
Preparation
Oversees the technical deployment of
Langdock in Merck’s infrastructure
Build out advanced AI use cases (custom
vector data base/RAG pipelines); Act as
enablers, helping teams adopt,
experiment, and get value from AI tools;
Act as “agent owners” for high impact
agents
Integrates the Langdock product as
central piece into the enterprise AI
architecture; Prevents system silos and
redundancy; Enables seamless
connections with enterprise platforms;
Ensures architectural alignment across
the organization
Product Technical Lead
Lead Architect
Key Users
IT & Infrastructure Team
AI Champions
16
Phase 2
HeyJobs Office
Berlin
17
Pilot
Invite Champions to the Platform
Experiment, Experiment, Experiment
Integrate Deeply into Existing Workflows
Prepare Your Workspace for Larger Rollout
18
Pilot
Rollout
Longterm success
Preparation
Setup a direct feedback channel
Invite Champions
to the Platform
For the pilot, admins and champions explore the
platform's capabilities and gain first hands-on
experience with AI.
Create a dedicated communication channel (#ai-pilot or
#ai-champions) where participants can share wins, ask
questions, and support each other. This builds
momentum and confidence from day one.
Organize regular office hours
Host weekly or bi-weekly sessions where participants
share experiences, ask questions, and troubleshoot
challenges together. Use these sessions to document
insights: emerging use cases, best practices, and
key learnings.
Celebrate your champions
Champions naturally bring high curiosity for AI. Your job is
to fuel that intrinsic motivation and make them feel valued
and proud to be an AI ambassador. Give them visibility,
celebrate their progress, or even hand out exclusive merch
that signals they're part of something important.
19
Longterm success
Rollout
Pilot
Preparation
Don’t start with too many or
too complex use cases. Instead,
start small, experiment and build
up step by step.
Reward curiosity, not just results
Experiment, Experiment,
Experiment
Getting familiar with new tools always requires trial
and error. Give your team explicit permission to
experiment, make mistakes, and share learnings.
Celebrate progress and insights, not just perfect
outcomes. Knowing what fails is as valuable as knowing
what succeeds. This shifts the focus from avoiding
mistakes to building intuition.
Set clear guidelines
Establish clear guidelines around data usage, specifying
what's approved and what's off-limits. When people have
trust in the platform, they feel comfortable to experiment.
Finding first use cases
Ask your AI champions to collect repetitive tasks from
their departments' daily work. Use the matrix to identify
quick wins and prioritize. Focus on high-impact tasks that
seem quick to implement first.
Iterate, then systemize
Once you identify use cases that work, translate them into
scalable systems using prompt libraries or shared
assistants.