Claude Code isn’t just for developers. Individuals and knowledge teams can benefit significantly from adopting agentic working: productivity gains, better outcomes, and the bonus of developing real AI skills.
However adopting Claude Code organization wide is more akin to a cultural transformation than a simple tooling roll out. It requires significant strategic investment, thinking through the launch up front, and team upskilling and adoption. There’s ways to make it a success, and there’s ways to fail.
In this article we’re going to cover learnings from helping multiple organizations onboard people onto Claude Code, and successfully roll it out to their teams. This includes what needs to be in place to make it a success, and what you should consider if you’re thinking about doing this.
By the end of the article you will have a playbook on how to roll out Claude Code to your team.
NB. We’ll be using Claude Code as an umbrella term across this article to refer to any sort of local agentic application. This includes Claude Desktop, Claude Code, Codex, Copilot, or using any of these tools via an IDE.
Claude Code & Cowork vs Chatbots
The majority of teams are now familiar with web based AI interfaces: such as embedded chatbots or ChatGPT.
These are:
- Web based: you navigate to it, it’s not native to your environment
- Conversational: back and forth interactions where you give new instructions every time
- Context limited: no access to your files or other applications
Claude Code’s functionality differs from AI chatbots in several significant ways.
- Local: it lives on your machine, and works with local files
- Integrated: it’s usually connected to other applications that matter to you, like your Google drive, or Jira
- Pre-loaded instructions: you create instructions once, and then can reference them repeatedly over time
- Agentic: it can execute actions on your behalf according to pre-set criteria
The combination of integrations and codified instructions, plus local access and agentic functionality makes these local harnesses significantly more powerful, and generates better outputs over chatbots.
Critically it’s not restricted to responding to you: it can also execute commands and write files for you.
Learn more about how Claude Code works with our Claude Code 101 Guide
Ways non-technical users are using Claude Code
The applications for Claude Code are almost endless, but common use cases include:
- Personal productivity and knowledge work: such as inbox management, processing meeting notes or qualitative user research, or conducting analysis
- Coaches: creating plans or walking step by step through how to do certain things
- Automating tasks: such as reviewing and updating Jira tickets, weekly goal round up emails, asset creation, automated analysis runs and so on
- Prototyping and vibe coding: personal software applications for repetitive tasks, vibe coding from customer call transcripts, or design mock ups
Claude Code can perform tasks you might be used to using an IDE for, and significantly outperforms chatbots, due to having access to your context and pre-codified instructions.
Learn Claude Code in a cohort
How far you choose to allow your team to go with Claude is usually a decision around needs, capability, and what your company is comfortable with.
We’ve defined 3 levels:
- Improved context, or ChatGPT+++: Local files and tool access gives dramatically better results than web-based AI. Web based tools are catching up, but there’s still a gap.
- Agentic working: Faster, higher quality results for routine tasks using skills and instructions. Can only be achieved with a local agent.
- Agent OS: Systematically mapping high impact workflows, augmenting them with AI and developing custom UI.
These all require different levels of ambition, investment and hand holding.
Teams won’t organically progress: they’ll require IT support, training, and crucially company focus and investment.
Building a business case
There’s a cost to upskilling your team on this type of tool: licences, credits, time, training. It’s also still hard to measure the ROI on AI investment for teams, since:
- Teams do more: but doing more becomes the norm
- Quality improves: but it’s hard to link quality lifts to costs or revenues
However, anecdotally, from teams who have embraced agentic working we’ve heard impacts like:
- Accelerated value: i.e. time slashed for a front end refactor across a tech team
- Team size reduction: i.e. shrinking squad sizes as teams became faster
- Greater access to data: i.e. faster analyses, and more accessible dashboards
- Morale: reducing daily grind and mental load, and freeing team members to act more strategically
“It is such a cost to not do this. I can’t even elaborate how big a loss of money it is. To not at least have a percentage of your organisation working this way in the next few months… it’s just suicide.” Johnny Quach, CMO/CPO MotorK
Transformation over tooling
This isn’t a simple tool selection, but an AI transformation journey, analogous to a digital transformation journey. This is especially the case if you’re adopting agentic working. It’s important to understand how great a mental and cultural shift this is.
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Preconditions for success
There’s 4 major things to consider:
- Budget: strategic goals require investment as well as ambition
- Time: this level of transformation also requires a time and resource investment
- Security and access: determining what your IT team will allow Claude to access upfront saves a lot of pain later
- Curating key context: preinstalling core company documents to every laptop will ensure every user remains focused on goals, and basic outputs align
Budget
Inference comes with costs. In order to extend Claude Code functionality to your team you’ll need to provide Pro licences as a minimum.
Dependent on your number and complexity of automations, you may also need to provide Max licences.
Finally, you may need to provide extra budget for further credits. Working agentically is significantly more token hungry than chatbot interactions as a baseline.
It’s important to understand where you want to see a return, and to monitor workflows for efficiency and ROI. Some initial proof points can help build confidence and the business case for extending across team members and functions.
If your company has set mission statements around advanced AI adoption however, make sure they’re prepared to put budget behind the goal. That includes test budgets, and enough R&D investment to get things to actually work.
Time
As this is a transformation, not a simple tooling rollout, it’s key to treat it as such. Transformations are usually multi-year journeys that require a long term mentality, and significant time investments.
That means carving out time over time to help people invest in skills, experiment with options, and build and refine workflows. If you don’t give time to adjust and adapt behaviors, they won’t adjust and adapt.
Security and access
The key to major gains from these local harnesses is:
- Context: they have access to your environmental context
- Access: they have access to any tools you routinely use to perform tasks
This means that in order to realise value, it’s key to give tool, skill and local file permissions.
Prior to training or trying to roll out Cowork or Code in your organization, it’s key to have discussed and determined access and functionality with your IT and procurement teams.
Otherwise you can go full steam ahead on investing in the tool, only to realize that impact is limited due to local file, MCP or API access being blocked on security grounds.
Claude 101 Guide
Curating key context
Creating a small subset of key company documents, and making them available to Claude can dramatically improve the outcomes you realise from the tool.
This includes: company strategy documents, OKRs, product architecture, system and process.
By providing a curated selection of the really key things to know, Claude will ensure outcomes align to goals or known processes and constraints.
By providing this for your teams, and ensuring they all have this installed, you’ll ensure alignment and a better starting point for all individuals.
Advance decisions
There’s some big questions it helps to think through and decide in advance:
- Harness: What application will everyone use?
- Tools: Do you have permissions and a process to get everyone connected to tools?
- Files: How will you locally store shared context?
- Skills: Which skills do you want to centralize, vs which do you want to democratize?
- Software: What personal apps are people allowed to make?
- Upskilling: How will you encourage adoption and proficiency
Selecting your application
There’s multiple ways to get started working with a local agentic application that allows you to store context and give file access.
Claude Code started out as a developer tool. Less technical users started to adopt it due to proximity to tech teams and interest.
Recently Anthropic have been investing in a more generalist, user friendly interface to increase the customer base – leading to the desktop app.
When you’re deciding which is right for you, the choice is between a more intuitive UI, and (currently) less functionality, versus increasingly technical options, which have more power:
- Desktop app: intuitive UI, less customization, less power, but fundamentally does the same thing
- Claude Code: more powerful, more applications, but need to work with it in an IDE or a terminal
If you decide to go for Claude Code, the next choice will be between:
- An IDE | Integrated Development Environment: tools like Cursor, Antigravity or VS Code, which provide a more intuitive UI than a terminal
- Terminals or Terminal Emulators: the most powerful option, but also the most daunting seeming, and technical. Tools like Warp or Ghostty
For individuals, getting started with Desktop and then branching out is a very common path, but companies need to decide early on what they’ll install or train on.
Claude Code is also available in the Desktop app, so if you’re running a company wide program, this is likely the best option. It’s also under development and functionality is likely to accelerate.
For product teams, however, you might want to commit to an IDE early, depending on whether you’re looking to only enhance personal productivity or to fundamentally reshape product organization operations.
An IDE is the best environment for prototyping, and picking this type of tooling aligns product managers to devs. Don’t be too scared of an IDE. It’s relatively easy to learn.
There are no bad decisions here, but one thing to consider is the length of time you commit to contracts for.
The tool landscape is changing rapidly: it’s hard in any scenario to say what we might all be using in 12 months. Codex might have overtaken Claude Code, a new challenger might have entered, and open source models are improving all the time.
Switching between tools is not hugely difficult, as the underlying principles remain the same. The skills you learn on any tool are transferable, as are your workflows. The tools even walk you through how to do this themselves.
A note on Copilot:
Many teams are restricted to using this option due to their company’s Microsoft suite.
Copilot has much of the same functionality.
However what we currently see is that it’s less user friendly, and building takes longer in Copilot than with other options. Therefore if this is your default option, budget extra build time and more training hours.
The more context you can give the agent that mirrors what existing team members have, the better the outcomes.
The ideal is to identify the high trafficked, daily tools that your team uses regularly and connect as many of those as possible.
Example
Your team uses Jira and Confluence daily, to manage tickets, write PRDs and manage projects. Claude does not have access to either of these tools. The quality of output it can generate, and the tasks it can perform for the team are therefore severely limited.
Connecting these tools so that Claude can give better responses doesn’t mean that you have to give full access everywhere. You can select where you give Claude read access, over what, and where you give it write access rights.
This permission allocation at the admin level will go a long way to answering concerns about Claude overwriting critical tools or documents.
Finally, your access rights should transfer to Claude, i.e. it can only access what you can access within your account permissions.
If you’re in a SaaS company an added benefit is that by working in this way with other connected tools, you can start to design your own MCP solution, understanding what needs people might have when they connect to your tool and what they want to achieve.
Solving the local file conundrum
One key constraint of local harnesses is that they work best with local files. For most organizations, this results in 2 key questions:
- Control: How can we control access to confidential information?
- Collaboration: How can we still work collaboratively?
Let’s deal with these one by one.
Users can give Claude access to specific folders, or to their entire root drive – either on their machines or in the cloud.
The second obviously comes with significantly more risk: especially when it comes to confidential information. Claude will work with whatever it has, and as people outsource tasks to agents, the likelihood of errors increases.
Filing is critical when working with Claude Code
At heart, this is a culture and process issue. You need to have a clear protocol for
- Where confidential documents are stored (i.e. secure cloud folders)
- Who and what can access them (i.e. no permissions)
- What the filing protocol is for Claude (i.e. no root folder access)
This needs to be reinforced with training and codes of conduct.
Collaborative working
The majority of organizations are used to working collaboratively in the cloud, on live instances of files going through phases of commentary and iteration.
You can connect Claude via MCP to cloud based file repositories. However this requires Claude to call the data, ingest, pass to harness, and is generally a lengthier, multi-step process requiring multiple calls per action. This has a cost and a failure rate which is higher than a local action.
Equally well if everyone is working independently on local versions of files, version control and collaboration swiftly gets lost.
This tradeoff isn’t great in either direction and requires a workaround.
Why this is the case
Two reasons:
- Tools in their infancy: expect cloud based harness applications to get solved in the future.
- Claude Code started as a dev tool: devs work in Terminal or an IDE by default and have a process for this (branching code, reposting to Github repos), which isn’t applicable to the majority of knowledge workers
Working round the issue
The current best solution is to mirror cloud based files locally, using a tool like Google Drive, One Drive, or Drop Box to keep them synced.
You need to install these as local drives on your machine, rather than simply navigating to them in your browser.
Once you have done this, any edits you make will automatically sync to the cloud, and other people’s local instances of the drive.
This also creates a folder under your machine’s local drive, labelled according to the drive you’ve installed. From here you can manage Claude’s permissions, determining which folders to copy or give access to.
Setting standards
Using centralised files allows organizations to set some rules and standards. For example, as the owners and administrators of folders, you can encode CLAUDE.md or instruction files which contain detailed instructions around company values, user permissions, or company principles.
This is an opportunity to drive consistency and cultural norms across workflows.
Creating shared skills
Skills in Claude are codified backlash commands which detail how to perform certain repetitive tasks.
The workflow is triggered using the command, and then Claude runs through its codified instructions to complete the task. This could include things like the persona to adopt, tone of voice, specific constraints, and so on.
Skills are very powerful. They create attractive opportunities for companies to standardize outputs.
However it’s important to find the balance between standardized skills, and democratized skills.
Skills need to be iterated and improved based on witnessed outputs. By controlling skills centrally, the administrative overhead inevitably increases: maintenance is centralized by default.
Skills are a core component of learning to use Claude Code effectively, and therefore disenfranchising users is limiting.
The automation potential of Claude is almost limitless, and therefore allowing users to innovate and design new skills creates more opportunities.
The optimal solution
What we’ve seen work best:
- 4-8 core skills built and maintained centrally per function: codified outputs for the most common repetitive tasks where standardization makes the organization run quicker. Owned by someone in the team who invests the necessary time
- Personal freedom for other tasks: allowing your team to innovate and design for the other cases they deal with daily
- Feedback loops: shared libraries matched to weekly show and tells, where personal innovation can be surfaced, shared, and used across the team
This creates a good balance between hands-on usage, true adoption and centralization. But there’s no right answer, and this will vary by company and team.
Taking a position on the limits of personal innovation
With great power, can come great innovation. It’s important to decide up front the limits on individual automation.
Typically once a team starts to adopt, some power users will emerge. These people are great, and highly valuable. But they will start to build things you didn’t envisage.
The typical adoption curve looks like this:
- Personal skill library created: users will make specific skills to help with discrete tasks. I.e. an analysis skill
- Agentic routines: users will start to optimize outputs by creating specific sub agents empowered with different skills, chained together to optimize outputs . I.e. a data collection agent, a data structuring agent, a business analyst agent, a senior business analyst review agent, and so on. They will then set these to run on schedules, i.e. to produce the weekly feature adoption sheet
- Building custom interfaces: as Claude Code can also code, the next step often for users is to create their own applications, which produce or display the outputs powered by their agentic routines. I.e. an interactive dashboard
Knowing this is the likely evolution of your team’s workflows, it’s helpful to decide on your limits in advance.
The benefits of this way of working are
- Highly adapted to your work: extreme customization by default
- Maximum effectiveness: better outputs, more outputs
- Compounds over time: every brick in the wall contributes to an effective machine
- Enforces system thinking: users start to understand how various tools and workflows interact and optimize for flow
The downsides are
- Continuity and ownership: it’s more fun to build than to maintain and responsible owners can abandon apps or leave the company
- Security & compliance: are risks introduced by access to tools and APIs? How are these things hosted? Who can view?
- Duplication & standards drift: Multiple people might build very similar things, to different standards
- Optimizing for marginal gains: the 80:20 rule can go out the window as teams strive to build the best possible agentic flows and outputs
There’s no right answer here. It’s highly context dependent, and like everything else, benefits from some upfront thinking and process development.
The key thing to understand is that some of this behavior is likely already endemic within your organization. Claude Code is one way to do some of these things, but other tooling already exists.
It’s best to decide your perspective on
- Permissions and access: where automations can run, and where they can’t, i.e. customer email data
- Scale: identifying big potential problems and mitigating for them
- Process: personal app review committee and IT sign off
- Training: helping teams understand potential risks and self govern
Influencing adoption
This is a big investment, and comes with risk. You can encourage adoption and mitigate risk by
- Preparing onboarding packages: standardising context and Claude’s approach to tasks across the organization
- Investing in upskilling: onboarding the team effectively, but also enforcing your rules of the road
Preparing onboarding packages
Upfront investment in some key documentation will ease your onboarding journey significantly, while helping your team understand how the tool works, and getting some useful outputs early.
Adoption is always a complex journey, but it’s significantly eased by early proof points. This is where a solid onboarding package can help.
In essence, an onboarding package is a collection of markdown files that give every team member a baseline input for Claude that helps improve initial outputs. They don’t all have to be markdown: Claude can also work with pdfs and docs.
Here’s what’s good to include.
Context files
These establish the baseline context for Claude. You can think of this as similar to what a new team member would need to work through to understand the company, their team, and their goals.
Examples of what’s good here include:
- Company level information: details about company offering and sector, competitors, current company strategy, last 4 quarters of OKRs, current goals, and current org chart
- Team level information: team structure, current goals, other items, such as product principles, product architecture, working metrics
Core skills
The 4-8 skills you’ve decided to create and store centrally.
Examples of what’s good here include:
- Core tasks which are done infrequently but have high impact: this might be something like an OKR coach, or a skill which write an exec summary of team activity weekly
- Core tasks which need to be done in the same way across the team routinely: for example OKR reporting, or PRD structuring
This is a great area to brainstorm with your team and crowd source initial drafts.
Master CLAUDE.md file
Claude automatically looks for the markdown file which contains its general instructions. Here you can create a file which contains high level principles you want the tool to adhere to in every interaction across any task, any team. Keep lightweight, but include the key things.
Examples of what’s good here include:
- Core values: principles, such as we seek to do good, but we can take tough feedback and so on
- Encoded ways of working: i.e. we are a document, not a presentation based company, we make decisions in this way, we focus on data over qualitative information and so on
- The index: referencing the context files and core skills that Claude should consult by default
Investing in upskilling
Not everyone in your team will be working from the same level of understanding and individual adoption. You likely have some power users already, but you shouldn’t assume that everyone understands the tool functionality or how it works.
Kick off training / initial workshops
This should be a mix of conceptual training coupled with hands-on training. Use this time to cover:
- Folders and filing: how Claude works with files and best practices
- Prompting: getting initial inputs out of Claude
- MCPs: connecting Claude to other applications
- Skills and commands: codifying different instruction and action types
- Agents and automations: chained routines and triggers
- Coding: using Claude to prototype and build
Demos and ongoing community of practice
These keep momentum going and allow the team to improve over time.
Regular demos where different team members show and tell help embed adoption and make sure things click.
It also gives you eyes on what’s being built and any emerging risks.
Role modelling
You can’t drive a transformation that you’re not part of.
Execs and leaders have to be visibly proficient and use the tools themselves. They should take part in demos and show that they’re part of the process.
Wrap up on rolling out Claude Code to your Team
If you are trying to roll out Claude Code across a team, the main challenge is not installing another AI tool. It is designing a workable system for access, context, governance, training, and adoption.
That is especially true if your team includes product managers, designers, researchers, operators, marketers, or executives, not just software engineers. Claude Code and similar local AI tools can do much more than a browser chatbot, but they also require a different rollout approach.
Preparation goes a long way, as does adopting the tools yourself as a senior leader. By doing so you can understand future risk, how best to train your team, likely outcomes and how the harnesses work most effectively.
Invest up front in thinking through advance decisions, access and security issues, and preparing centralized resources to ensure consistency.
FAQs on Rolling Out Claude Code to your team
What makes Claude Code different from a standard AI chatbot?
Claude Code is part of a broader category of local AI tools that run on your machine and connect AI models to your files, apps, and workflows. Instead of only answering questions in a chat window, it can work with local documents, use connected tools, and follow persistent instructions.
That makes it more useful for real work because it starts with more context. A browser chatbot usually needs you to paste information into every conversation. A local AI setup can already “know” your files, instructions, and connected systems.
In practice, that means better responses, less copy-pasting, and the ability to take actions rather than just generate text.
Who should use Claude Code inside a company?
Claude Code is useful for developers, but applications include non-technical knowledge workers. This includes
* Product managers: creating PRDs, tickets, and analysis
* Designers: prototyping and organizing design systems
* Researchers: pulling together findings and summaries
* CRM and GTM teams: automating repetitive workflows
* Executives and team leads: using it as an operational assistant
What are the most common PM use cases for Claude Code?
Most successful use cases fall into three buckets:
1. Prototyping and lightweight coding: Teams use it to mock up interfaces, generate front-end changes, or build small internal utilities.
2. Reusable workflows: Teams create skills for recurring tasks such as writing tickets, prepping for meetings, drafting follow-up emails, or assembling analysis.
3. Custom internal tools: Some teams go further and build simple interfaces tailored to their own workflows, such as dashboards, task tools, or workflow-specific apps.
The key point is that the tool becomes more valuable when it is embedded in actual work, not treated as a generic chat assistant.
Is there evidence that team-wide adoption creates meaningful ROI?
The exact return will vary by team, but the reported outcomes can be significant.
Examples shared by teams include:
* A front-end refactor completed in 2 weeks instead of 4 months
* A team reducing headcount by about 30 percent through natural attrition while maintaining output
* An analysis task that used to take 3 weeks being completed the same day
That said, measuring ROI is not always straightforward. The value often shows up as faster work, fewer dependencies, higher consistency, and better leverage across the team.
What do you need in place before rolling out Claude Code?
Four basics matter most:
* Budget: for the right account tier. More advanced usage is more token-intensive than casual chatbot use.
* Security and access decisions: so the tool can connect to the systems that matter.
* Shared context: such as product, company, process, and team information.
* Time for training: so people learn how to use it well.
If you skip these, you often end up with scattered experimentation and weak adoption.
Which Claude setup should a team choose?
For most non-technical teams, a desktop-based setup is the simplest and safest starting point. It gives you the main benefits without forcing everyone into a more technical environment.
Some product and engineering teams may prefer tools like Cursor or VS Code because they already work there. That can make sense if the team is comfortable in those environments.
But the bigger advice is this: Do not overthink the harness choice at the start. The market is moving quickly, and flexibility matters more than perfection.
It is also wise to avoid long contracts. Model providers, pricing, and product capabilities are changing fast.
Should your team use local files or cloud files for context?
Local files are usually faster and more reliable for AI work than cloud documents pulled in on demand. The tool can read them directly without repeatedly making API calls to fetch content.
For collaboration, a practical approach is to mirror shared files locally using tools like OneDrive, Dropbox, or Google Drive. This gives you the speed of local files with the convenience of shared storage.
For many knowledge worker teams, this is easier than forcing everyone onto Git-based workflows.
Good shared context files often include:
* Company strategy
* Product documentation
* Team objectives
* Process guides
* Organization context
What are shared skills, and how should you govern them?
A balanced model works best.
Skills are reusable workflows, similar to saved prompts with instructions, triggers, and often tool access. They are powerful because they turn repeatable work into a repeatable system.
Best practice is usually to maintain a small set of central core skills while still letting individuals create personal ones.
A useful rule of thumb is to keep the centrally maintained set small, often around 4 to 8 core skills. That keeps them discoverable and trusted.
Examples of core skills could include:
* Write a Jira ticket
* Prepare meeting briefings
* Draft follow-up emails
* Support quarterly planning or OKRs
If someone creates a high-value personal skill, promote it into the shared library later.
How do you handle security, permissions, and governance?
Usually, the main issue is not brand new risk. It is whether your existing permissions and controls are being applied properly inside the AI setup.
If a person already has access to a system, Claude-connected workflows may make actions faster or easier. That means you should review:
* Read vs write access
* Which tools are connected
* What permissions are allowed by default
* Whether automation could amplify mistakes at scale
For regulated industries, the same core question applies: what data is being sent to the model provider, and under what compliance terms?
Teams in areas like health tech will need tighter review, but the underlying principle is the same.
How do you drive adoption across the team instead of getting random experimentation?
Successful adoption usually depends on three things:
Training: so people understand prompting, context, files, and skills
Regular demos: so team members can see useful workflows in action
Leader role modeling: so AI use is visibly part of how work gets done
Without this, usage often becomes uneven. A few enthusiasts build impressive systems, while everyone else stays at basic chatbot use.
A strong onboarding package can help a lot. Include:
* Shared context files
* Core team skills
* A lightweight index file that tells the system what resources exist
* Clear examples of good workflows
What mistakes do teams make when rolling out Claude Code?
The most common mistakes are:
* Treating it like a simple software rollout: instead of a workflow and behavior change
* No guardrails: Giving access without context files or training
* Over-centralizing everything: and blocking individual experimentation
* Letting complete chaos happen: with no shared standards at all
* Choosing long contracts too early: in a fast-moving market
* Ignoring maintenance and ownership: for internal tools people build
The best teams treat this as an operating model shift. They start simple, create shared foundations, keep flexibility, and invest in practical learning.
Should you track usage and telemetry?
Some teams want telemetry to justify investment, but the most important measures are often workflow outcomes, speed, quality, and dependency reduction. Token usage alone does not tell the full story.
What is the best first step for rollout?
Start with a focused pilot. Give a small team the right accounts, connect key tools, provide a shared context pack, define a few core skills, and schedule demos. Once you see what works, expand from there.
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