Pilots are scattered and never scale
A team tries one tool here, another team tries something else there. A few promising experiments happen, then stall. Nothing turns into a repeatable workflow the whole company can rely on.
Enterprise AI transformation · Consulting and setup
AI transformation for working systems
Most companies have already tried AI. The hard part is not buying a tool, it is turning AI into repeatable workflows, governed adoption and measurable results. We help you map, build, govern and roll out AI workflows, agents and automations across every function, and set up the internal operating model that keeps it running.
1 plan one strategy instead of scattered experiments
weeks to a working, measured first pilot
every function gets a clear, governed use case
ROI measured against a real baseline, not opinion
The real problem
The issue is organizational design, not just technology.
Companies are investing heavily in generative AI, yet most are still immature in how they use it. The tools are not the bottleneck. The bottleneck is structure: who is allowed to do what, with which data, measured how, and approved by whom. Fix that and the technology starts paying back.
Employees use random tools with no rules
People paste company data into whatever they found last week. There is no agreed list of what is allowed, what is risky, and where sensitive information must never go.
Leadership cannot see the ROI
Money and time are going into AI, but nobody can point to a clear before-and-after number. Without a baseline and a measure, every conversation about value turns into opinion.
Data and access are messy
The useful information lives in inboxes, drives, CRMs and spreadsheets nobody has mapped. Permissions are unclear, so either everything is locked down or far too open.
Governance is missing
There are no approval points, no logs, no quality thresholds and no escalation path. That is fine until the first mistake, and then it becomes the only thing anyone talks about.
Teams are unsure where AI is even allowed
People hold back because the rules are vague, or they go too far because nobody said no. The result is uneven adoption and quiet anxiety instead of confident use.
What RandomForest sets up
A complete operating capability, not a single tool.
We help you set up the whole picture: strategy, a scored backlog, governance, working pilots, the agents and automations behind them, the permissions that keep them safe, the training that drives adoption and the reporting that proves value.
AI readiness audit and strategy
We map your goals, processes, data and access, then turn that into a clear strategy: where AI pays back first, what to leave alone, and what the next few quarters should look like.
Use-case backlog scored by value, risk and feasibility
Instead of a vague wishlist, you get a ranked backlog. Every idea is scored on business value, risk and how realistic it is to build, so the order of work is obvious.
Governance and usage policy
A short, practical policy your team will actually read: what is allowed, what needs approval, where sensitive data goes, and who to ask when something is unclear.
Pilot design and implementation
We pick one or two pilots with real value and low disruption, define the baseline, and build the working version. You see results, not slides.
Agent and workflow setup with your context and tools
Agents and automations are connected to your real systems and grounded in your own documents, processes and tone, so the output fits your company rather than a generic template.
Permissions, approvals and human-in-the-loop checks
Clear security boundaries, role-based access, logs and approval steps. The system knows what it can do alone and what waits for a person.
Training for leaders, managers and power users
Different roles need different things. Leaders need judgment and oversight, managers need to run adoption, power users need hands-on skill. We train each group for its job.
ROI dashboards and a continuous improvement cadence
Adoption reporting, value tracking and a regular review rhythm, so the work keeps improving instead of fading after the launch buzz.
The three-phase process
Lay the foundation, launch a pilot, scale impact.
The same approach that works for any serious operating change: get the ground right first, prove value on something small and real, then turn the wins into capability the whole company can use.
Phase 01
Lay the foundation
Before building anything, we get the company aligned and the ground prepared. Most failed AI efforts skip this and pay for it later.
- Clarify the business goals AI should serve, in plain terms.
- Map the key processes and where time and effort actually go.
- Audit data and access: what exists, where it lives, who can see it.
- Build a stakeholder map and name a steering group.
- Set up a champion network across functions to carry adoption.
- Agree practical governance rules and acceptable use.
- Define the success metrics that will prove or disprove value.
- Produce a shortlist of pilots worth starting.
Phase 02
Launch a pilot
We choose one or two clear pilots with real ROI and low business disruption, then build something that works and that people can learn from.
- Choose a low-risk, high-value workflow as the first pilot.
- Define the baseline so improvement can be measured honestly.
- Build a working prototype, not a demo that only runs in a meeting.
- Connect systems where it helps, and start with manual handoffs where full integration would be too heavy.
- Train the people who will use it day to day.
- Collect usage, quality and satisfaction metrics from the start.
- Run short weekly reviews to fix what is not working.
- Close with a post-mortem that captures what to keep and what to drop.
Phase 03
Scale impact
A pilot that works is the start, not the finish. We turn proven wins into reusable capability the whole organization can use.
- Turn winning pilots into reusable playbooks other teams can follow.
- Stand up an AI center of excellence or a small AI operating team.
- Run role-based training so each group learns what its work needs.
- Hold internal showcases so progress is visible and contagious.
- Report ROI and adoption so leadership stays informed and bought in.
- Build feedback loops that feed improvements back into the workflows.
- Keep a living roadmap for the next set of workflows to tackle.
Where it pays back
Example use cases, function by function.
AI transformation is not one big project. It is a series of focused workflows across the business, each with a clear owner and a measurable result. These are the kinds of use cases we help companies set up.
Sales
Proposal and email drafts, CRM enrichment from scattered notes, and prioritization of which follow-ups matter most this week.
Marketing
Content production workflows, campaign analysis, and brand-safe drafting that stays on message and on tone.
Customer support
An internal knowledge assistant, ticket triage, response drafts and clear escalation rules for anything sensitive.
Operations
SOP automation, document processing and recurring reports that no longer eat someone’s Friday afternoon.
Finance
Reporting automation, forecasting support and routine checks on invoices and categories, with a person on the final call.
HR
Smoother onboarding, an internal helpdesk for everyday questions, and policy Q&A that points to the right document.
Legal and compliance
Contract summaries, clause checks and review queues that speed up the reading, while human approval is always retained.
E-commerce and website teams
Product and content updates, catalog intelligence and clearer insight into what customers are actually trying to do.
Governance and safety
Practical guardrails, not compliance theatre.
Governance is what lets people use AI with confidence instead of quiet worry. We keep it practical: clear boundaries, sensible approvals and honest logs, so the system knows what it can do alone and what waits for a person.
Role-based access so people and agents only reach what they should.
Clear data boundaries: where sensitive information may and may not go.
Acceptable-use rules written in plain language, not legal filler.
Quality thresholds so weak output is caught before it ships.
Approval workflows for anything with money, legal or customer impact.
Audit logs that record what happened, when and why.
GDPR-aware workflows that respect how personal data is handled.
A defined escalation path for when something looks wrong.
What you walk away with
Deliverables you can use the next day.
Every engagement leaves you with concrete things: a plan, a backlog, a working pilot, the rules around it, trained people and a way to measure what happens next.
AI transformation roadmap
A clear plan for where AI fits, in what order, and what each step should achieve.
Prioritized use-case backlog
Every opportunity scored by value, risk and feasibility, ready to work through.
Pilot specification and a working pilot
A defined first pilot and the live version of it, measured against a baseline.
Governance kit and policy templates
Usage policy, approval rules and boundaries your team can adopt right away.
Training sessions and internal docs
Role-based training for leaders, managers and power users, plus the docs to keep it alive.
Adoption and ROI dashboard
A simple view of usage, quality and value, so progress stays honest and visible.
Rollout plan and operating cadence
How the work scales beyond the pilot, and the review rhythm that keeps it improving.
Where this connects
One transformation, several moving parts.
AI transformation pulls in work we already do every day. Once the strategy and governance are set, the individual workflows often look like a website AI team keeping content and translations fresh, an AI team for an online store, stronger AEO and AI visibility so your brand shows up in AI answers, or the wider agentic company operating model for businesses that want to run teams of AI employees safely. The transformation program is the layer that ties them together.
Questions
AI transformation consulting, answered.
What is AI transformation consulting?
It is the work of turning scattered AI experiments into a working operating capability. Rather than buying a tool and hoping, you get a strategy, a prioritized backlog, governance, a measured pilot and a plan to scale what works across the company. The focus is on repeatable workflows and adoption, not one-off demos.
How long does an AI pilot take?
A focused pilot runs in weeks, not quarters. We deliberately pick something with real value and low disruption so you can see a measurable result quickly, learn from it, and decide what to scale next.
Do we need clean data before starting?
No. Most companies start with messy data and unclear access, and that is normal. We map what you have, choose a first pilot that does not depend on a giant cleanup, and use manual handoffs where a full integration would be too heavy at the start.
Can AI work with our existing systems?
Yes. We connect to the tools you already use where it helps, and where integration would be expensive or slow we begin with lightweight handoffs and tighten them later. The goal is value early, not a year of plumbing first.
How do we keep sensitive data safe?
With practical guardrails: role-based access, clear data boundaries, approval steps for risky actions, audit logs and GDPR-aware workflows. The system is designed so it knows what it can do on its own and what must wait for a person.
Who should be involved internally?
A small steering group from leadership, a champion or two in each function, and the people who will actually use the pilot day to day. You do not need a big committee. You need a few engaged people and clear decisions.
What happens after the first pilot?
We turn what worked into a reusable playbook, set up a small AI operating team or center of excellence, run role-based training, and build the reporting and roadmap that carry AI into the next set of workflows.
Start here
Book an AI transformation audit. We will show you where to start.
Tell us a little about your company and where AI feels scattered today. We will take a short look at your goals, processes and the tools you already use, and come back with a clear first pilot and the foundation it needs. No buzzword soup, just the next practical step.