// AI implementation for business
We bring AI into your processes to help you work more efficiently every day
ezacae helps SMEs and mid-sized companies implement AI in their processes to automate, analyse and simplify everyday tasks. We start from your needs to identify the relevant use cases, without putting AI everywhere just because it's trendy.
Let's talk// what are we talking about, exactly
AI implementation: scoping before building.
Building an AI agent or an automation is a development project: you already know what you want to automate, you write the workflow, you put it into production. AI implementation sits upstream. It's the process that lets you know what to deploy, how, with which tool, and under what security conditions, before you even write the first workflow.
In practice, this means choosing the right AI tools for your teams (Copilot, ChatGPT Enterprise, Claude, or a model we host based on your constraints), defining a data governance and security framework, prioritising the use cases with real business impact, and supporting teams so they actually adopt these tools day to day rather than leaving them on the shelf.
Already know precisely which process to automate or which agent to build ? AI implementation is aimed more at those who still need to scope their strategy before moving to build.
// who it's for
An executive team that has approved an AI budget, but doesn't yet have a clear rollout plan An IT department that needs to get ahead of AI usage (Shadow IT, data security, compliance) before it spreads unchecked A company that already has an isolated AI pilot in one department, and wants to scale it without starting from zero An organisation that wants to prioritise its AI use cases by real business impact, not by trend
// our approach
We scope before we deploy, we deploy before we scale.
1. Audit & prioritisation
We start with an audit of existing AI usage — usually more widespread and more chaotic than expected — then prioritise the use cases with the highest impact for your business.
2. Tool selection
We choose the tools with you, without dogma or forced licensing: Copilot, ChatGPT Enterprise, Claude, or a model we host and control ourselves if your security constraints require it.
3. Governance & security
We set a clear framework: which data can flow through which tool, with what access, within what limits. A serious scoping process avoids unmanaged Shadow IT without falling into a blanket ban that teams work around anyway.
4. Rollout & adoption
We roll out in waves, measuring actual adoption rather than the number of licences handed out. If upskilling requires structured training, we draw on our AI training offering.
// frequently asked questions
What's the difference between AI implementation and an AI agents or automation project?
AI implementation is the scoping and rollout process that comes before or alongside a concrete project: it defines which AI use cases make sense for you, with which tool, and under what security conditions. An AI agent or automation project starts from an already identified need and builds the corresponding technical solution — that's what we do through our AI agents and automation agency offering. The two are often combined: an AI implementation scoping exercise regularly leads to one or more concrete automation projects.
Should you impose a single AI tool across the whole company?
Not necessarily. Some teams need a general-purpose assistant like Copilot or ChatGPT Enterprise, others need a more specialised tool or a model we host ourselves for confidentiality reasons. We define the right combination with you rather than imposing a single standard by default.
How do you secure data sent to an AI tool?
It depends on the tool and the use case: data processing agreement, model hosting, the actual access level needed for each use case. A serious scoping process defines upfront which data can flow through which tool, before any large-scale rollout.
How long does it take to roll out AI across an organisation?
An initial scoping phase (usage audit, prioritisation, tool selection) usually takes a few weeks. Rollout then happens in waves, team by team, with checkpoints to measure actual adoption. We prefer a gradual, controlled rollout over a big bang that creates more friction than usage.
// our other expertise