AI consultancy or managed IT provider: who should implement AI in your business?
AI consultancy or managed IT provider - which should implement AI?
An AI consultancy brings model expertise and strategy but usually hands over at go-live and does not hold your systems. A managed IT provider holds the identity, data and security estate that AI depends on, and stays afterwards. For most mid-market businesses the deciding factor is who runs the environment once the project ends.
What each one is good at
A specialist AI consultancy typically brings deep model and tooling knowledge, experience of comparable use cases across industries, and the ability to build something bespoke. If you need a custom model trained on proprietary data, or a novel product feature, this is the right call.
A managed IT provider already administers your Microsoft 365 or Google Workspace tenancy, your identity and access management, your endpoints and your security controls. Every one of those is a dependency for an AI rollout. The provider also stays after deployment, which matters more than it sounds.
The handover problem
Most AI projects do not fail during the build. They fail six weeks after it.
A consultancy delivers a working system and leaves. Then a permission changes, a data source moves, a licence lapses, or someone asks why the output looks different this month. The people who built it are gone, and the people who run your IT were not involved.
This is why the question is less “who can build this?” and more “who will still be here when it breaks?”
Where the real dependencies sit
An AI deployment in a typical mid-market business touches:
Identity and permissions - tools like Copilot surface whatever a user can already access. Loose permissions become an incident.
Data governance - retention, classification, and where information is allowed to travel.
Licensing - which users are eligible, and what each licence actually includes.
Endpoint and update posture - devices on unsupported channels do not behave predictably.
Security monitoring - new tools mean new logs, and logs nobody reads are not monitoring.
Change management - adoption is a people problem long before it is a technical one.
All six sit with whoever manages your IT. An AI consultancy working around them adds co-ordination cost at every step.
A straightforward way to decide
Choose an AI consultancy when:
You need a bespoke or novel model rather than applied use of existing tools.
The work is a product initiative rather than an internal one.
You have an internal IT team that can own the result afterwards.
Choose a managed IT provider when:
The use cases are applied - automation, Copilot, agents, workflow.
Your data and permissions need work before anything is deployed.
You have no internal team to inherit the system.
Security and compliance obligations sit alongside the project.
Choose both when the build is genuinely specialist but the environment is not ready. In that case, sequence it: the provider prepares the foundations, the consultancy builds on top of them, the provider runs it afterwards.
What to ask either of them
Who owns this system in month six, and what does that cost?
What do you need from our identity and permissions model before you start?
How will you show the result is correct, not just plausible?
What happens to the work if the person who built it leaves your firm?
Which parts of this can our existing tooling do already?
That last question is worth asking early. A meaningful share of AI value in mid-market businesses comes from features already included in software you pay for.
How NVOY approaches it
NVOY holds all four disciplines under one team - security, IT operations, data platforms and AI. That means the readiness work, the deployment and the ongoing operation sit with the same people, rather than being split across three vendors and a co-ordination problem.
We are ISO/IEC 27001:2022 certified for the provision of IT managed services, professional services and supply of IT equipment - certificate 21497ISMS001, issued by Alcumus ISOQAR under UKAS accreditation, and independently verifiable.
Start with our Data Platforms work, or read why a Copilot readiness assessment comes first.
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