AI readiness: find out where AI could take the repetitive work off your team.
Done well, AI takes the repetitive work off your team — the chasing, the copying between systems, the checks people miss on a busy day. Ignyto’s AI readiness assessment tells you in writing which of those tasks an AI agent could take on now, what has to be fixed first, and what it would cost. Fixed scope, no obligation to build with us.
Are you on Salesforce today? You don’t need to be.
It takes ten seconds to say, and it tells us how to read your answers. Either way you get the same seven questions and the same score.
What happens next, depending on your score.
Ready to build
Your data would carry a grounded AI agent. The next step is proving it on one narrow, measurable use case, in whichever tool fits the job — then putting it where your team already works.
Nearly there
One or two foundations to close first, and they are quick wins more often than not — usually connecting the systems either side of your CRM. The full AI readiness assessment puts the steps in writing.
Foundations first
Common, and very fixable. Simple process changes plus data quality and unification work move this score quickly, and the gains show up in reporting long before AI goes on top.
Wherever you land today, the score moves. Every step is work you would want done anyway: records you can report on, systems connected so data is entered once, a sharing model that matches how the business actually operates, and processes written down. We connect the systems either side of Salesforce and unify the data so an AI agent reads one version of the truth. Whether AI then belongs on top of it, and in which tool, is what the assessment decides. Ignyto has delivered over 1,000 Salesforce projects since 2016 from London, Manchester and Dublin. See what that looked like in practice across our Salesforce case studies.
The demo always works. Your business is not the demo.
You have probably sat through an AI demo. It was impressive, and it was running on a clean environment built to make it look that way. The gap between that and your environment is not a technology gap — it is duplicate accounts, fields half the team stopped filling in, and knowledge articles nobody has revisited since 2022.
Grounded in that, an AI agent does not fail politely. It answers confidently and incorrectly, in front of a customer. Which is why the first question is not which AI to buy, but what your data could honestly support — and whether AI is the right answer to the problem at all.
The other half is governance. An agent can reach whatever the user it acts for can reach, so your sharing model stops being an administrative detail and becomes the thing that decides whether this is safe to switch on.
What the assessment gives you.
Data and grounding review
What an agent could truthfully answer from your Salesforce today — object model, knowledge, entitlements, sharing — and where the architecture needs work before anything is switched on.
Ranked use case shortlist
Scored on volume, risk and how measurable the saving is. Ignyto usually recommends starting narrower than clients expect, because a small agent that works beats a broad one that does not.
Guardrails and governance
Topics, actions and escalation paths. What the agent must hand to a human, what it may never say or do, and how that is evidenced for an auditor.
Costed build plan
User stories with effort against each, a licence view, and a phased sequence you can stop between. Yours to keep either way.
Most people do not start where they expected to.
Most organisations that go through this do not start where they expected to. Service deflection and internal productivity tend to come out ahead of the customer-facing use case that prompted the conversation, because the data supports them sooner.
Where readiness gaps are found, they are almost always the same work you would want done anyway: data quality, a sharing model that reflects how the business actually operates, and processes written down rather than remembered. That is the honest part — AI readiness work pays for itself even if you never switch an agent on.
What we recommend at the end of it depends entirely on what the assessment finds. Sometimes that is an AI agent grounded in your own records, in regulated industries where the audit trail has to hold — the agent on this website is one of those. Often it is simpler automation, better integration or a process change, because that is what the evidence points to. Ignyto is a Salesforce Summit partner and an Anthropic Claude Registered Partner, which means we can build on either and have no reason to talk you into the wrong one. Where the work suits it, that is Claude AI consulting; where it belongs inside your CRM, it is Agentforce.
Questions we get asked first.
How do we know if we are ready for AI?
Readiness is not about licences. It comes down to whether an AI agent could truthfully answer from your data today, whether your sharing model still holds when an agent can reach everything a user can, whether the process you want automated is actually defined, and whether there is a specific outcome to aim at. Ignyto assesses all four and tells you which are blocking.
How long does an AI readiness assessment take?
Fixed scope, and possible in as little as two weeks, depending on complexity and your team’s availability. You get a data and grounding review, a ranked use case shortlist, a guardrail and governance model, and a costed build plan with effort against each user story. The plan is yours to keep whether or not you build with Ignyto.
Why do most AI projects stall?
Grounding, not technology. An AI agent in a clean demo environment answers beautifully. In a real system with duplicate accounts, half-filled fields and knowledge articles nobody has revisited in years, it answers confidently and wrongly, which is worse than not answering at all.
Do we need Data Cloud for Agentforce?
For most grounded use cases, yes. Data Cloud is how content and records are indexed and retrieved. Part of the assessment is establishing what you already hold, what needs restructuring, and what that means for licence cost before you commit.
Is our data secure if we use AI in Salesforce?
Agentforce runs inside your own Salesforce, so your sharing model, field-level security and audit trail apply to every response an agent gives. The Einstein Trust Layer means prompts are not retained by the model provider or used to train third-party models. Ignyto covers this in the governance stage of the assessment.
Ready to find out where you actually stand?
Half an hour with an Ignyto AI consultant, on your own systems rather than in the abstract. If it is worth going further, that leads into the full assessment. Either way you will know more than you do now.