Consultant

For a process to run autonomously, four things have to line up: the technology, the process itself, the data behind it, and the people who work with it. That alignment does not happen at go-live. It happens in the months after, and that is where this role sits.

Our customers go live with a working solution. What they do not have on day one is an organisation that operates in an AI-native way. Processes were designed around people checking things. Data was maintained well enough for humans to interpret. Teams know the old workarounds better than the new system. Your job is to lead that transition, from a process that is automated in parts to a process that genuinely runs on its own.

That means working across every level of the customer's organisation. With operational teams you build understanding and find where automation is actually blocked. With department leads you challenge the process rules that make full automation impossible, and drive the decisions that change them. And you use the right lever for each blocker, because they are all different: some are a data clean-up you coordinate with the customer, some are a training and adoption gap, some need a custom agent, and some are structural and belong with our engineering teams.

You do not do this alone. You enable the customer's core team to solve their own issues, you work with our Customer Success Engineers on the technical side, and you use the Customer Success Manager to get decisions made where you need commercial weight. For our smaller customers, where there is no Customer Success Manager, you are the escalation point yourself.

You own customer health. Your sharpest measure of it is the automation rate.

• Own customer health across your portfolio, measured first and foremost by automation rates.
• Lead the transition to AI-native ways of working: identify what blocks automation, and pick the right way to solve each blocker.
• Work with operational teams on enablement and adoption, so people work efficiently with blp rather than around it.
• Challenge process rules that prevent automation, and drive improvements with the department leads who can decide.
• Coordinate data clean-up with the customer where master data quality is capping what automation can reach.
• Enable the customer's core team to resolve issues themselves, and reduce what has to come to us at all.
• Identify where a custom agent solves the problem, and where the issue is structural and belongs with our engineering teams.
• Collaborate with Customer Success Engineers on technical topics, and with the Customer Success Manager to get decisions made.
• For customers without a Customer Success Manager, act as the escalation point for SLA breaches and own continuity of operations.
• Direct the AI Automation Specialists working on your accounts.