Energy payments & collections PM
Here's what AI can do for this role — and what still needs a human. Built straight from ENSEK's own job advert, running live on my_db.ensek_demo.payment_events — 27,640,145 real rows via MotherDuck (DuckDB). Not a slide about AI. The job, getting done.
Every line on the left is lifted from ENSEK's actual job ad. If a card lacks a harvested JD line, it is omitted. On the right is the AI doing it — with eligible cards running live against the warehouse and offline inspection clearly labelled in the workspace.
“Own the strategy, vision and roadmap for payment and refund processing — engaging users, stakeholders and commercial partners to define, validate and iterate it continuously, using competitive intelligence and AI tools to inform positioning.”
Show DD collection status distribution from live payment events — collected, pending and returned BACS.
bar chart“Drive internationalisation of payment capabilities, ensuring the platform supports compliant payment processing across each regulatory jurisdiction without bespoke builds.”
Payment-method mix and failure rate by customer segment — where are direct debits failing hardest?
bar chart“Define and track key product outcomes — implementing dashboards for real-time performance visibility, running A/B and multivariate test and learn, and using data to drive continuous improvement in quality and user experience.”
What is the arrears-entry rate among DD accounts — what share of mandates have entered arrears, by segment?
bar chart“Create and maintain a prioritised roadmap, working with your team to validate what adds value now and in future, drafting PRDs and specifications.”
Show the open dunning-stage backlog aged against SLA — which steps are overdue, by type?
deviation“Build long-term stakeholder relationships, implementing communications strategies and influencing effectively to drive alignment and outcomes.”
Which active accounts carry a credit balance — the refund and credit-balance exposure book?
deviation“Own the strategy, vision and roadmap for payment and refund processing — engaging users, stakeholders and commercial partners to define, validate and iterate it continuously, using competitive intelligence and AI tools to inform positioning.”
Give me the payments board pack by period — DD collection, retry recovery, total failures and cash in.
kpiThe honest other half. AI does the analysis; a person owns the decision — especially where regulation, fairness and accountability bite.
A plain-English question — the same one the job ad describes — is translated to SQL by the agentic backend.
Curated cards run server-side against MotherDuck when eligible. The workspace separately labels any local inspection path.
Runs against my_db.ensek_demo.payment_events (27,640,145 rows declared by the manifest). No synthetic numbers.
Each figure carries a falsifier — recomputed from the result set, not a stored number, so it can't quietly drift.
It's the role getting done: curated questions run live server-side against the warehouse; local inspection is labelled inside the workspace.
Open the live workspace →Provenance. Mixed proof: live MotherDuck payment_events for DD collection status, plus synthetic ENSEK-style payments dataset extending pm-billing for retry, arrears, dunning and credit-balance workflow cards (seed 20260608/20260609). Synthetic cards are reproducible and labelled; no real ENSEK customer data. Live server-side path for supported card: my_db.ensek_demo.payment_events.
It's Sorted — I took ENSEK's job ads and didn't write a report on what AI could do. I built it. Get the rest sorted →
I'm trained on this proof and the real ENSEK: the Ignition meter-to-cash platform (seven modules), the move under Centrica in 2024, 7M+ energy accounts migrated for suppliers like British Gas and Utility Warehouse, and the Ofgem framing. Ask me how the Data Analyst function changes shape, or which open roles map to which Ignition module.