Council Contact Centre AI Saving Calculator

Try the savings calculator

Adjust the inputs to match your councils contact centre and see how AI deflection could reshape your run-rate. Defults are based on published UK council benchmarks. 

Your assumptions
Contact volume
Annual contact centre cases All channels
cases / year
Cost of human contact
Average advisor salary Excluding on-costs
£ per year
On-cost burden LGPS, NI, overheads
%
Average handle time Talk + admin
minutes
AI deflection
Deflection rate Of total cases
35% 10% — 60%
Cost per AI conversation Agentforce typical
£ per case
Your modelled savings
Annual gross savings
£161,742
19.3% reduction in cost-to-serve
Three-year cumulative
£485,227
FTE capacity unlocked
8.8 advisors
Cost per human contact
£3.35
Cases AI handles
87,500
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The defaults aren't guesswork

The starting numbers in this calculator come from published UK council contact centre benchmarks. If you want to challenge them, you should — and you can, that’s why every input is editable. Here’s where the defaults come from.

Hillingdon Council

40

%+

Calls handled by conversational AI. Freed 25-30 FTE capacity within a three-month rollout. 

City of Edinburgh

41

%

Inbound calls processed without human intervention.

13.4 FTE equivalent. 

Mid Sussex DC

£51k

Efficiency gain from a 20,000-call reduction in annual volumes through digital channel shift.

Oxford City Council

c.

262k

Annual Contact centre call volume across waste housing, conical, tax, planning and benefits. 

Frequently asked questions

Got a question that we’ve not covered or simply want to learn more? 

What is a realistic AI deflection rate for a UK council contact centre?

Published case studies from UK councils suggest 35–45% is achievable on routine enquiries.

Hillingdon Council reported handling well over 40% of calls through conversational AI. The City of Edinburgh Council’s self-service telephony processed 41% of inbound calls without human intervention.

The right rate for any council depends on the mix of enquiry types, the quality of underlying service data, and the maturity of the implementation.

Cost per contact is fully burdened agent cost divided by productive minutes per year, multiplied by average handle time.

The calculator uses: (annual salary × 1.30 burden multiplier) ÷ (working days × productive hours × 60) × handle time in minutes. The 30% burden covers pension (LGPS contributions are typically 18–25% for councils), National Insurance, and shared overheads.

Agentforce is typically priced per conversation, not contact in enterprise agreements, with rates around £1.50 per conversation in 2026 enterprise pricing.

Actual pricing depends on volume commitments, the model used, and what’s bundled into a wider Salesforce agreement. The calculator uses £1.50 as a reasonable working assumption.

No. The calculator shows steady-state operational saving only. Real implemenatations involve discover, integration with line-of-business systems, data preparation,training and change management. Most councils should plan for these seperate one-off investments and ongoing licensing costs, alongside the recurring savings the calculator esitmates. 

The realistic outcome is redeployment, not replacement. AI handles routine, high-volume enquiries (bin collection days, council tax balance queries, missed bin reports). Human advisors are freed to handle complex, sensitive, or vulnerable resident contacts where empathy and judgement matter. Most councils that have deployed contact centre AI use the freed capacity to absorb growing demand or improve service rather than reduce headcount.

Start with the calculator’s gross savings figure, subtract a realistic one-off implementation cost (we can help you estimate this), and model payback over a three-year horizon. The strongest cases pair the financial savings with non-financial outcomes: improved access for residents, reduced wait times, capacity to absorb growing demand without headcount growth, and freed advisor time for complex cases.

CFOs and Section 151 officers respond well to honest assumptions and conservative modelling.