Everything we have found so far, from public research and from speaking to the vendors ourselves. This is written for us, not for a client. Where a number comes from a vendor, it says so.
This is the finding that reframes every conversation. It is not a reason to stay out of the market. It is the reason clients need someone who has read the post mortems.
Sources: Gartner (analyst), Sinch 2026 (vendor), 8x8 (vendor, trend corroborated elsewhere).
We worked these out so we can say no early, which is cheaper for everyone. All three have to be open.
At least three people effectively on the phone. That is roughly 5,000 contacts a month at four minutes a call.
One named person at the client who owns the knowledge base and the error queue after go live.
Three to five experienced agents, released for interviews and shadowing. Without them the knowledge base is second hand.
Missing data is not a gate. If a client cannot name their top ten call reasons, that is a paid contact analysis first, then the agent.
The running costs are never the problem. A human call costs about £6.26, the same call handled by AI about £0.35, a factor of fifteen. The problem is the fixed cost that does not scale: £35k to £150k to build, plus half to one full time owner forever, plus an annual minimum of around $150k on a managed platform.
| Contacts a month | Net FTE freed | AI cost a year | Net a year | Payback at £60k build |
|---|---|---|---|---|
| 2,000 | +0.1 | £8,400 | minus £5,400 | never |
| 5,000 | +1.0 | £21,000 | £10,600 | 5.7 years |
| 12,000 | +2.9 | £50,400 | £47,900 | 1.3 years |
| 30,000 | +7.9 | £126,000 | £127,700 | 0.5 years |
| 5,000 | +2.5 | £21,000 | £58,200 | 1.0 year (at 8 minutes) |
Our own model, 9 August 2026. Note the AI cost applies to all contacts, including the ones that escalate to a human.
So 5,000 is the floor where the sum stops being negative, not the point where it becomes attractive. At the usual four minute call it only gets comfortable at 10,000 to 15,000 contacts. Below that, either the calls have to be long, or the reason has to be something other than saving money: opening a channel, covering nights and weekends, or stopping complaints about not being able to reach anyone.
Sorted by how much weight they will carry in a room. The dashed tags are the ones to quote carefully or not at all.
| Measure | Value | Source | Weight |
|---|---|---|---|
| Cost per call, UK | £6.26 | ContactBabel | Independent |
| Same call handled by AI | about £0.35 | derived | Independent |
| Realistic containment | 45 to 65% | IrisAgent | Best estimate |
| Containment as sold | 60 to 90% | vendor decks | Marketing |
| True cost of AI telephony | $0.11 to $0.30 a minute | several | Independent |
| Self built stack | $0.035 to $0.122 | component pricing | Independent |
| Managed enterprise platform | from $150k a year | third party | Estimate |
| Initial build | $35k to $150k | several | Estimate |
| Integration share of effort | 40 to 60% | several | Estimate |
| Build versus buy break even | 40k to 60k calls a month | our model | Our model |
| Agent assist, cases solved per hour | +14%, +34% for new staff | Brynjolfsson et al., QJE | Peer reviewed |
| Return on investment | 391% | Forrester TEI | Vendor funded |
Two things to carry out of that table. First, the only peer reviewed effect in this whole field is for agent assist, meaning AI helping a human, not replacing one. For a nervous client that is the right first project. Second, headline per minute prices of $0.05 to $0.07 only cover orchestration. They do not include the model, speech to text, speech synthesis or the line.
There is no independent payback benchmark anywhere. That is the biggest hole in the market, and it is also our most honest line: we build the business case from the client's own numbers, not from an industry average.
Infrastructure (models and speech), toolkit (build it yourself), managed (they run it), and the suite you may already own. The average score below is the least useful column. Cognigy leads and is still wrong for a small unregulated client. Always read the column that matches the constraint the client actually has.
| Vendor | Layer | Score | Strongest argument |
|---|---|---|---|
| Cognigy / NiCE | Managed | 4.3 | The only real on premise option. ERGO as a named insurer |
| Parloa | Managed | 3.8 | Swiss Life, EU data, real telephony audio work |
| PolyAI | Managed | 3.7 | UK company, voice first, simplest residency answer |
| Observe.AI | Assist | 3.7 | 100 per cent QA instead of sampling |
| ElevenLabs | Infrastructure | 3.7 | Best voice quality, published prices, EU residency since 2026 |
| Speechmatics | Infrastructure | 3.7 | On premise speech recognition, UK |
| Cresta | Assist | 3.2 | Live coaching, lowest risk way in |
| Synthflow | Toolkit | 3.2 | Best certifications in the no code segment |
| Sierra | Managed | 3.0 | Outcome pricing, but $50k to $200k just to set up |
| Decagon | Managed | 2.8 | Fast time to value, no regulated track record on paper |
| Regal | Managed | 2.8 | Outbound, but built around US law |
| Vapi / Retell / Bland | Toolkit | 2.7 | Fast and cheap, compliance too thin for regulated work |
Desk research gives you the brochure. The calls give you the shape of the deal. We are working through the shortlist and writing down what each one says, so the comparison is ours rather than theirs.
Still to do: Sierra, ElevenLabs and Vapi, so the list is complete. Cognigy and Parloa came to us cold, which is worth remembering when we need a fast conversation.
There is no separate AI law in the UK. The existing FCA rules apply to an AI conversation exactly as they apply to a human one, which is stricter than most people assume.
| Rule | What it forces into the design |
|---|---|
| Consumer Duty since July 2023 | Four outcomes have to be evidenced. A named senior manager is personally liable under SMCR. Handing a decision to an algorithm does not hand over the liability. |
| FG21/1 vulnerable customers | The agent has to spot vulnerability in voice, wording, confusion or stress, and hand over to a person at a low threshold. |
| DISP complaints | The agent has to recognise when something is a complaint, including implied dissatisfaction, and route it properly. Getting that wrong is a regulatory failure, not a service failure. |
| Art. 22A to 22D UK GDPR new since 5 Feb 2026 | Automated decisions are now permitted in principle rather than prohibited, but anything with an adverse effect needs information, representation, human review and the right to object. Underwriting is named as exposed. |
| SYSC 8 outsourcing | Due diligence, audit rights, ongoing monitoring, and notification if the service counts as critical or important. This is our clearest hook: we build that vendor governance layer. |
The FCA is friendly to AI as long as governance can be evidenced. The AI Lab, AI Live Testing and the Supercharged Sandbox all exist. Our line is simple: auditable from day one.
Section 87(1) no. 6 of the German Works Constitution Act bites on the abstract suitability of a system to monitor performance, not on whether you actually use it that way. The Federal Labour Court confirmed this on 8 March 2022. Call logging and QA scoring fall under it. Without works council agreement, the rollout is void, and there is a prohibition on using what you gathered. This belongs in the plan as its own phase, before the contract, not as a footnote.
This summary comes from web research, not from reading primary law. Have it checked before it goes to a client.
Vendor choice, use case prioritisation, first working agent. Two to three weeks.
Three to six weeks, overlapping. Transcript mining across a full seasonal cycle, two to three days shadowing the most experienced people, structured extraction interviews with the top performers, and a review of whatever documentation exists. The gap between the written process and the lived one is itself a finding.
From go live. Five per cent of conversations sampled weekly, plus 100 per cent of anything with a negative signal. A fault taxonomy, 30 to 50 golden conversations as regression tests, and a dashboard the client can see. Errors cluster at the edges, so random sampling alone is not enough.
Phase two is the whole argument. It is the part vendors deliver worst, because it is not a software problem. The knowledge usually does not exist in writing at all: it lives in the heads of the people who have been answering the phone for six years. Nobody sells a product that fixes that.
The risk to settle before signing anything: if the client will not release the time of their best agents, the knowledge base gets built second hand and stays shallow. That is worth losing a deal over, because it is how the project fails later.
PolyAI and Decagon this week, then Sierra, ElevenLabs and Vapi. After that we have something no vendor can hand a client: a comparison written by someone with nothing to sell on the platform layer.