Why Does Automatic Speech Recognition Matter So Much to Your Operation Right Now?
Customer conversations contain valuable insights, but much of that information remains locked away in call recordings that are difficult to search or analyse. Automatic speech recognition (ASR) converts spoken audio into text, making those conversations easier to access and put to use. It also provides the foundation for many voice-driven AI tools, giving them the text they need to interpret requests, identify patterns and generate responses.
Yet most of these conversations never receive detailed analysis. McKinsey found that traditional manual call sampling captures less than 2% of customer interactions, leaving the vast majority of customer feedback unexplored. Automatic Speech Recognition helps bridge this gap by turning recordings into searchable transcripts that contact centres can analyse at scale, making it easier to uncover recurring issues, flag potential compliance risks and understand the reasons behind customer dissatisfaction.
The need to improve service operations is becoming more pressing. A Gartner survey of 321 customer service and support leaders found that 91% faced pressure from executive leadership to implement AI in 2026. At the same time, contact centres face significant operating costs. Gartner estimated in 2022 that labour could account for up to 95% of contact centre costs, across approximately 17 million agents worldwide. Yet investment alone has not guaranteed better outcomes. Deloitte Digital's 2026 Global Contact Center Survey found that fewer than one in five leaders considered their current strategies fully effective, while more than half of surveyed consumers said service quality had stayed the same or worsened in 2025.
ASR is not a solution to every contact centre challenge, but it provides a foundation for addressing many of them. Before conversations can be analysed at scale or used to power AI-driven services, speech must first be converted into text accurately and reliably.
So how does Automatic Speech Recognition work, where does it deliver the most value, and what are its limitations? This guide explores the technology, its practical applications and the factors businesses should consider when adopting it.
What Exactly Is ASR, and How Does It Actually Work?
Automatic speech recognition (ASR) converts spoken audio into written text, giving other AI systems a way to work with the content of a conversation. It provides the foundation for technologies such as AI voice agents and AI voice assistants, which use transcripts to understand requests, retrieve information and respond to customers. Combined with AI Voice Recognition and AI Voice Generation, these capabilities allow businesses to build more natural, responsive voice experiences across customer service operations.
For example, ASR can feed transcripts into AI agents, which use a large language model (LLM) to interpret requests, retrieve relevant information and decide how to respond. A neural text-to-speech (TTS) engine then converts the response into spoken audio, completing the conversational loop. This combination enables an AI phone agent to handle enquiries, answer questions and escalate complex issues to human agents. As part of a broader agentic AI strategy, these systems can also connect to business tools and execute tasks, supporting more advanced AI automation, customer service automation and customer experience automation. Together, these components power conversational AI applications that do more than respond to callers, helping businesses manage interactions and complete customer service workflows.
Automatic Speech Recognition also supports features beyond live conversations. Transcripts can feed speech analytics, AI-powered quality assurance and customer experience analytics, helping teams identify recurring issues, monitor compliance and understand customer sentiment. Integration with CRM platforms and other business systems connects these insights to customer records, case histories and operational workflows, allowing teams to use conversation data to improve both service delivery and automation.
Understanding these connections is important when evaluating an Automatic Speech Recognition solution. Performance depends not only on transcription accuracy, but also on how reliably transcripts support the AI agents, voice assistants, analytics tools and customer service workflows built around them. Results published for complete AI applications may therefore reflect the performance of several integrated components, rather than ASR alone.
How does a recording become text?
Implementations differ, but most follow the same sequence.
The system captures audio and digitises it. Phone audio is typically sampled at 8 kHz (8,000 measurements a second), much coarser than studio quality, which is why models trained on clean audio often struggle on calls.
The ASR system slices the signal into frames a few milliseconds long and converts each into a numerical picture of its frequencies, called a spectrogram.
An acoustic model, a neural network trained on many hours of transcribed speech, estimates which speech sounds each frame most likely contains.
A language model weighs those candidates against which words usually follow each other, so "account number" beats "a count number".
Post-processing adds punctuation, formats numbers and dates, and can label speakers, a step called diarisation.
Modern end-to-end neural systems merge the third and fourth steps into one model, while older hybrid designs keep them separate. Some add an LLM on top, and that choice carries consequences. In a 2023 evaluation of OpenAI's Whisper, roughly 1% of transcriptions contained whole phrases that nobody had said, and 38% of that hallucinated content included explicit harms. That finding describes the system tested, not every ASR product, which is why testing on your own audio matters.
ConnexAI Automatic Speech Recognition Benchmark: Testing Accuracy on Real Calls
Speech recognition accuracy is commonly measured using word error rate (WER), which counts words a system substitutes, misses or inserts compared with a human-verified transcript. A lower WER means fewer transcription errors, but accuracy depends heavily on audio quality. Models that perform well on clean studio recordings may struggle with background noise, varied accents, unfamiliar terminology and compressed contact-centre audio. For businesses investing in call center AI, benchmarking against real customer conversations provides a more meaningful measure of Automatic Speech Recognition performance in operational environments.
To compare performance under these conditions, ConnexAI published a benchmark in February 2026 evaluating seven streaming automatic speech recognition (ASR) models across 16,311 recordings, totalling approximately 25 hours of real customer service and sales calls. The dataset covered insurance, healthcare and finance, with native and non-native English speakers from the US, UK, Australia and South Africa. All recordings were streamed in their original 8 kHz format, reflecting typical telephony audio rather than cleaner, idealised inputs. Results were scored against human-verified transcripts, using provider-specific normalisation rules to account for differences in text processing.
ConnexAI achieved the lowest median word error rate in the benchmark at 7.7%, outperforming every other system tested. Amazon Transcribe recorded 10.5%, Deepgram Nova-3 15.8%, Google Speech-to-Text 20.0% and OpenAI's gpt-4o-transcribe 28.6%. ConnexAI's median WER was approximately 27% lower than Amazon's, 51% lower than Deepgram's, 62% lower than Google's and 73% lower than OpenAI's in this evaluation.
These differences matter for organisations implementing call center AI across customer service and sales operations. Fewer transcription errors produce more reliable records of customer conversations, giving downstream tools a stronger foundation for search, analytics, quality assurance and AI-driven workflows. Although results vary across datasets and operating conditions, this benchmark demonstrates how ConnexAI's speech recognition performed against the competing models tested on real-world contact-centre audio.
What is Automatic Speech Recognition (ASR)?
How does Automatic Speech Recognition work?
What is the difference between ASR and speech analytics?
How accurate is Automatic Speech Recognition?
How do businesses use ASR in contact centres?
Summary
Automatic Speech Recognition converts speech into text, usually with neural networks, and sits underneath speaker identification, language understanding and AI agents rather than replacing them. Implementations differ in how they handle noise, accents and context.
What Do Businesses Actually Use Speech Recognition For?
Automatic Speech Recognition rarely arrives as a standalone product. It sits inside workflows, and five patterns account for most of its use in contact centres, clinics and public services.
Reviewing every call instead of a sample
Quality teams traditionally listen to a small share of recordings. Transcripts can be searched and scored automatically, so supervisors can find every call that mentions a complaint, a cancellation threat or a missing compliance statement. Platforms differ in whether scoring happens live or after the call, and in how much of it is automated.
Supporting agents during the call
Live transcription can push a knowledge article or prompt onto the agent's screen while the customer is still talking, and can draft the after-call summary. A Gartner analyst estimated that using AI to capture a caller's name, policy number and reason for calling could cut up to a third of the interaction time normally handled by a human agent.
Automating routine conversations
Voice agents answer common requests such as balance checks, bookings or order status. ASR is their ears, so a misheard account number becomes a failed transaction. Adoption is spreading: 35% of contact centres already use agentic AI, meaning AI that carries out tasks towards a goal rather than only answering questions.
Drafting records of professional conversations
Clinics and public services use ambient scribes, tools that listen to a consultation or meeting and draft the note. Between October 2025 and June 2026, probation staff in England and Wales summarised over 800,000 meetings with the Ministry of Justice's Justice Transcribe tool. In the US, 81% of physicians surveyed reported awareness or use of AI in their practice, though that covers AI broadly rather than ASR specifically.
Feeding analytics and compliance
Transcripts provide the foundation for AI Analytics, turning spoken conversations into structured data that teams can search, analyse and act on. Customer experience analytics and customer interaction analytics use this data to identify recurring topics, track sentiment analysis trends and uncover patterns in customer behaviour. Combined with speech analytics, these insights help businesses understand why customers contact support, where conversations go wrong and which issues require attention. This also supports AI customer experience initiatives, helping teams use conversational data to identify friction points and make more informed decisions about service improvements.
As part of a wider customer analytics software or call center monitoring software platform, transcripts also support AI quality assurance by checking whether required statements were read, highlighting potential compliance risks and evaluating agent performance. They create a searchable record of every interaction, making it easier to investigate disputes, review conversations and make evidence-based improvements to service quality.
Where do Automatic Speech Recognition implementations differ?
Mainly in whether they transcribe live or afterwards, which languages and accents they handle, whether they capture both sides of a call, how they treat payment card details and other sensitive data, and how they signal low confidence. A hospital scribe and a collections voice agent may both "use ASR" with very different requirements, so never assume one platform's capabilities apply to another's.
What does a sensible first Automatic Speech Recognition deployment look like?
Most teams start with a single queue and a clear objective. A service manager might introduce an ASR solution for post-call transcription within their existing call center software for post-call transcription in one team, then compare three measures with the previous quarter: the proportion of calls reviewed, after-call work per interaction, and the number of compliance issues identified.
With Automatic Speech Recognition transcribing every call, teams can also search for specific phrases, such as a competitor’s name or a recurring product fault, and track how often they appear. This gives managers visibility across the full call volume, rather than relying on a sample of recordings.
Once the initial results are reliable, teams can expand their ASR solution to support live agent guidance or use voice agents to handle routine enquiries. The sequence matters because the consequences of speech recognition errors vary by application. A misheard word in a post-call review may be easy to correct, while an incorrect digit during a payment conversation could lead to a failed transaction or customer complaint.
Starting small also gives teams time to establish recording consent, data retention and compliance requirements before expanding. These rules vary across US states and must also be considered alongside UK data protection law.
Summary
The main uses of Automatic Speech Recognition are full-coverage quality review, live agent support, routine call automation, drafting records of conversations and analytics. All of them address the same underlying problem: spoken information that is too costly to capture by hand, so most of it goes unused.
What Business Benefits Can You Expect From Automatic Speech Recognition?
Automatic Speech Recognition gives businesses a way to turn everyday conversations into actionable information, while reducing manual work and helping employees deliver better service. When integrated into the right customer service automation workflows, it can free up time, support faster onboarding and give teams clearer insights into customer needs. Research across healthcare and customer service offers a glimpse of what's possible.
More time for customer interactions
In an NHS England-sponsored study across nine London sites and more than 17,000 patient encounters, AI scribing was associated with a 23.5% increase in direct patient interaction time and an 8.2% reduction in overall appointment length. By reducing the need to type notes during conversations, ASR-enabled tools can give employees more time to focus on the person they are helping.
Less admin and lower employee burnout
In a study of 263 clinicians across six US health systems, burnout prevalence fell from 51.9% to 38.8% after 30 days with an ambient AI scribe, alongside 0.90 fewer hours of after-hours documentation. Reducing documentation work can ease the administrative burden that extends beyond the working day. Physicians see similar potential for AI, with seven in ten of 1,692 surveyed identifying opportunities to automate tasks that contribute to burnout.
Faster onboarding and more effective agent support
Automatic Speech Recognition can help new agents get up to speed faster by turning live conversations into text that Customer Service AI tools can analyse and use to provide relevant guidance. Instead of relying entirely on product knowledge, memory or help from colleagues, agents can receive suggested answers, retrieve information from a knowledge base and follow recommended next steps while speaking to customers. Supervisors can also use transcripts to identify knowledge gaps, review real interactions and tailor coaching to the situations agents find most challenging. This makes it easier to build confidence, improve consistency and help employees handle a wider range of queries independently.
Less time spent on routine questions
Gartner estimated that conversational AI could reduce human-handled interaction time by up to a third by automating the collection of identifying details. For businesses handling high call volumes, automating routine questions and capturing spoken answers can reduce the time agents spend on repetitive tasks, freeing them to focus on more complex customer needs.
Stronger profitability and customer relationships
Contact centres with mature AI capabilities reported 85% greater profitability than low-maturity peers. In the same survey, consumers estimated they spent 36% more with companies that provide great service. Automatic Speech Recognition can support these outcomes by making customer conversations easier to analyse, helping teams identify recurring issues, improve quality assurance and address service problems before they affect more customers.
The most relevant benefit depends on where your operation loses the most time or money. Start with the workflow linked to your biggest cost or clearest performance gap, establish a baseline and measure the improvement before expanding your ASR deployment.
Can Automatic Speech Recognition reduce administrative work?
How can ASR help train new customer service agents?
Can Automatic Speech Recognition reduce contact centre costs?
How can businesses measure the ROI of ASR?
Which Type of Automatic Speech Recognition Fits Which Job?
Automatic Speech Recognition products differ most in how and when they listen. Four variants cover most business needs, each suited to a different job.
Real-time (streaming) Automatic Speech Recognition
Streaming ASR transcribes audio as it is spoken. Audio travels in small chunks over a continuous connection, and the model returns provisional words within a fraction of a second, revising them as more context arrives. This lets other systems act mid-call, whether prompting an agent, driving a voice bot or flagging a compliance risk.
The trade-offs are less context, which can increase errors on short utterances, and sensitivity to latency, the delay between speech and text appearing. Streaming systems must also determine when a speaker has finished, a process called endpointing. Cut in too early and a sentence gets split; wait too long and a voice agent feels slow. Ask suppliers how they tune and test this on telephone audio.
What businesses should use real-time ASR? Those needing to act during conversations, including voice automation, live agent guidance and compliance alerts. If you only review calls afterwards, you are paying for speed you do not need.
Batch (post-call) Automatic Speech Recognition
Batch ASR processes complete recordings after calls end. With the whole file available, it can use later context and run extra passes, potentially improving accuracy and reducing the cost per hour of audio. Results arrive with a delay, so batch ASR cannot support live interactions. It also suits historic recordings, which can be transcribed and mined for previously overlooked patterns.
What businesses should use batch ASR? Teams starting with quality assurance, analytics or record retention. It is a lower-risk first step because customers do not depend on the output in real time, and errors can be caught before anyone acts on them.
Ambient multi-speaker ASR with summarisation
Ambient systems capture conversations between several people, separate speakers and usually pass the transcript to an LLM that drafts a structured note. They greatly reduce typing, but the drafting step can omit or invent content. In a University of California, Davis Health pilot, physicians reviewed 356 AI-drafted notes: 94.7% were free from significant errors, yet omissions appeared in 18% of notes and hallucinations, meaning content the AI made up, in 11.5%. These findings relate to one tool in one setting, so every deployment needs its own evaluation.
What businesses should use ambient ASR? Clinics, social care teams, probation services and similar organisations where professional conversations form part of the record, provided a person reviews each draft before filing.
Domain-tuned ASR with custom vocabulary
General models learn from broad audio, while domain-tuned systems incorporate industry terminology through custom vocabulary lists or training on industry-specific recordings. The main benefit is improved entity accuracy, particularly for names, numbers and alphanumeric codes such as postcodes and reference numbers.
The trade-off is maintenance: vocabularies need updating, and narrow training data can reduce performance on other speech. Alphanumeric strings are especially important to test because one incorrect character can make a value unusable, regardless of the overall word error rate. When evaluating suppliers, score these entities separately from general conversation.
What businesses should use domain-tuned ASR? Operations where misheard digits or names carry a cost, including insurance claims, healthcare, financial services and collections. For mostly general conversation, a well-tested general model may be sufficient.
What Can You Combine With Speech Recognition to Get More From It?
Speech analytics
Speech analytics software analyses call transcripts to identify recurring topics, sentiment, silence, customer intent and compliance gaps across every interaction, rather than relying on a small sample of manually reviewed calls. By combining speech analytics with AI Analytics and customer interaction analytics, contact centres can identify patterns behind customer complaints, understand which scripts lead to abandoned sales, and spot where agents need additional support. These insights feed into customer experience analytics and customer analytics software, helping managers make evidence-based decisions about service quality, agent performance and customer retention. Integrated with call center monitoring software and AI Quality Assurance, speech analytics can also flag missed compliance statements, highlight unusual changes in call behaviour and help standardise quality assessments across teams.
CRM and systems integration
A CRM (customer relationship management system) holds your customer records. If transcripts stay outside it, staff still copy and paste. Connected, summaries and outcomes post to the right record automatically, and live data flows back so a voice agent can check an order. ConnexAI's Integrations Marketplace offers pre-built connectors for popular CRMs, payment systems and management tools with two-way sync, alongside webhooks (event-driven triggers that notify another system when something happens), RESTful APIs (a standard way for software to request data and actions from another system) and MCP, the Model Context Protocol, which lets AI agents call external tools on demand. Integration breadth varies by vendor, so check your own systems against any list.
LLM summarisation and agentic AI
Combining Automatic Speech Recognition with large language models (LLMs) allows contact centres to turn spoken conversations into concise call summaries, automated disposition codes, action items and structured customer records, reducing manual after-call work. When connected to agentic AI, these capabilities go further: AI agents can interpret customer intent, retrieve information, update CRM records and trigger workflows across connected systems, with appropriate approval and escalation rules. This is where contact centre agentic AI creates new opportunities for customer experience automation, particularly when AI voice agents need to resolve enquiries from start to finish rather than simply respond to individual questions.
Deloitte Digital's 2026 Global Contact Center Survey reports that 35% of contact centres are already using agentic AI, highlighting the growing focus on systems that can take action as well as generate responses. However, every downstream capability depends on the quality of the original transcript. If ASR mishears an account number, payment amount or booking reference, an LLM may carry that error into its summary or an AI agent may act on incorrect information. Investing in accurate Automatic Speech Recognition solutions therefore helps protect the reliability of the entire workflow, from call documentation to autonomous task execution.
Journey and routing automation
Intent detected in the first moments of a call can set the route, trigger a workflow or hand the customer to a person with full context. ConnexAI Flow, for instance, lets teams define routing rules, outcome logic and agent actions across voice and digital channels. The value is fewer transfers and less repetition of information.
How should you judge any of these combinations? Ask three practical questions. How quickly does data move between systems: live during the call, or in a nightly batch? Who controls what each connected system may read and write, since a voice agent that can update records needs tighter permissions than a reporting dashboard? And what happens when the transcript is uncertain: does the system flag low confidence and hand over to a person, or carry on regardless? Answers to these questions matter more than the length of a connector list.
In What Sectors Has Automatic Speech Recognition Already Proved Its Value?
Healthcare
Clinicians spend significant time documenting consultations, leaving less time for direct patient care. Ambient AI scribes or AI Digital Assistant tools listen to consultations and automatically draft clinical notes, helping clinicians reduce administrative work while keeping patient records up to date. In the US study of 263 clinicians described earlier, burnout fell from 51.9% to 38.8% after 30 days, while a NEJM AI trial covering about 72,000 encounters found a 9.5% reduction in note time for one product. In England, the Cabinet Office reported that doctors using NHS ambient voice spent almost 25% more time directly with patients. These applications show how speech recognition can support clinical workflows beyond transcription, turning conversations into structured documentation that clinicians can review and refine rather than writing every note from scratch.
Government and justice
Probation officers must document meetings with people under supervision, capturing relevant details, decisions and agreed actions. This creates a substantial administrative workload alongside their responsibilities for assessment, guidance and public protection. The Ministry of Justice's Justice Transcribe tool has been used to summarise more than 1.2 million probation meetings in England and Wales between 7 October 2025 and 29 July 2026, while the Cabinet Office reports a 75% reduction in officers' note-taking time. By converting spoken discussions into draft summaries, these tools can reduce the time spent producing records and allow officers to focus more on their interactions with the people they support. Human oversight remains essential to check the accuracy of summaries and ensure that sensitive information is recorded appropriately.
Financial services
Regulated financial firms need reliable records of customer conversations, and calls often contain account numbers, postcodes, transaction references and personal names where a single transcription error can matter. The Bank of England notes that 75% of UK financial firms already use AI, creating opportunities to connect speech recognition with compliance monitoring, customer service and collections workflows.
Perch Group, a UK credit management and debt resolution firm, provides a real-world example. Managing more than two million customers and over £2.5 billion in assets, the organisation needed to scale its operations without increasing headcount at the same rate. By deploying ConnexAI's AI Agent, AI Voice Recognition and AI Analytics, Perch automated a substantial proportion of inbound and outbound customer communications, while using AI to classify customer intent and analyse interactions at scale. Its AI Voice solution handles nearly 50% of inbound call volumes, while AI Analytics has scored more than 213,000 interactions, replacing manual sampling and giving teams broader visibility into performance, compliance and customer sentiment.
This illustrates how speech recognition can contribute to a wider financial services workflow. Transcripts provide searchable records of conversations, while analytics help teams identify recurring issues, assess customer interactions and prioritise calls for further review. In sensitive debt resolution cases, automation can handle routine enquiries and reduce repetitive work, leaving case managers more time for conversations requiring empathy, negotiation and human judgement.
Customer service and contact centres
With labour accounting for up to 95% of costs, contact centres have a strong incentive to reduce repetitive administrative work while maintaining service quality. Automatic speech recognition (ASR) provides the foundation by turning conversations into searchable text that can feed call summaries, quality assurance, compliance monitoring and customer analytics.
Imagine a 300-seat outsourcer handling insurance claims, again as an illustrative scenario. Instead of requiring agents to write detailed notes after every interaction, the operation could generate draft summaries from call transcripts, with agents checking the output before it is saved. Meanwhile, the quality team could use automated analysis to flag calls containing potential compliance issues, missed process steps or signs of customer dissatisfaction, prioritising these for review rather than relying exclusively on random sampling. Over time, the operation could compare handling time, after-hours overtime, after-call work and the proportion of calls reviewed to assess whether the technology is delivering measurable improvements. The aim is to give agents more time for customer interactions while helping managers identify service issues across a much larger share of conversations.
What data protection considerations apply when using speech recognition in sensitive industries?
Can speech recognition handle industry-specific terminology and regional accents?
How can financial firms use transcribed calls to strengthen compliance monitoring?
How can contact centres use speech recognition to improve quality assurance across large call volumes?
How Do You Roll Out Automatic Speech Recognition?
1. Define one problem and its baseline
Choose a single workflow: review coverage, after-call notes or live assistance. Record today's numbers, including handle time, the share of calls reviewed, minutes of after-call work and the error rate in manually typed notes. This gives you a baseline for measuring how an automatic speech recognition solution improves transcription and supports the wider workflow.
2. Collect representative audio
Pull a few hundred recordings across queues, accents, devices and noise levels, and get human-verified transcripts for a subset. This creates a private test set for evaluating ASR accuracy, which you can reuse whenever a supplier releases a new model. Check how well automatic speech recognition handles names, numbers, postcodes and industry-specific terminology, as errors in these details can affect downstream summaries and records. Check consent and retention rules before copying any audio.
3. Align stakeholders early
Bring in operations, compliance, IT security, legal (for recording consent and data residency) and frontline agents. Agree success measures and establish who reviews transcription errors, how corrections are handled and when human intervention is required. This is particularly important when ASR feeds into compliance monitoring, customer records or AI-powered assistance.
4. Choose the mode and shortlist suppliers
Decide between real-time and batch automatic speech recognition, then test every supplier on your recordings. Measure word error rate (WER) and entity accuracy for names, numbers and codes, not headline WER alone. If transcripts will support live agent assistance, AI voice agents or automated call summaries, also assess latency and how reliably the ASR output supports those applications.
5. Integrate with your systems
Connect telephony and your CRM so ASR-generated transcripts land where staff already work. Consider how transcription will feed into related tools, such as call analytics, quality assurance, knowledge retrieval and AI-generated summaries. Confirm how sensitive data is handled, including whether payment card details can be redacted and where audio and transcripts are stored.
6. Pilot, review and iterate
Run one team for several weeks, with human review of transcript samples. Log recurring ASR errors, add custom vocabulary and expand gradually. Track transcription accuracy alongside operational measures, such as after-call work and review coverage, to understand whether the wider workflow is improving. Keep a monthly sample check running after launch, because call mix, products and accents change over time, and automatic speech recognition accuracy can drift with them.
Conclusion
You have seen how Automatic Speech Recognition works, how it differs from speaker identification, language understanding and AI agents, and why published accuracy figures only matter when they match your own audio. The sensible path is the one in the previous section: pick one workflow, measure a baseline, test suppliers on your recordings and expand from there.
Want to see how automatic speech recognition performs in practice? ConnexAI’s team can walk you through its speech recognition and call center AI technology, the benchmark behind its 7.7% median WER, and how it integrates with your CRM and telephony. To understand how it would perform in your environment, ask to test it against a sample of your own audio. You can also explore how accuracy is measured, how errors are flagged and how transcripts feed into your existing systems. Book a demo or speak to a representative to get started.
Sources
Labour can represent up to 95% of contact centre costs; about 17 million agents worldwide (Gartner)
Up to 2 or 3 minutes saved per consultation; national registry of 19 suppliers (NHS England)
Over 800,000 probation meetings summarised, 7 Oct 2025 to 2 Jun 2026 (UK Ministry of Justice)
75% of UK financial firms use AI; a further 10% plan to within three years (Bank of England)
