In 2026, when OpenAI pulled the plug on its GPT-4o model, the backlash wasn’t just technical frustration. Users mourned it like a lost friend, sharing heartfelt goodbyes online. This unexpected emotional wave underscores a deeper truth: AI isn’t just a tool anymore; it’s weaving into our psychological fabric at work.

AI has gone mainstream, infiltrating daily tasks from drafting emails to analysing data. But the real shift is toward semi-autonomous AI agents that act as coordinators, collaborators, and even mediators. These aren’t passive chatbots; they schedule meetings, monitor compliance, and negotiate on our behalf, evolving from helpers to active team members.

The psychology behind our AI bonds

Research shows we form real emotional connections with AI, even though it’s one-sided. Using frameworks such as Sternberg’s theory of love, studies reveal that users experience intimacy and support through responsive AI interactions. Those with anxious attachment styles bond even more deeply, treating AI as a reliable confidant.

We anthropomorphise these systems, attributing human traits to them because they respond contextually and empathetically. Online trends, like asking AI to “reflect on our relationship,” highlight this symbolic intimacy. Yet, it’s all projection; AI doesn’t feel or reciprocate.

Hidden risks in the human-AI relationship

As AI integrates deeper, three psychological pitfalls emerge:

Over-reliance: We might lean on AI’s constant availability, sidelining human colleagues and eroding team bonds.

Emotional outsourcing: AI’s instant positivity could replace tough human feedback, hindering personal growth.

Trust miscalibration: Over-anthropomorphising inflates our faith in AI’s neutrality, risking errors in high-stakes decisions.

These aren’t deal-breakers; moderate emotional framing can boost AI adoption. The key is setting boundaries to avoid dependency.

The future: AI as workplace mediator?

Imagine AI optimising performance, preserving organisational knowledge, or even mediating conflicts with impartial pattern recognition. This could reshape team dynamics, raising questions about trust, loyalty, and who “owns” shared ideas. Will we prefer AI managers over human ones? How will it handle emotional disputes without emotions?

While AI remains artificial, its social impacts are profoundly real. Organisations must design thoughtfully to harness these relationships without letting them redefine work in unintended ways.

This is a condensed take on the evolving human-AI dynamic. For the full exploration, including deeper research and future scenarios, check out the original article on the Tovie AI blog.

What are your thoughts on bonding with AI at work? Share in the comments!

Artificial Intelligence, Real Feelings? was originally published in Tovie AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

By January 2027, every copper phone line in the UK will be switched off. For many public sector organisations, this feels like just another technical headache: swap analogue for VoIP, keep the lights on, move along. But here’s the thing: this deadline is actually your biggest opportunity.

Why this matters more than you think

Contact centres across the public sector are drowning. More enquiries. More complexity. Fewer resources. Meanwhile, the majority of those calls, including council tax queries, bin collections, and appointment bookings, are routine and highly automatable.

The digital switchover isn’t just about maintaining the dial tone. It’s your chance to fundamentally rethink how citizens interact with your services.

What digital infrastructure unlocks

When you move to digital telephony, you’re not just replacing old wires. You’re opening the door to:

- Conversational AI that handles routine queries 24/7 across voice, webchat, and SMS
- Intelligent routing that gets complex cases to the right people faster
- Unified service journeys instead of fragmented channels
- Real accessibility — not as an afterthought, but built into the foundation

Real-world proof: Westminster City Council deployed an AI-powered platform that now triages and responds to resident enquiries across channels, dramatically improving response times while freeing staff for cases that genuinely need human judgment.

The smarter migration path

You don’t need to rip out everything and start from scratch. The smartest organisations are taking a different approach:

1. Audit your landscape

Know what’s still on copper and what actually needs upgrading

2. Move to digital voice early

Give yourself time to test and refine

3. Pilot AI on high-volume, low-risk queries

Learn what works before scaling

4. Build accessibility from day one

It’s cheaper, better, and legally required

5. Bring your team along

Staff should see AI as support, not a replacement

Why Tovie AI?

We’re not here to sell you a massive platform overhaul. Our approach works with your existing infrastructure, adding an intelligent layer that:

- Automates routine transactions and captures structured information
- Provides natural, empathetic conversations across all channels
- Hands off seamlessly to humans when complexity requires it
- Delivers immediate insights into service demand and performance

Our technology powers organisations like Westminster City Council, proving that conversational AI doesn’t just reduce volume — it creates genuinely better citizen experiences.

The choice is yours

The switchover is happening whether you’re ready or not. The question is: will you just replace the dial tone, or will you use this moment to build smarter, more accessible, future-ready services?

Ready to turn this deadline into an opportunity? Explore more insights on intelligent automation and digital transformation on the Tovie AI blog.

The UK’s Digital Switchover: a Window to Transform Public Services was originally published in Tovie AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI agents are moving automation beyond basic chatbots and voice tools. Instead of only responding to requests, they can complete tasks, trigger actions, and work across systems with minimal human input.

To do this well, organisations need more than models. They need a platform that can connect data, channels, and workflows in a secure and controlled way. That’s where Tovie Platform fits in.

Below are some of the most common AI agent use cases we see in banks and insurance companies, with a clear focus on efficiency, accuracy, and measurable return on investment.

What we mean by agentic AI

Agentic AI refers to systems built with AI agents that can manage tasks end to end. These agents can read information, make decisions based on rules or context, take actions in connected systems, and hand off to humans when needed.

For example, an AI agent can summarise a client conversation, identify next steps, and update a CRM automatically. Another agent can analyse customer data, detect interest in a new insurance product, and send a personalised follow-up. These are already live use cases across financial services.

Why Tovie Platform

Tovie Platform is a no-code and low-code environment for building AI agents, from simple task automation to complex multi-agent workflows that span teams and departments.

It combines chat, voice, and action automation in one enterprise-grade platform. It includes built-in security, governance, and integrations with the systems financial organisations already use. This makes it possible to test and launch AI initiatives quickly, even in highly regulated environments.

AI use cases in finance

Inbound email automation

Banks and insurers receive a constant flow of inbound emails. These include claims, policy changes, KYC documents, broker submissions, and customer questions. Each message needs to be read, understood, classified, and routed. This creates delays, manual effort, and avoidable errors.

AI agents remove this bottleneck by processing unstructured emails and attachments in seconds. They classify messages, extract key data, and route cases to the right systems automatically.

An agent can turn free text and documents into structured data such as policy numbers, claim details, dates, amounts, and broker names. It checks for missing information, updates CRM or case management systems, and triggers follow-up actions like document requests, acknowledgements, or escalations.

Industry research shows that 65% of large enterprises are accelerating Intelligent Document Processing projects in 2025, with a reported 50% reduction in processing time.

Impact and ROI

• Up to 40% faster email processing
• No manual data re-entry errors
• Better SLA compliance
• Higher customer satisfaction
• 30–50% cost savings and up to 50% faster workflows

Typical examples include automated claim intake, broker policy amendments, KYC document handling, instant claim status updates, and secure logging of compliance notices.

Post-call actions automation

Calls generate a large amount of follow-up work. Agents spend time writing summaries, updating systems, and sending follow-ups. This work is repetitive and often inconsistent.

AI agents can handle post-call actions from start to finish. They process call transcripts, summarise conversations, extract key data, and update CRM or case management systems automatically. They can also create tasks, send follow-up messages, log audit entries, and notify teams when escalation is needed.

McKinsey research shows that AI in contact centres can significantly reduce low-value work while improving accuracy.

Impact and ROI

• Up to 50% reduction in after-call work
• More consistent and accurate summaries
• Faster follow-ups
• Stronger compliance and audit trails
• Lower agent burnout
• Better data for QA and coaching

Common use cases include claims follow-ups, policy renewals, transaction disputes, mortgage document updates, and compliance-related calls.

Recurring process automation

Finance teams spend a large share of their time on routine tasks. These include renewals, KYC refresh, payment reminders, reporting, and internal checks. AI agents can manage these recurring workflows reliably and at scale.

Agents can schedule tasks, send reminders, update systems, escalate issues, and prepare reports using data from spreadsheets, databases, BI tools, or core systems. They can also identify risks and trigger the next best action automatically.

Impact and ROI

• 35–50% reduction in manual work
• Lower lapse rates through proactive retention
• Better compliance with automated KYC and reporting
• Faster, more accurate reporting
• Cost savings by freeing teams for higher-value work

Examples include renewal reminder flows, retention campaigns for at-risk customers, automated KYC refresh cycles, and payment reminder workflows.

Explore more financial services use cases

If you want to see how AI agents support other teams, explore these dedicated solutions:

AI-powered policy renewals
Increase revenue by up to 20% with personalised cross-sell offers.

Debt collection automation
Handle up to 1 million interactions per day and reduce costs by 50%.

Enterprise voice AI and support automation
Automate up to 90% of routine enquiries while staying compliant.

Visit our website to discover more.

What teams get with Tovie Platform

Tovie Platform connects enterprise systems, data, and channels in one place. It supports RAG, databases, CRM tools, and multichannel communication across chat, voice, and messaging apps.

Teams can connect to leading language models, including OpenAI, Anthropic, and Gemini, and deploy agents in tools like Teams and Slack.

Built-in FinOps tools help track agent performance, control costs, and analyse usage. For advanced teams, a pro-code environment allows deep customisation and integration with internal systems.

Security and data protection

Tovie AI solutions are built for financial services, where privacy and control are non-negotiable. Security is applied across development, deployment, and operations.

This includes IBM Cloud for Financial Services validation, personal data masking in logs, regular security audits, granular access control, and compliance with GDPR, Cyber Essentials Plus, SOC 2 Type 1, PII requirements, and internal policies.

Tovie Platform Use Cases: How AI Agents Support Financial Services was originally published in Tovie AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Artificial intelligence is no longer new. Many teams already use it to handle routine work by following clear instructions. The next step goes further. Agentic AI can act with little human input, make decisions, and adjust its approach as things change.

This shift moves AI from simple automation to systems that can manage work on their own.

What makes agentic AI different

Traditional generative AI responds to prompts. It waits for instructions and follows them.

Agentic AI does more than respond. It can:

• plan work on its own
• split a goal into smaller tasks
• assign those tasks to other agents
• react when conditions change
• find solutions to problems it has not seen before

In short, it does not just answer questions. It works toward a goal.

Gartner predicts that by 2028, around 15% of daily work decisions will be made by autonomous systems. This points to a clear change in how work gets done across many sectors.

What is agentic AI

Agentic AI is an approach to building systems made up of autonomous AI agents. These agents work together to complete tasks with minimal human input.

A simple way to think about it is this:
Instead of a junior assistant that waits for instructions, you get a senior teammate who can run a project end to end. The system can gather data, delegate work to other agents, review results, and present a finished outcome.

Agentic systems combine:

• traditional software logic
• generative AI models
• sometimes other machine learning models

What sets them apart is autonomy. They do not wait for step-by-step guidance. They decide how to reach the goal and adjust when needed.

From automation to autonomy

For years, companies relied on RPA and chatbots to automate routine work. These tools work well, but only inside fixed rules.

RPA automates tasks that are clearly defined and manually programmed. If something unexpected happens, it fails or stops.

Agentic AI works differently. With clear goals and access to data, it can handle new situations without being reprogrammed each time.

Agentic AI vs AI agents

The two terms are related but not the same.

Agentic AI
This is the overall system design. It describes how multiple agents work together, make decisions, and adapt to changes. Examples include logistics systems, advanced customer support platforms, or autonomous vehicles.

AI agents
An AI agent is one part of that system. It has a specific role and skill set. It usually runs on a language model that supports planning and decision-making. Think of it as a team member with a clear job.

AI agents do the work. Agentic AI is how that work is organised and managed.

The difference, at a glance

The core difference is autonomy. AI agents execute tasks. Agentic systems decide how tasks should be done.

Autonomy with human oversight

Agentic systems often rely on reinforcement learning to improve decisions over time. They learn from outcomes and refine their approach.

This does not mean humans lose control. These systems include guardrails, approval steps, and clear boundaries. In many cases, agents are designed to involve a human when a situation becomes sensitive or unclear.

A common example is banking support. An AI agent can handle most requests, but it knows when to pass a case to a human advisor.

Where agentic AI is used today

Agentic workflows are most useful when conditions change often and fast decisions matter.

• Finance: monitoring markets, adjusting strategies, managing risk
• Insurance: faster claims handling, fraud detection, automated decisions
• Customer support: linking knowledge bases, CRMs, and ticketing tools to cut response times and costs
• Logistics: adjusting routes based on traffic or weather in real time
• Manufacturing: spotting inefficiencies and predicting equipment issues
• Cybersecurity: simulating attacks to uncover weaknesses in AI systems

These are only a few examples. Research from firms like PwC shows many more emerging use cases.

What comes next

Agentic AI changes how work is organised. It does not replace people. It supports them by handling complex coordination and decision-making at scale.

As these systems mature, the focus will shift to using them safely, clearly defining boundaries, and finding the right balance between autonomy and human judgement.

Agentic AI: What It Is and How It Differs from an AI Agent was originally published in Tovie AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Customer service shapes how people feel about a brand and directly influences revenue, retention and overall competitiveness. Over the past few years, AI agents have started to play a major role in contact centres, helping companies improve support quality while reducing operational load.

To understand why AI agents matter today, it helps to look at how customer service has evolved.

The early days: contact centres run by humans

Early contact centres relied fully on human operators. Their goal was to solve requests quickly, deliver a positive experience and encourage loyalty.

As companies grew, the limits became clear:

• long wait times during peak hours
• human error and fatigue
• high operational costs

Automation became a logical next step.

The first chatbots: helpful, but limited

The first wave of chatbots automated simple tasks like FAQs or basic information requests. They helped reduce workload but had clear weaknesses:

• fixed scripts that didn’t support natural dialogue
• difficulty understanding long, messy messages
• frequent escalations to human staff

By 2020, only a small share of customers preferred interacting with chatbots because so many issues were left unresolved.

LLM-powered chatbots: better understanding, new risks

The next generation of chatbots, powered by large language models, could understand intent in long messages and respond more flexibly. But they still had gaps, like generating irrelevant or inaccurate answers.

This stage didn’t last long, because something more powerful emerged.

AI agents: the real shift

AI agents mark a turning point in customer service. They don’t just respond to messages but take needed actions.

AI agents can:

• use external tools and systems to complete tasks
• hold natural, adaptive conversations
• understand complex, multi-step requests
• learn from new data to improve service quality

They handle up to 90 percent of routine queries without human involvement. They reduce wait times, personalise support and free human teams for high-value tasks.

According to McKinsey, companies using AI agents reduce service costs by up to 40 percent and increase customer satisfaction by at least 20 percent.

Today, AI agents are not an experiment. They are becoming a core part of how companies deliver customer experience at scale.

If you want a deeper look at how classic chatbots compare to AI agents and how to introduce agents in your organisation, you can find more on our blog.

The Evolution of Customer Service: From Chatbots to AI Agents was originally published in Tovie AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI is redefining what it means to outsource financial services. No longer just about efficiency, AI automation enables BPOs to become intelligent, customer-centric partners, delivering up to 40% cost savings, 30% efficiency gains, and a 20% increase in recovery rates.

From predictive analytics to virtual agents and automated compliance, AI is helping BPOs build faster, more empathetic, and compliant customer journeys. The result? A shift from cost centre to strategic value driver for financial institutions.

The next evolution of financial BPOs

As financial institutions evolve, their outsourcing partners must evolve too.

Business Process Outsourcing (BPO) providers in the financial services sector are leveraging Artificial Intelligence (AI) to achieve more than just operational efficiency. They’re using it to transform customer interactions and create what many now refer to as “intelligent BPOs.”

In this new model, automation doesn’t replace humans; it empowers them.

From automation to intelligence

According to Staple.ai’s report, "Future of BPO: Embracing Automation and AI," automation has moved far beyond data entry and document processing. Today, AI is helping to manage claims, loan approvals, and fraud detection tasks that were once thought too complex for machines.

For BPOs working with banks and insurers, that means:
✅ Faster turnaround times
✅ Fewer human errors
✅ More consistent compliance
✅ More time for agents to focus on empathy and strategy

And the ROI speaks for itself. Ecosystem Ventures found that AI-led operations reduce costs by up to 40% and improve accuracy by 25–30%. These aren’t distant goals; they’re today’s benchmarks.

Debt collection, reimagined

Debt collection has always been one of the most emotionally charged areas of finance. For customers, it’s stressful. For agents, it’s draining.

AI is changing that.

Instead of pressure, the new focus is on partnership, helping customers navigate debt with empathy and clarity.

AI systems can now segment customers by behaviour, financial history, and even sentiment, creating personalised repayment journeys. Cleartouch’s 2025 study found that predictive AI models improve recovery rates by up to 20%, primarily because they send the right message, at the right time, through the right channel.

Meanwhile, virtual agents powered by Natural Language Understanding (NLU) are engaging customers 24/7, compliantly, conversationally, and without emotional friction. Research on SSRN confirms that AI-driven communication improves cooperation while reducing costs.

Real-world transformations

1. Predictive collection and prioritisation
   AI predicts which customers are most likely to pay, helping agents prioritise effectively. Appinventiv reports efficiency gains of up to 30%.
2. Virtual collection agents
   AI-powered voice and chatbots, like those developed by Tovie AI, negotiate repayment plans, send reminders, and ensure compliance with a tone of empathy.
3. Automated compliance and reporting
   RTS Labs highlights how AI continuously monitors communications for regulatory breaches, flagging issues instantly and reducing exposure.
4. Omnichannel communication strategies
   AI analyses response data across SMS, email, and voice, optimising outreach for each customer segment. Procera Group’s 2025 report validates this data-driven approach.

The human impact

At its core, AI’s most powerful contribution is human.

Customers are more willing to cooperate when treated respectfully, offered flexibility, and given transparent information. AI makes that possible by removing friction, automating repetitive tasks, and allowing users to manage their obligations on their own terms.

This translates into higher recovery rates and stronger customer relationships for financial institutions.
For BPOs, it’s a chance to evolve from a cost centre to a strategic growth partner, one that drives both loyalty and revenue.

Looking ahead

AI integration in financial services BPOs isn’t a futuristic idea — it’s already here.

As Innovature Inc.’s AI Finance Outsourcing 2025 report notes, tomorrow’s leaders will stand out through automation and analytics, empathy-driven design, and intelligent communication.

At Tovie AI, we’re helping BPOs and financial institutions build that future today by delivering faster, smarter, and more human automation.

👉 Explore more insights and real-world case studies on the Tovie AI Blog.

Reimagining Debt Collection: How AI Is Powering the Next Generation of Financial BPO was originally published in Tovie AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Chatbots were a great first step. They answered questions, cut call centre queues, and added a touch of automation to customer service. But in 2025, that’s no longer enough. Financial institutions are moving beyond chatbots toward something smarter: agentic AI.

Our new whitepaper, The Real Evolution of Generative AI in Financial Services, explores how forward-looking banks and insurers use intelligent AI agents to do more than talk. They plan, act, and deliver measurable outcomes.

👉 Download your free copy at tovie.ai

From conversations to completions

The chatbot era was about talking. The agentic era is about doing.

Agentic AI doesn’t just say “Here’s how to update your payment date.” It actually updates it across systems, drafts a confirmation message, and logs the interaction for compliance.

These systems move from conversation to completion, helping teams and customers reach their goals faster, with less friction and more intelligence.

What makes agentic AI different

Agentic AI represents the next leap in enterprise automation:

Autonomous action: agents can execute complex tasks without constant supervision

Goal orientation: they focus on outcomes, not isolated prompts

Memory and learning: they remember past interactions and adapt over time

Reflection: they can evaluate their own performance and adjust their approach

This shift is now possible thanks to orchestration frameworks like LangChain and CrewAI, powerful large language models, and better enterprise integrations.

Four real-world use cases

Inside the whitepaper, we explore how financial leaders are already using Agentic AI in production:

Knowledge AI for internal operations: turning massive policy libraries into instant, context-aware answers for employees.

Document automation at scale: drafting, routing, and approving customer documents end-to-end.

Fraud and risk management: summarising incidents, flagging patterns, and recommending next steps automatically.

Proactive training and communications: creating personalised, evolving training paths for every employee.

These are not future concepts. They are working examples of agentic AI in today’s financial institutions.

Safe by design

With great autonomy comes great responsibility. The whitepaper outlines how to build AI systems that are secure, auditable, and compliant with enterprise standards. Agentic AI from Tovie includes:

Human-in-the-loop controls

Transparent audit logs

Data masking and tone control

Confidence scoring and explainability

Trust and transparency are built into every layer.

Why Tovie AI

Tovie AI helps financial institutions make the leap from chatbots to intelligent agents. Our platform offers an orchestration layer for goal-based workflows, seamless plug-and-play integrations, and security that meets the toughest regulatory requirements. We’ve already deployed live systems across Europe that cut task times by up to 60 percent while improving compliance and customer satisfaction.

Because in the Agentic Era, success isn’t about answering faster. It’s about acting smarter.

Download the full whitepaper for free
Discover how agentic AI transforms financial services and how you can get started today.

Get your copy now at tovie.ai

The Real Evolution of Generative AI in Financial Services: From Chatbots to Agents was originally published in Tovie AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

The global IT market is in the midst of a talent crisis. The rapid rise of new technologies, especially generative AI, has intensified an already pressing shortage. According to the IMF, by 2030, the shortfall could exceed 85 million professionals, causing up to $8 trillion in annual revenue loss.

At the same time, attracting and retaining Generation Z talent is harder than ever. Their priorities and expectations differ sharply from previous generations, forcing companies to rethink how they compete for top minds. One emerging solution? Generative AI, now a critical tool in the race for talent, helping organisations remain competitive in a shifting job market.

Generation AI

Gen Z’s reputation among employers is… complicated. Social media overflows with memes about young hires expecting senior salaries on day one, quitting by lunch, or flagging emails sent after hours as a work-life violation. Underneath the humour, the numbers paint a similar picture: 86% of Gen Z workers are willing to reject tasks and employers that don’t align with their values, and work-life balance tops their list when choosing a job.

Yet research shows a different side: Gen Z adapts to change quickly, embraces new ideas, takes risks, shows leadership regardless of role, and values teamwork.

This adaptability is no accident. Gen Z grew up immersed in technology: smartphones, VR, voice assistants, and AI-powered tools have been part of their world for as long as they can remember. For them, ChatGPT and similar tools aren’t “the future”; they’re part of the present. If a workplace lacks such tools, it can seem outdated and unattractive.

How GenAI transforms the workplace

Generative AI can give young professionals an edge early in their careers. Every newcomer has faced moments where they lacked the experience to complete a task but hesitated to ask “simple” questions. AI offers a safe learning space: whether rephrasing questions, requesting more context, or getting examples, helping them fill knowledge gaps and build confidence.

Onboarding also becomes faster and more engaging. An intelligent chatbot can answer questions, guide employees through company processes, and simulate real scenarios. Instead of slogging through static manuals, new hires get interactive support. To make this work, companies often integrate AI with internal data using Retrieval-Augmented Generation (RAG), ensuring the AI can access the organisation’s knowledge base.

AI also helps with one of Gen Z’s biggest challenges: repetitive tasks. By automating routine work — emails, data analysis, meeting notes, even coding — companies can prevent burnout, which studies show can strike within the first year of a new job.

The most effective setups provide access to multiple specialised AI models through a unified platform, sometimes hosted on a company’s own infrastructure for security, rather than relying on a single tool for everything.

GenAI skills as the new standard

In an era of talent scarcity and evolving employee expectations, generative AI is moving from “nice to have” to “must have.” Companies that integrate it attract younger talent and boost innovation by involving employees in AI-driven projects.

Some examples of fostering an AI culture:

• PwC gamifies AI learning with quizzes and competitions.
• Ally Financial runs mentorship programs pairing AI experts with employees.
• Adobe encourages staff to beta-test AI products and provide feedback.

These initiatives build skills, spark creativity, and give employees the sense they’re shaping the future — a powerful form of non-material motivation in uncertain times.

Generation AI: Winning Over Gen Z Talent in a Skills Shortage was originally published in Tovie AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Voice bots have been around for a while, but generative AI has made them much smarter. Today’s AI voice bots don’t just follow scripts — they can listen, understand, and respond in real time. Businesses are using them for tasks like handling customer service calls, scheduling appointments, and running outbound campaigns.

The good news? This technology isn’t just for big companies anymore. Small and medium businesses can now afford AI voice bots that save time, cut costs, and automate everyday tasks.

Voice feels natural for most people. It’s faster than typing and simpler than most apps. And with recent advances in speech-to-speech technology, voice is finally living up to its potential.

• 60% of customers prefer to call after finding a local business online → AI voice bots can answer instantly, 24/7.
• 59% are open to companies using AI to improve service → AI bots can book appointments, qualify leads, renew subscriptions, and more.

AI voice bots can handle repetitive, high-volume tasks like reminders, waitlist updates, upselling, or cold outreach. This frees your staff to focus on work that adds more value.

You can even test one yourself. Just visit this page, enter your phone number, and get a quick, friendly call from a bot.

When Your Business Might Need a Voice Bot

• Your contact centre struggles with peak-time call volumes
• Customers can’t get through, causing frustration or churn
• Your team is overworked, and turnover is high
• You can’t hire more staff despite growing demand

Voice bots aren’t just for call centres — they help anywhere your business talks to customers by phone.

Real-Life Example

Richmond and Wandsworth Councils in the UK used AI voice bots to reach residents with a simple phone call. The results were better service delivery, less admin work, and smoother communication. The bots handled routine calls, logged notes, and managed interruptions, freeing staff to focus on complex cases.

Key Use Cases for Small and Medium Businesses

Here are a few practical ways AI voice bots are helping businesses today:

1. Customer Service

• Answer common questions so human agents can focus on complex cases
• Collect booking details or capture leads outside office hours
• Act as a virtual receptionist offering 24/7 support

Example: A local salon can confirm appointments, answer “Are you open today?” calls, or share promotions automatically.

2. Sales Support

• Make initial calls and route leads to the right team
• Suggest add-ons or special offers during calls to boost sales
• Qualify leads so sales teams focus on promising prospects

Example: A real estate agency can have a voice bot call new website sign-ups, ask questions about needs and budget, and pass qualified leads to agents.

3. Customer Retention and Engagement

• Reach out to existing customers with seasonal updates or offers
• Run campaigns to win back former clients

Example: A gym can use AI bots to remind members about new classes or limited-time offers.

These are just a few of the ways AI voice bots can help. You can find more use cases on the Tovie blog.

Why Choose Tovie AI Voice Bots

Tovie AI voice bots help businesses reduce costs and improve customer service without adding complexity. They offer:

• Natural, human-like conversations and multi-intent handling
• Smooth handoff to human agents when needed
• Built-in compliance and secure communication
• Custom voice personalities that match your brand
• Advanced IVR that quickly connects callers to the right person

Plans start from €99/month, and setup takes just a few days. No complicated IT is needed.

Try the bot yourself — enter your phone number on our website, and it’ll give you a friendly call.

How Small and Medium Businesses Can Use AI Voice Bots was originally published in Tovie AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Across the UK, more councils are seeing how artificial intelligence can improve public services. Generative AI is already helping local governments modernise processes, make better decisions, and deliver results faster — even with limited budgets and staff.

The UK Government has estimated that automating routine public sector tasks with AI could be worth billions to the economy. An action plan is already in place to encourage adoption, aiming to drive growth, create jobs, and improve daily life for residents.

But what does this look like in practice? Here are real and practical AI use cases already making an impact in local government.

Richmond and Wandsworth Councils: A success story

One standout example comes from Richmond and Wandsworth Councils, who worked with Tovie AI to help their Adult Social Care team. Using Tovie AI’s voice solution, they automated outbound calls to residents for two key occupational therapy use cases:

• Waitlist updates — The AI assistant called residents to give updates on their application status and gather details about current needs.
• Care tech follow-ups — It checked if devices provided by the council were still in use, flagged any issues, and arranged collection of unused items.

The results were clear: call engagement rates hit 74% and 77%, and scaling these outbound calls to production is expected to save around 21,000 minutes of staff time every month. Following this success, the councils are now launching a chatbot to support unpaid carers.

1. Waiting list management

Managing long waiting lists — especially in adult social care and occupational therapy — is a time-consuming challenge for councils.
An AI voice assistant can call residents to confirm appointments, gather information, and update records, all in natural, conversational language.

For example, councils can use AI to check in with residents waiting for mental health support, assessing wellbeing and flagging urgent needs. If necessary, the call can hand over to a human agent for immediate help.

2. Telecare digital switchover

By January 2027, the UK will replace its analogue phone network with a digital one. This change affects telecare services that support millions of vulnerable people. Councils need to inform residents, confirm service needs, and arrange replacements — a massive undertaking with limited resources.

AI can help by making automated calls that explain the switchover, check equipment status, and book appointments when needed.

3. Community engagement, surveys, and urgent alerts

From extreme weather warnings to urgent service updates, councils sometimes need to reach residents at scale and on short notice.

AI-powered voice and text campaigns can deliver these messages quickly, freeing up staff to focus on emergency response. Outside of crisis situations, AI can also run regular surveys and collect feedback to help councils better understand local needs.

4. Council tax reminders and debt collection

Collecting council tax and managing overdue payments can put extra strain on council teams. AI voice assistants and chatbots can send payment reminders, explain next steps, and answer common questions — reducing staff workload and helping prevent debts from escalating.

The same approach can support housing benefit and council tax reduction applications by gathering eligibility information and explaining the process.

5. Care assessments

Under the Care Act 2014, councils must assess residents’ care needs, whether for home adaptations, equipment, or support services. These assessments can take place in person, online, or by phone — and AI can handle the initial stages.

An AI assistant can conduct phone-based assessments, summarise responses, and pass them on to council teams, only transferring to a specialist when required.

These five examples show how AI can help councils save time, cut costs, and improve services for residents. But there are more ways it can be applied — from handling inbound queries to running chatbots for social care and community services.

You can read about five more AI use cases for UK local government on the Tovie blog.

5 Ways UK Councils Are Using AI to Save Time and Improve Services was originally published in Tovie AI on Medium, where people are continuing the conversation by highlighting and responding to this story.