The recent CX Today report on AI in Customer Experience found 46% of businesses now invest more in AI for customer service than they do for sales, marketing, or commerce. Additionally, more than 90% of CX teams are planning on centralizing customer-facing solutions to ensure AI applications can influence every part of the customer journey.

The tools are already everywhere from virtual assistants, AI copilots, auto-reply apps, to smart routing systems. But customer satisfaction levels still aren’t improving as much as expected.

In fact, Forrester’s Customer Experience Index hit a record low in 2024. So, while AI is certainly changing how things get done, it’s still unclear whether it’s making things better for the people on the other end of the call. So, what can CX leaders learn from all this, and what’s next?

The Current State of Generative AI in the Contact Center

It’s wild to think about how quickly generative AI in the contact center went from interesting concept to “already everywhere”. Just two years ago, most contact centers were still playing around with basic chatbots. Now, nearly 80% have implemented some form of generative AI, from agent copilots to auto-summarization tools.

For most companies, the early go-to was AI copilots, built to sit alongside agents during live interactions. These tools help with drafting replies, pulling up relevant articles, and summarizing calls afterward. They’re fast, low-risk, and popular. 82% of CX teams say they’re already using some kind of copilot, and three out of four say it’s delivering real value.

But the tech is moving fast. Many companies are now experimenting with autonomous AI agents, tools that don’t just assist agents but actually handle full conversations on their own.

Some can answer customer questions using internal documentation. Others can triage issues and escalate them if needed.

Despite some very public AI misfires (remember the delivery bot that started swearing at customers?), the confidence in these tools is surprisingly high. According to the same CX Today report, 79% of CX leaders say they’d trust an AI agent to talk to customers without any prior training.

That’s interesting, when 66% of businesses still admit their customers prefer talking to humans over bots. And 61% of industry pros believe the government should step in and guarantee people the right to speak to a human if they want to.

Core Use Cases of Generative AI in the Contact Center

So, what’s generative AI actually being used for in contact centers today? Quite a lot. Some of the most common use cases, as mentioned above, focus on helping agents move faster, avoid admin work, and make fewer mistakes. The CX Today report highlights a few popular options:

Writing replies for agents

This is the big one. Half of all contact centers are using generative AI to draft replies for agents. The AI figures out the customer’s intent, pulls relevant info from internal systems, and writes a suggested message. Agents can tweak it or send it as is. It saves time and helps teams stay consistent in tone and language.

Auto-QA and coaching

Quality assurance is another go-to. Around 45% of teams use AI to automatically review conversations, flag good and bad moments, and even generate coaching tips. It’s a lot faster than handling manual reviews, and it’s helping managers spot trends they might miss otherwise.

Creating knowledge articles on the fly

Tools like NICE Enlighten AI are excellent for this. Instead of waiting for someone to write up a new help article, the AI listens to real conversations and creates articles based on what agents are already doing. Around 39% of teams are using this to keep their knowledge bases fresh and useful.

Post-call summaries and CRM updates

Fewer teams are using AI here (about 38%), but demand is still high. Instead of spending a few minutes writing a call summary, the AI does it instead. It fills out CRM fields, updates tags, and gets the agent ready for the next call in seconds.

Virtual assistants and copilots

Over 80% of contact centers now use some kind of copilot. These tools help agents find answers, suggest next steps, or guide them through processes. They’re everywhere, and for good reason: most teams say they’re working well.

Full-service AI agents

This is where generative AI in the contact center is really starting to evolve. Some businesses are testing fully autonomous agents that don’t just assist, they act as digital team members. They can answer customer questions, escalate complex cases, and even improve themselves over time by learning from past interactions.

Additional Use Cases

Of course, lots of other generative AI use cases are beginning to emerge too, particularly as AI continues to be baked into endless tools and platforms. Companies are exploring:

• Real-time language translation for global support teams.
• AI that understands sentiment and escalates emotional or frustrated customers faster.
• Bots that talk to other bots as “machine customers” become more common.
• Compliance-checking tools that flag risky language or policy violations.
• Predictive tools that spot early signs of churn and trigger personalized retention efforts.

Lessons to Learn from Generative AI in the Contact Center

There’s no shortage of excitement around generative AI in the contact center. But companies aren’t just focusing on the hype anymore, either. They’re paying attention to the reality. Now that the tech is living in thousands of contact centers, patterns are starting to emerge, and they’re not all good.

We’re learning that:

More AI doesn’t automatically mean better CX

Despite all the investment, customer satisfaction scores aren’t going up. At least not consistently. In fact, Forrester’s CX Index hit its lowest level ever in 2024. Throwing AI at the problem without a clear strategy doesn’t guarantee positive results.

A Gartner study found 64% of customers would actually prefer it if companies didn’t use AI in their service strategy at all. That doesn’t necessarily mean AI doesn’t have a place in the contact center, but it might not always need to be customer-facing.

Another major issue is that AI and humans aren’t always working together effectively. Bot-to-human handoffs are still messy. Some people still have to repeat themselves when they get to a live agent. If a bot hands over a conversation, the agent needs the full backstory, otherwise the customer experience falls apart.

Companies Need to Build the Right Foundations

For all the talk about AI transforming customer service, many contact centers are still skipping the groundwork. One common issue is rushed investments, often driven more by C-suite pressure than by actual customer needs. When the goal is just to cut costs or ride the AI trend, the resulting deployments tend to underdeliver or backfire.

But even well-intentioned projects can stumble without a clear understanding of customer demand. Many teams still don’t fully grasp why customers are reaching out. Without good journey mapping or conversation analytics, there’s a real risk of automating the wrong things.

On top of that, governments are starting to enforce guardrails. Spain’s three-minute response rule and California’s AI Act are just the beginning. Brands will soon need to prove that their AI systems are fair, accessible, and respectful of customer rights. Future-proofing means thinking about compliance from the very beginning, not scrambling after a policy change.

Get the Data Right, or Everything Falls Apart

There’s a saying in tech: garbage in, garbage out. And that applies heavily to generative AI in the contact center. Even the best generative AI won’t deliver results if it’s working with bad or incomplete data.

Outdated knowledge articles, siloed customer histories, and missing context can all lead to poor recommendations or wrong answers. That erodes both trust and efficiency.

This is why centralizing and enriching customer data has become a top priority. The best-performing contact centers are investing heavily in unified CRMs, real-time data pipelines, and knowledge management tools that keep everything current. Because when AI has access to the right data at the right moment, its value skyrockets.

Containment is a Flawed KPI

Many companies still measure the success of their generative AI bots by measuring containment rates, how many users stay with a bot and never reach a human. But containment doesn’t always mean that an issue was solved.

Contact centers need to back up their AI strategy with clear, business-relevant metrics. Think resolution rates, average handle time, deflection to human, customer satisfaction (CSAT), and even Net Promoter Score (NPS).

While high containment rates or automation percentages might look good in a dashboard, they rarely tell the full story. What actually matters is whether the customer walked away with their problem solved, and whether they’d come back again next time.

AI Is a Living System, Not a One-Time Project

One of the biggest misconceptions about AI is that it’s plug-and-play. That’s particularly true now that there are so many pre-built bots and AI solutions that seem so easy to use.

Realistically, though, generative AI in the contact center needs to evolve alongside the business. That means tracking performance, reviewing what’s working (and what’s not), and constantly refining prompts, workflows, and escalation rules.

The contact centers seeing the most success place their AI tools in a continuous improvement loop. They create an engine that gets better over time with the right inputs. It’s not just about the tech either. Cross-functional collaboration is also essential.

When service, marketing, sales, and IT teams align on data, goals, and customer experience design, AI deployments become more consistent, scalable, and effective. In some cases, organizations are creating new roles, like Chief Experience Officers, to keep everyone focused on the full customer journey, not just isolated fixes.

The Rise of Agentic AI: What’s Next?

Interest in generative AI in the contact center hasn’t disappeared completely, but business leaders are shifting their attention. While GenAI usually focuses on content, like writing replies, summarizing calls, and generating knowledge, the new era of AI focuses on action.

Agentic AI is stepping into the spotlight. Major companies, from Salesforce, to IBM, Microsoft, Zoom, and even Adobe are investing in a new era of flexible agents.

These agents don’t rely on humans for constant handholding and prompts. They follow multi-step workflows autonomously, adjust dynamically, make decisions based on context, and even access connected tools. Real world examples include:

• AI agents that handles a customer issue from start to finish, triaging the request, checking the knowledge base, resolving the problem, and closing the case.
• Sales-focused AI agents that joins calls with new reps, offers real-time coaching, and updates the CRM automatically afterward.
• Marketing agent that audits landing pages, suggests stronger calls to action, and A/B tests messaging, without human input.

These tools are becoming just as accessible as generative AI, with kits like Salesforce’s Agentforce, Microsoft Copilot Studio, and even Zoom’s customizable AI Companion.

Agentic AI will undoubtedly unlock new value for contact centers, but it also raises the stakes. With more autonomy comes a bigger need for transparency, explainability, and trust.

Looking Ahead: Preparing for the Next Age of AI

For companies investing in generative AI in the contact center or forward-thinking brands exploring agentic AI, there’s still a lot of work to be done. Every organization will need to clean up its data strategy, adjust how it monitors metrics and KPIs, and think carefully about how it will manage human-AI collaboration going forward.

GenAI definitely brought both innovation and disruption to the contact center. Now, companies have numerous lessons to learn as they move forward into the next age of autonomous agents and intelligent growth. The businesses that thrive will be the ones that don’t just focus on reducing costs or speeding up tasks but use AI to enhance customer experiences.

As agentic AI takes hold, the expectations will rise. Customers will want fast, smart answers, but still expect empathy and control. Agents will rely on AI more heavily, but still need tools they can trust. And CX leaders will need to justify not just the cost of AI, but its long-term value.

Today’s contact centers are interacting with customers that stretch from the Silent Generation all the way to Gen Alpha in a day.

As Garry Gormley, Founder of FAB Solutions, put it, “We’re at a strange impasse.”

There’s the Silents and Boomers who still value voice contact with human agents; Gen X and Millennials who mix digital self-service with human support; Gen Z, who jump between apps and channels; and the emerging Gen Alphas, who are growing up expecting hyper-personalized, predictive experiences.

Put all six generations together, and it’s easy to see why no single communication style or service model can cover everyone.

Managing by Generation Isn’t Just an HR Strategy

There’s an economic case for taking generational differences seriously. Data from the World Economic Forum shows that countries can increase their GDP by 19% over 10 years by managing their workforce based on generational groups, and that extrapolates itself out to customers, noted Katy Forsyth, Managing Director of Red Recruitment.

Every generation wants speed, multichannel and intuitive service, but the defining difference between the generations is that younger consumers want personalization, Forsyth said.

“[Gen Z] want everything addressed to them personally. They want an emotional connection with their service… And then when we get the Alphas coming along, they’re even more hyper-personalized.”

These consumers expect brands to use predictive analysis to make recommendations based on the activity on their phones.

Much of this comes down to economic pressure. Gen Z’s spending habits are different because their financial realities are different. Forsyth highlighted that in the UK, “it costs Gen Z six times their salary to get a house deposit. For an Xer in 1995, it was a third of one year’s salary.”

It’s partly because of that smaller buying power that when Gen Z consumers do choose to spend, they want meaning behind the transaction. “The personalization is super important,” Forsyth said, noting that on Black Friday, Gen Z consumers will spend an average of £255 on purchases, which is almost double the £155 that Gen Xers are expected to spend.

That makes Gen Z customers an important demographic for brands to address and they need to understand how to appeal to them. Younger consumers are the mostly likely to use GenAI and AI assistants in their holiday shopping, with Gen Z accounting for two-thirds of shoppers turning to ChatGPT for gift inspiration, according to research from Bread Financial.

Despite narratives around younger generations’ aversion to picking up the phone, Forsyth warned against oversimplification when it comes to providing voice channels:

“Even though they might want to self-serve, you’ll be surprised… as soon as it gets uncomfortable, they want voice. Do not believe the headlines.”

Data shows that when dealing with emotional issues Gen Z wants to speak to a service agent, indicating that companies need to tread carefully and avoid dealing with customers on the basis of assumptions.

Matching Service to the Customer You Actually Serve

For contact centers, getting to grips with practical ways to stay authentic as they juggle all the different ways generations communicate is not an easy task. Beyond managing multiple channels, it’s about making sure every interaction feels honest and human, whether a customer reaches out on the phone, chat or through social.

That means giving contact center agents the freedom and tools to adjust their tone to the customer, using technology to support real connection.

While older generations may still prefer a softened message, younger generations will not tolerate spin. Forsyth said of Gen Z:

“They do not trust you as businesses, whether you’re employing them or selling to them… They will take bad news… but you need to tell them the truth. Do not flower it up… Forget the good news, just go for the jugular, and they’ll respect you a whole lot more. Tell them it’s expensive, but why?”

Trust was a recurring theme during the discussion, as well as the ways in which it spans across generations.

Marco Ndrecaj, Director of Customer Experience Management, Shared Services Connected, said the biggest threat to customer experience isn’t channel fragmentation, it’s eroding trust:

“We need to make sure that we are demonstrating trust in the right way, through communicating honestly and openly about the engagements that we have, either through a bot, or through an AI agent, or through a live person and being really clear on the distinction between those and servicing that to the right people.”

Ndrecaj highlighted a sentiment from one of his contact center advisors: “Customers are being overwhelmed with information… technology on its own doesn’t build trust. People do… What matters is how we use technology to enhance the customer connection.”

The balance between human empathy and AI capability is the foundation on which to build credibility, increasing trust rather than eroding it.

“Humans bring empathy and judgment, while AI provides skill and insight. And when brands get this balance right, that’s when the magic happens.”

Brands are challenged with producing content to appeal to the TikTok generation, which gravitates toward fast-paced, video-led storytelling, while remaining relevant to older audiences that engage in different ways. “How do we think about how we adapt and create that video first experience for the consumers of tomorrow?” Ndrecaj said.

But Ndrecaj also urged brands not to confuse channels with meaning: “I don’t think it’s the actual medium. It’s more around how you make them feel. Gen Z and Gen Alpha think very differently. It’s not about video content or… collecting points. It’s about them feeling a sense of purpose. It’s about organizations that actually have shared values.”

Ndrecaj pointed to brands like Nike and Lego as examples, noting: “They actually invite their customers to co-create products. And that is a feeling that you can’t buy through TikTok or Instagram.” Forsyth, too, cited Nike as brand that is connecting well with customer service for Gen Alpha.

Brands also need to strike a balance between acknowledging the differences between generations and making assumptions about what customers want.

Beyond Stereotypes: Reading the Real Customer Need

Sandrea Morgan, Head of Customer Support at Adanola, warned against treating generational traits as blanket truths. “It depends on where you are as a business and what type of customer you’re interacting with…. because what a customer expects depends on the experience they want to have. What am I trying to purchase. Is it something for the home? Is it something for you personally? That does change what you expect no matter what age you are.”

Morgan contrasted the customer expectations of two different types of businesses.

“In my current role [with a] younger Gen Z customer, the majority of what they want is [for interactions] to be simple, quick, on brand, but pretty efficient and professional. I was in a role a year ago, [with] a slightly older customer. The product was a bit more expensive. There was more of a luxury feel to it. What they wanted from us was very, very different, and the tone of voice that the advisor had was very, very different.”

Understanding the customer makes it easier for brands to move beyond generic customer service design and give their contact center employees the tools and training they need to connect with customers in the most appropriate way for the service they expect.

“Sometimes that’s a piece of technology that you can give them, and sometimes it’s about the training that you give them to be the best in their job every day,” Morgan added, stressing the importance of aligning agents’ skills to the customers they serve.

And to complicate things even further, figuring out what customers want isn’t straightforward, because while they might say they make purchases based on their values, their actual choices can tell a different story.

For Gen Z, for example, their values matter, but they are also under strain from the limits to spending power. As Forsyth pointed out:

“Their values are really critical, but we are in a cost-of-living crisis that is affecting the Zs, and they’re having their values pushed as a result of that.”

Businesses need to be prepared for that to change over the next three to five years and make sustainability more cost-effective to deliver. “Then we keep every generation happy, but particularly the Alphas, who will just be hitting with the spending power,” Forsyth said, as they transition to becoming a larger share of retail spend.

Ultimately, serving customers spanning six generations is about listening closely and building the kind of service that can flex as customers’ needs shift.

A system that mishandles a refund or sends a tone-deaf promotion doesn’t just create a bad interaction; it creates headlines. When mistakes are amplified across social media, the damage spreads faster than any brand can control.

The recent record is full of warnings. Google saw more than $100 billion wiped off its value after a Bard demo went wrong. KFC Germany was forced to apologise worldwide after its automated campaign promoted chicken on the anniversary of Kristallnacht. Australia’s Commonwealth Bank had to abandon AI-related job cuts when public anger boiled over.

Consumers are not patient. Research shows that a third of them will walk away after a single poor experience. That leaves businesses scaling automation into a trust gap big enough to swallow years of brand equity.

The real question isn’t how much can be automated, but how much should be. Cross the wrong line, and efficiency gains quickly become AI brand risk and lasting AI reputational risk.

AI Reputational Risks: Beyond Cybersecurity and Privacy

Talk of AI risks usually circles around security or compliance. Important, yes. But those are risks companies already know how to manage. The one that keeps catching brands off guard is reputational fallout. When automation goes wrong, it breaks trust. Once customers stop trusting a brand, the damage spreads faster than any IT team can contain.

Damaged Consumer Trust & Brand Loyalty

Loyalty is fragile. A single poor interaction can be enough to push customers away. Unfortunately, customers are already wary of AI – most don’t trust bots to begin with. Any evidence that this mistrust is justified is enough to drive people away.

Just look at Google, when its Bard chatbot shared one single incorrect fact during a demo, the company lost over $100 billion in market value overnight.

CNET had to correct 41 out of 77 AI-generated finance articles after readers uncovered plagiarism and factual mistakes. Cursor AI, a coding tool, hallucinated answers so often that paying customers canceled in frustration.

For contact centers, the risk is magnified. Automation often greets the customer first, which means the brand’s reputation is in the bot’s hands. That’s why AI maturity, the ability to run automation on reliable, well-governed data, is now the hidden differentiator. Without it, brands risk handing their most valuable asset, customer trust, to systems that aren’t ready.

Public Backlash & Social Media Amplification

One mistake can live forever online. With social platforms acting as megaphones, AI reputational risks don’t stay contained. A misfired campaign or a bot that behaves badly quickly becomes a trending story, with hashtags turning into boycotts.

KFC Germany learned this the hard way. An automated system sent customers a push notification urging them to celebrate Kristallnacht, the anniversary of a Nazi pogrom, with fried chicken. The backlash was immediate and global.

DPD’s chatbot similarly went viral for all the wrong reasons, insulting users and even writing a poem about how bad the company’s service was.

In Australia, Commonwealth Bank’s attempt to link AI to large-scale job cuts collapsed under public pressure. The bank was forced into a public reversal after customers and employees slammed the move. These incidents highlight how AI reputational risk multiplies once social media takes over. A local error can turn into a global crisis in hours.

Regulatory & Legal Risks

AI reputational risk doesn’t stop with angry customers. Regulators are watching closely, and governments are setting stricter rules. The EU AI Act, GDPR, and California’s CCPA all put sharp limits on how data can be used. Slip up, and the penalties include both fines and headlines.

Air Canada’s chatbot misled a grieving passenger about bereavement fares. When the case reached a tribunal, the airline argued the bot was responsible for the error. The tribunal disagreed, ruling the company was on the hook.

New York City’s MyCity AI assistant told entrepreneurs it was legal to withhold tips from workers and discriminate against tenants, both false and illegal.

Hiring software at iTutor automatically rejected older applicants, a clear violation of employment law. The company settled with the U.S. Equal Employment Opportunity Commission for $365,000.

Biased Algorithms & Discrimination

Bias is one of the most dangerous AI risks, because it strikes at values as much as outcomes. An algorithm that skews hiring, pricing, or recommendations signals that a brand is unfair. That reputational damage spreads quickly.

Amazon’s recruiting AI famously downgraded résumés from women, effectively automating bias in hiring. The project was scrapped after public backlash. Watson Oncology, once pitched as a revolution in cancer care, recommended unsafe treatments in part because its training data reflected narrow patient populations.

For brands, bias creates headlines about discrimination, a label that is hard to shake. Regular bias audits and transparency in how algorithms make decisions are now non-negotiable if companies want to avoid AI brand risk.

Lost Market Share Due to Ethical Misalignment

Ethics and values now carry direct commercial weight. Research shows that 62% of consumers prefer to buy from brands they see as values-aligned. That makes ethical missteps in AI more than a PR problem – they are a revenue problem.

When AI choices seem to put profit ahead of fairness or care, customers don’t wait around,they switch to rivals. That’s when AI reputation risk bites hardest: brands lose not only goodwill but also market share. The only real safeguard is building governance that ties AI use back to the company’s core values and ethics.

How to Reduce AI Reputational Risk: Practical Steps

The fallout from automation mistakes shows up on balance sheets, in lost customers, and in the morale of the workforce asked to pick up the pieces. When AI fails in public, the costs extend far beyond fixing the system.

Zillow’s Zestimate model forced the company to take a $304 million write-down when its automated valuations collapsed the housing business it had built. Legal hallucinations from ChatGPT landed a New York lawyer with a $5,000 fine after fake case citations were submitted in court.

Failures don’t just frustrate customers. They also hit employees. When McDonald’s tested AI at its drive-thrus, the system repeatedly added phantom items, sometimes hundreds of nuggets, forcing staff to override orders and frustrating customers.

So, how do companies minimize reputational risk?

1. Put Data Integrity First

Automation is only as reliable as the data it runs on. Flawed, incomplete, or biased data feeds lead directly to reputational mistakes. SAP estimates poor data quality costs companies $3.1 trillion annually. Forbes highlights it as one of the biggest hidden costs behind AI ethics failures.

Without “agent-ready” data, AI agents are prone to hallucinations – generating wrong answers that erode trust. Strong governance, golden records, and freshness checks are crucial for brand protection.

2. Set Guardrails and Boundaries

Don’t trust machines to do everything. The most resilient companies define clear boundaries for what AI can and can’t handle.

• Low-risk, reversible tasks (simple FAQs, order tracking) are good candidates.
• High-risk or sensitive issues (legal advice, medical guidance, refunds tied to customer hardship) require a human in the loop.

The vendor race to launch AI agent studios (from NICE, Genesys, Five9, Salesforce, Microsoft) is pushing many brands to over-automate before they’re ready. Without boundaries, businesses risk turning efficiency gains into AI brand risk.

3. Audit for Bias and Measure Ethics

Unchecked bias is a reputational hazard. Regular reviews are needed to catch unfair patterns in hiring, pricing, or customer service. Leaders should track ethics alongside business results – monitoring fairness scores, transparency ratings, and compliance checks.

Companies that share what they find, or at least explain how they address bias, often gain more trust. These reviews can’t be a one-time fix; bias audits should sit on the calendar with the same weight as quarterly financial audits.

4. Communicate Transparently

Customers don’t like to feel deceived. Making it clear when they are interacting with automation, and why, can actually build trust. Often, brands with open communication about AI use are far less likely to suffer backlash when mistakes happen.

Data minimization is one effective step: only collecting the data needed, not every available detail. This cuts regulatory exposure and signals respect for customer privacy.

Being clear from the start prevents the impression that something is being deliberately concealed, and that suspicion can do more harm than the original mistake.

5. Keep Humans in the Loop

Not all decisions should be left to machines. Customers expect empathy when something serious goes wrong.

Air Canada’s chatbot failed because it provided misinformation without any human safety net. The tribunal ruling made clear: accountability rests with the company, not the bot.

Retailers often limit bots to handling small refunds automatically, but escalate larger or more emotional cases to a live agent. Keeping people in the loop stops automation from crossing into areas where mistakes can’t be reversed.

6. Monitor Continuously and Govern Proactively

AI systems change over time. A model that works well today can drift off course tomorrow if left unchecked. Strong oversight is essential. Many firms are adopting dashboards to track error rates, bias issues, and customer sentiment in real time. Kill switches and escalation paths provide circuit breakers if a system begins producing harmful results.

Regular “red team” testing, where systems are deliberately stressed to find weak spots, is fast becoming a best practice.

7. Manage the Workforce and Culture

A Duke Fuqua study shows another layer of AI reputational risk: inside the workplace. Employees who use AI are often seen as less competent, creating stigma that slows adoption. Managers who don’t use AI themselves are more likely to penalize candidates who admit to using it.

To avoid these pitfalls, companies need to:

• Train “automation champions” who can demonstrate AI’s value to peers.
• Reframe metrics, focusing on containment, accuracy, and customer trust rather than just speed.
• Create a safe environment for staff to disclose and discuss AI use.

This is about protecting the company’s reputation as an employer and building a culture that sees AI as augmentation, not replacement.

Protecting Against AI Reputational Risk While Scaling

AI isn’t going away. Companies will keep leaning on it to cut costs and speed up service. The risk comes when they hand over too much, too quickly.

Customers don’t forgive easily. Many walk away after a single poor exchange. Regulators are less forgiving still. That puts brand reputation on the line every time an automated system speaks for a company.

The answer isn’t to avoid automation. It’s to draw clear lines around what should be automated and what shouldn’t. Keep data clean. Be transparent. Let humans handle the moments that matter.

This comes after its annual report revealed that AI implementation had increased by 282% since 2024. 

These tools enable enterprises to deploy AI agents without worrying about the reliability and safety of their performance within a system. 

Salesforce’s observability tools provide AI agents with the capabilities to analyze performance, optimize interactions, and monetize stability. 

Agent Analytics

This capability allows enterprises to view how well an AI agent is operating through monitoring its movements, how it’s improving/declining, and where these pain points are coming from. 

This can be turned into performance data, trends, and insights to understand how efficiently these agents are performing and take actionable steps to improve their usage. 

This can also be done across all implemented agents, allowing enterprises to view their agents’ overall effectiveness on customer interaction and support their continuous improvement. 

Agent Optimization

As a key observable capability, Optimization offers customer enterprises full transparency with each agent interaction. 

Customers can uncover how agents make decisions and what led them to make those choices, highlighting performance gaps and session flows to diagnose any issues and deduce the steps needed to improve its performance. 

This can include prompt, rule, or data source adjustments to solve misinterpreted information, inconsistent results or agent hesitation. 

Salesforce provides access to end-to-end visibility for customers to view each agent’s response and action, even with larger, complicated action chains. 

For less varied issues, similar requests can be accumulated to uncover larger problems in patterns or trends. 

Customers can also identify an agent’s configuration issues to pinpoint how an agent’s behaviour is affecting its operation and uncover which areas need to be retrained or personalized further for improved performance. 

Agent Health Monitoring 

This capability can monitor an AI agent’s reliability and safety level to ensure that it is running as expected. 

It provides almost real-time visibility and alerts when the agent is performing unpredictably, notifying the company before any significant damage takes hold. 

It measures an agent’s ability to handle requests, time taken to respond, and tracks incidents such as failures, breaks in activity, or invalid responses. 

By leveraging the capability, teams can speedily detect and resolve issues to minimize agent downtime and continue productivity. 

This tool is formed by two of Agentforce’s components, acting as the foundation for the observability tool by supplying the data and governance structure needed to monitor agents: 

• Session Tracing Data Model: By logging every agent interaction, the data model can store all its data in Data 360 and provide the observability tool the means to generate reliable analytics, error identifiers, and support optimization for unified visibility.
• MuleSoft Agent Fabric: This enables enterprises to control, register, and review agents to justify how they function and interact. 

AI Implementation Report 

In a report published in November, Salesforce announced that AI implementations had increased to 282% since last year. 

This data reveals that companies are now at a far better position to deploy pilot projects at scale rather than risk the threat of experimentation. 

Despite this, data governance, security, and trust remain high priorities, requiring risk management across workflows. 

This means that more companies are going to require higher visibility and control across large-scale AI deployments, which is where Salesforce’s observability tools come in. 

By supporting enterprises with agent interactions, Salesforce’s observability tools can decrease operational risk by allowing teams to keep up to date with agent visibility and analytics to keep agent deployments stable. 

Reddit, a customer of Salesforce, highlighted how Salesforce has allowed the customer enterprise to scale agents securely through consistent visibility. 

John Thompson, VP of Sales Strategy and Operations at Reddit, stated: “By observing every Agentforce interaction, we can understand exactly how our AI navigates advertisers through even the most complex tools.  

“This insight helps us understand not just whether issues are resolved, but how decisions are made along the way. 

“Observability gives us the confidence to scale these agents, continuously monitor performance, and make improvements as we learn from their interactions.”

The timing matters. Shoppers expect speed and personalization in the same breath. Around 71% say they actually want AI built into the shopping journey. They’re not asking for gimmicks. They want better stock visibility, quicker service, and recommendations that actually fit. Miss those marks and loyalty drops fast.

Amazon has already shown where this is heading: robotics in its fulfilment centres have cut costs by roughly 25%, a sign that retail automation solutions can shift margins as well as customer experience.

Tech giants are moving quickly, too. Salesforce, Google, and Microsoft are building AI agents to automate frontline support and back-end operations alike. It’s the “agentification” of the enterprise – automation that doesn’t just support the business but runs through it.

Challenges Retailers Must Overcome

One of the reasons retail automation is gaining so much attention right now is that the right tools can genuinely solve real-world problems – the kind that hold brands back. Right now, retailers have a lot of issues to overcome. The systems they already have don’t connect. Processes run in silos. Customers fall through the gaps. The result is frustration on both sides of the checkout.

Automation has the potential to tackle issues like:

• Disconnected inventories: A shopper checks a website, sees an item listed as available, makes the trip, and finds nothing on the shelf. The reverse happens too: stock piling up in storerooms with no visibility online. Without automation tying together store systems, warehouses, and ecommerce data, managers are left to guess.
• Cart abandonment: More than seven out of ten online baskets are abandoned before payment, a persistent drain on digital sales. Some of that is down to clunky checkout flows. But much of it comes from poor timing: slow shipping updates, lack of payment options, or no personalized nudge to finish the order.
• Poor customer experience: Customer experience is another sore spot. Fragmented journeys cost U.S. businesses an estimated $136.8 billion a year in lost loyalty. It’s the same pattern every time: a customer starts with live chat, follows up by phone, then gets a completely different answer by email. Each handoff repeats the pain. Without retail automation solutions that unify data, every channel feels like a different company.

As Gartner warns, “limitless automation” is a myth. But the goal isn’t automating everything. It’s automating the right things, with the right guardrails, to fix broken journeys.

Retail Automation Use Cases and Benefits

The impact of retail automation shows up in the basics: how goods flow, how shelves stay full, how support teams respond. When it works, it links the back office to the customer in one thread. When it doesn’t, it becomes just another layer of friction.

The following use cases show where the biggest opportunities lie.

Supply Chain & Logistics

Retail supply chains face constant pressure. Surges in demand, shipping delays, and rising costs. The systems built years ago weren’t built for the pace of modern ecommerce. Automation is starting to bridge that gap. AI now forecasts demand spikes, reroutes deliveries, and even triggers restocks without human input. The payoff: fewer empty aisles, lower transport costs, less waste.

Analysts at NetSuite note that automation in logistics can trim lead times significantly while also cutting excess inventory. Amazon’s own network shows the effect at scale, using AI-driven workflows to manage thousands of sites, speed up decisions, and reduce overheads.

Inventory Management & Forecasting

Inventory has always been retail’s balancing act. Too much stock ties up cash and fills warehouses. Too little drives customers to competitors. The gap between online and in-store data only makes it harder.

Retail automation can close that gap. Machine learning models forecast demand more accurately, pulling signals from sales patterns, seasonality, and even local events. IoT sensors and ERP integration push updates in real time, so a store manager isn’t left guessing what’s on hand. One company, FLO, reduced lost sales by 12% just with AI-powered demand forecasting, allocation, and replenishment tools.

Elsewhere, by connecting systems and automating core workflows, ThredUp reduced manual bottlenecks and kept inventory moving efficiently through its marketplace. That meant quicker processing times, fewer errors, and a smoother experience for both sellers and buyers.

Smarter Customer Service

Customer service is often the first test of a retailer’s brand. It’s also one of the hardest to scale. Long queues, repeated questions, and inconsistent answers push customers away.

This is where retail automation has some of the clearest wins. Many firms now use AI agents to cover FAQs, returns, warranty requests, and basic order updates. That shortens queues and frees staff to focus on tougher cases.

Proactive outreach also helps cut down on cart abandonment and cancellations. At a deeper level, automation is reshaping the shopping experience itself. L’Oréal, for example, used Salesforce’s Agentforce to unify data and automate service interactions. Customers received consistent, personalised responses across every channel, turning routine contacts into relationship-building

Revenue Growth & Marketing

Automation goes beyond efficiency; it drives sales. Ecommerce automation tools are now used for predictive pricing, upselling, cross-selling, and tailored offers at scale. Customer Data Platforms bring scattered records into a single profile, enabling true personalisation. That data fuels real-time campaigns designed to anticipate customer needs and lift conversion rates.

By automating parts of its customer experience, marketing, and sales strategies, Simba Sleep generated more than £600,000 in additional monthly revenue. The company’s AI agent now does the work of 8 full-time employees, freeing human staff up for other work. The automation didn’t just cut costs. It created a direct and measurable growth impact.

Enhancing Employee Experience

Retail isn’t just about customers. Employee experience matters too. High turnover and burnout are expensive. Automating repetitive work helps keep staff engaged, while workforce scheduling tools ease pressure during peak demand.

For example, by automating key workforce processes, Lowe’s saved over $1 million in just eight months. The benefits went beyond the bottom line – supervisors reported higher satisfaction, and frontline staff were able to focus on more meaningful work.

Great Southern Bank also achieved similar results, watching attrition rates fall by 44% after building intelligent automation into workflows. This is clear evidence that automated retail tools don’t replace staff. They make jobs more rewarding by removing the least engaging parts of the day. That has a direct impact on retention.

Unlocking Business Insights

Retail runs on data. But in most organizations, that data is split. Marketing has one view. Ecommerce has another. Service teams work with something different again. By the time reports land on a desk, the moment to act has already passed.

Retail automation changes that. Automated systems connect the dots between platforms and feed AI models that can see patterns in real time. Which product lines are about to sell out? Which promotions will flop? Who looks ready to walk?

A single view of the customer makes the difference. That’s why retail automation solutions now often include Customer Data Platforms. When Vodafone brought its records together in one place, engagement rates jumped by nearly 30%, and teams were able to build more effective journeys without risking burnout.

The gains aren’t limited to revenue. Automation can also catch compliance issues, broken workflows, or supply chain weak spots before they turn into costly problems.

Best Practices for Retail Automation

The potential of retail automation is huge. But so are the risks. Without a clear plan, projects can misfire – frustrating customers, raising compliance concerns, and wasting money. The retailers that succeed tend to follow a few clear rules.

• Get the data foundation right: Automation is only as good as the information it runs on. If customer records are scattered, bots will give inconsistent answers and supply chains will make the wrong calls. That’s why many retailers are investing in Customer Data Platforms. A CDP pulls together records from marketing, sales, service, and ecommerce. One view. One source of truth. Without that, everything else is shaky.
• Set guardrails: Gartner has already warned about the danger of chasing “limitless automation”. Not every process should be automated. Not every customer interaction should be handed off to AI. The best deployments use escalation rules, monitoring, and clear ownership so nothing gets lost.
• Avoid generic automation: Customers spot it instantly. A one-size-fits-all chatbot that can’t see their order history does more harm than good. Graia has called out this problem in CX, showing that automation has to be tuned to the business and the customer journey, not just bolted on.
• Train the workforce: Automation changes jobs. It takes away repetitive tasks, but it also requires staff to know how to work with AI systems. The best companies invest in training and create “automation champions” on the front line. That reduces fear and speeds up adoption.
• Measure what matters: Metrics like call volume or handle time don’t show the true impact of automation. Smarter measures include containment quality, safe deflection, and revenue lift. Tools like Scorebuddy now track the performance of AI agents directly, adding oversight where it’s needed most.

Don’t jump in trying to automate everything. Automate carefully, with the right data, the right checks, and the right training.

The Future of Retail Automation: Growth, Loyalty, and Smarter Operations

The role of retail automation has shifted. It’s now about reshaping the sector end-to-end – supply chains, inventory, customer service, and marketing. When used well, automation and AI cut costs, trim waste, and improve both staff and customer experiences.

But there are risks too. Fragmented data, overuse of bots, and weak oversight can undermine trust faster than they deliver returns. Success depends on planning: build solid data foundations, set limits, train teams, and track outcomes that go beyond call times or ticket counts.

Automated retail is already here. The retailers that move carefully but with intent will be the ones winning the next decade, with leaner operations, more loyal customers, and stronger margins.

Yet, despite how prevalent the technology now is within the customer service and experience space, many organizations still view it as a separate tool that gets bolted on.

While this can be effective, to truly reap the rewards of AI, organizations need to start thinking bigger – treating AI as a foundation, not a toolkit.

That much is clear after speaking with Sirte Pihlaja, Head of Team at CXPA Finland, who is organizing the event.

This year’s event will see speakers and CX professionals from around the globe descending on Helsinki for tw0 days of talks and workshops on how AI-native organizations are the future of CX.

But what exactly does AI-native mean?

For Pihlaja, it is about moving far beyond surface-level AI adoption.

“We want to help organizations become AI-native,” she said, explaining how relying on scattered pilots or small internal experiments simply won’t cut it.

“If you think, ‘Ok, I’m going to use ChatGPT or Copilot’… you might only achieve like a 10% personal productivity gain from that. But you won’t be able to deliver the major improvements that you’re looking for.”

And this isn’t purely a CX issue. Pihlaja details how AI-native organizations can have a wider economic impact.

“Finland [Philaja’s home country] has been in an economic slump for almost 20 years,” she said, and AI offers one of the clearest pathways out of it, but only if organizations re-engineer themselves with enough urgency and ambition.

In her words:

“We have been handed the technology and the solution… it’s just a question of making people understand how vital this is for our society in the first place.”

That belief shapes this year’s CX Masterclass, which brings together 25 speakers from across business, government, and technology.

The goal is to give leaders the cross-functional perspective needed to go from dabbling with AI to embedding it in the operating model of the entire organization.

Building the Case for AI-Native Organizations

Pihlaja’s definition of ‘AI-native’ goes well beyond integrating agents into customer service or adding a handful of automation features; it means rethinking how processes run, how decisions are made, and how work is distributed between people and AI systems.

She points to Sitra – the Finnish organization responsible for public-sector funding – as an example of the shift that is already underway.

According to Pihlaja, Sitra has decided that if an organization does not have a plan in place to become AI-native, it will not be given funding.

While these measures may seem extreme, they emphasize how strongly Finland believes in the technology .

Pihlaja believes that CX sits at the center of this transformation. Not because CX teams own AI strategy, but because customer journeys are where the impact becomes tangible – and where broken processes reveal themselves most clearly.

Still, she is clear that this event is bigger than a CX audience alone.

“This is not just for CX people,” she said. “We have sent the invite out to any business people who are interested in developing their business and their organizations.”

Why the Broader CX Community Should Pay Attention

Many CX leaders are already feeling the pressure to adapt, but Pihlaja argues that the change coming next will be deeper and faster than anything the sector has faced in a decade.

For example, Nokia’s former Head of Culture and Leadership, Mark Hayton, will explore what it means to lead in a world where AI agents don’t just support teams – they may be the team.

Pihlaja summarized this by explaining that the industry is “on the brink of a big change… the future of work is going to look totally different when many of us will maybe have an AI agent as our team member… or a boss who is actually an AI.”

Although many a disgruntled employee might be reading this and joking with their colleagues about an AI boss being able to do a better job – but it is a serious point.

How will agents respond and adapt to a world where the contact center leaders might be AI?

Meanwhile, C-level leaders from Finnair, Fujitsu, Microsoft and others will discuss how enterprise strategy shifts when organizations must design with, not just for, AI.

CX leaders often talk about customer expectations rising. Pihlaja’s point is that expectations aren’t the only thing rising. The capabilities available to customers, agents, and AI-driven systems are rising too – and the organizations that fail to adapt their operations quickly will fall behind.

Machine Customers: Where the Shift Becomes Real

Linked with the overall theme of AI-native organizations is the role of machine customers.

This year’s Masterclass devotes significant attention to that subject, beginning with the opening keynote from Gartner VP Don Scheibenreif, who has spent more than a decade researching machine customers.

Historically, “machine customers” referred to connected devices, such as a fridge reordering groceries, or a car booking maintenance. But, as Pihlaja explained, “GenAI and agentic AI have reshaped what we mean by machine customers.”

These agents now have the capacity to negotiate, compare, choose, and transact at a level far beyond earlier definitions.

The closing keynote from Standard Chartered Bank’s Katja Forbes explores another angle: how machine customers will transform complex B2B environments.

“One of her tasks is to understand how machine customers are going to be affecting this kind of clientele,” Pihlaja said – referring to governments, institutions, and large corporates

Other sessions delve deeper into the operational implications. Nexi Group’s agentic commerce lead will lay out how identity, security and payments will function when AI agents initiate transactions.

And for those looking for practical next steps, day two focuses on hands-on capability building – from AI search optimization to agentic commerce tools that help organization’s “speak the same language with the AI… across different channels and different spots”

You can discover everything about CX Masterclass 2025, including the full list of speakers and events, by checking out the website today.

You can also tune in to the livestream here.

At Microsoft Ignite 2025, the company announced that its innovations for AI transformation were being introduced to Microsoft’s Frontier – its preview program for customers to gain early access to newer products. 

This agent is just one of several products Microsoft has announced to address security and compliance issues in AI agents. 

Sales Development Agent 

The Sales Development Agent is designed to advise sales teams in increasing their selling capacity. 

As a fully automated agent, this tool can be used to research, authorize, and handle outreach even after business hours, supporting steady revenue growth. 

This tool can work independently of a human agent, utilizing personalization for seller outreach with automated follow-ups to maintain client-seller relationships that extend beyond a company’s working time zone, as well as hand off leads to human sellers when needed. 

The agent operates through Microsoft’s security and compliance rules, ensuring that the tool can be utilized safely and efficiently in Microsoft 365 without security gaps. 

Microsoft has launched further security and compliance-focused tools to address frequent concerns around AI agents and how they operate around sensitive data. 

These tools are designed to be manageable and to monitor any suspicious activity, risky behavior, or possible threat to data exposure or accidental leaks, helping enterprises to govern their agents reliably. 

Other Security and Compliance Tools 

Entra ID 

Microsoft has announced that Entra ID has expanded its secure identity and access to adapt to the AI era. 

The tool allows users to manage accounts and resources securely, including multi-factor authentication for extra security checks, activity monitoring, and secure cloud workloads. 

It can also help guide at-risk users away from data threats, detect unauthorized AI usage, and prevent overprivileged agents from accessing controls. 

Defender 

One core component of the tool is to govern and protect AI agents across Microsoft’s ecosystem. 

As a unified platform for governance and threat protection, Microsoft Defender can offer protection across all environments where AI agents are active, deploying AI-powered security bots to monitor newer zones to forecast potential criminal activity. 

This includes safeguarding against any potential threats and vulnerabilities to an agent, as well as resolving and investigating incidents where necessary. 

Microsoft Purview

Alongside Entra and Defender, Microsoft Purview is included in Microsoft Agent 365 to ensure compliance across Microsoft. 

It is an AI-enhanced control plane component, in charge of handling recently deployed AI agents to prevent agent-specific risks, rather than being focused on human data. 

The tool also allows customer enterprises to view an agent’s status, their typical tasks and interactions, as well as their current risk level to prevent data loss.  

Foundry Agent Service

This tool includes built-in features to support security, oversight, and policy alignment, such as agent controls that limit the amount of data an AI agent can access. 

Foundry also provides security and compliance teams with real-time tracing and full insight visibility to investigate and review activity. 

It also works with other Agent 365 tools to handle threat detection and prevent data loss, ensuring that all agents are screened properly. 

Edge for Business Security Features 

The browser environment allows companies to hide information with a watermark overlay and set boundaries on web apps to stop data from being copied. 

These features can be used by organizations to secure sensitive information and prevent data leakage by aligning company policies to the tool. 

This can be monitored from within the Microsoft 365 admin center across various devices. 

Microsoft Ignite 2025

Microsoft Ignite will run from Tuesday 18th November to Friday 21st November in San Francisco. 

The company has emphasized its commitment to agentic AI and is set to showcase this message throughout the conference, as well as further touching on issues such as Security and Governance, and Identity and Access. 

You can find out more about the biggest CX announcements from Ignite 2025 here.

The AI startup will allow UJET to continue its AI roadmap for enhanced customer service solutions. 

This partnership will also address customer data analysis issues for UJET’s enterprise customers. 

This acquisition is set to further UJET’s AI roadmap vision by bolstering the company’s AI capabilities and addressing customer experience concerns. 

By highlighting these issues of visibility between customer and leader, organizations will be able to improve their customer issues before they reach escalation. 

In fact, UJET has reported that organizations that are unaware of these individual customer problems are losing approximately $5MN-$30MN in customer churn revenue. 

This can be linked to ignored or forgotten negative customer experience complaints, with organizations reportedly gathering only five percent of reported customer issues. 

According to UJET CEO, Vasili Triant, customer churn remains a blind spot for many enterprises, arguing that customer interaction analysis is not done effectively. 

He said: “Most companies can’t analyze interaction data at scale, leaving many common customer issues in the dark.” 

However, this acquisition provides enterprises the capabilities to view all customer conversations through unifying collected data. 

He added: 

“UJET’s acquisition of Spiral will provide businesses with a unified view of all customer conversations for more proactive, personalized service.”

This will also help enterprises locate blind spots in other areas of the business, such as product, other services, and the company itself. 

In conversation with CX Today, UJET VP Product Marketer, Matthew Clare, highlighted how other areas of companies can utilize this tool to understand their customers’ needs:

“This could be used by product teams to understand product and service issues – by marketing teams who want to understand what customers are saying about campaigns that are running.” 

Spiral’s AI Product 

Spiral is an AI startup specializing in conversational analytics to improve customer experience data. 

By leveraging AI, Spiral can be used to analyze customer interactions at scale to uncover pain points in customer experience, whilst also offering proactive recommendations to enterprises. 

The product can also be used to analyze various customer conversations across voice and chat channels, the internet, online reviews and surveys, and social media. 

Clare stated: “Anywhere customer conversations happen is a data source for this product.” 

Furthermore, this tool can be used to ask questions about customer churning and how enterprises can respond to these results through predictions to improve future customer experiences. 

“They are trying to solve the problems of customer conversations and customer feedback being spread across different teams and organizations,” he said. 

“How do you not only unify data but bring it together in a way that anyone in the organization can run deep research with a simple conversational AI agent?” 

This acquisition allows UJET to strengthen its status as a prominent CCaaS platform provider and offer customers an improved version of what is already available. 

Clare explained that the purchase will extend “UJET’s reach and gives us the ability to sell Conversational Analytics over the top of any Contact Center and CX software that may be in place, without having us need to position our end to end CCaaS platform.”

For Spiral, this acquisition will allow them to continue providing conversational intelligence alongside UJET’s AI service capabilities, rebranding as Spiral by UJET. 

Elena Zhizhimontova, Founder and CEO of Spiral, discussed how the acquisition will allow them to prioritize a customer-focused plan and continue to improve customer outcomes for a wider enterprise range. 

She said: “We built Spiral to take millions of customer conversations and turn them into clear, actionable insight,”  

“By combining Spiral’s AI with UJET’s cutting-edge CCaaS platform for modern-day customer service, Spiral by UJET will continue as the focused product our customers rely on, now with a more CX-driven roadmap and deeper integrations. 

“Together we can shine a brighter light on customer issues for more organizations worldwide, giving brands the clarity they need to spot issues sooner, address problems faster, and create better products, services, and experiences over the long term.”

Customer Feedback

This partnership will allow current and future customers of UJET to experience Spiral’s product integrally by improving its overall AI and product organization. 

Turo, a long-term customer of both UJET and Spiral, has reaped the benefits of both these companies’ approaches to solving customer issues, as well as having collaborated on a program with Spiral to improve its data collection method. 

Julie Weingardt, Chief Operations Officer at Turo, emphasized how both companies have enabled them to receive customer experience resolutions with reduced friction. 

She said: “Spiral’s AI transformed our approach and helped us build a Voice of the Customer program that is smart and strategic, by capturing structured feedback during the support journey.  

“Spiral AI’s platform allows us to analyze customer conversations and commentary, pinpointing areas where we can improve proactively. 

“We’ve used these insights to refine our self-service options, hone our knowledge base, and help better guide quality agent responses.”

Despite the acquisition, Spiral has confirmed that it will continue to work with its existing customers and products however with UJET integrations.

Spiral was acquired by UJET for an undisclosed amount.

Boards and CFOs are already asking for more. A recent Salesforce survey found that 61% of CFOs see AI agents as critical for competitiveness, and 74% expect them to deliver both savings and growth. That’s why concepts like cost per resolution are gaining ground.

Legacy CX metrics don’t capture all the dimensions. They miss the costs of recontact, the revenue lost through refund leakage, and the value of risk avoided. The next wave of Agentic AI ROI needs to measure all of that, alongside the Digital Labor TCO that leaders use to compare AI agents with human FTEs.

The Limitations of Traditional ROI Metrics

Most automation and agentic AI business cases still lean on familiar numbers: average handle time, cost per contact, or headcount savings. Those measures show efficiency, but they don’t show outcomes.

They leave out whether a customer’s problem was solved, whether loyalty improved, or whether churn dropped. They don’t account for Risk ROI either. An AI agent that processes a refund incorrectly or mishandles sensitive data doesn’t just create an unhappy customer -it creates reputational risk and potential compliance costs.

The habit of celebrating “deflection” adds to the problem. Deflecting a call without solving the issue only guarantees the customer will come back, often more frustrated. It’s a false saving that shows up quickly in recontact rates and refund leakage.

Analysts are warning that boards are asking for more. There’s a growing need for ROI measures that cover user satisfaction, decision quality, and organizational resilience, not just efficiency.

There are real-world examples of this gap. Vonage cut its customer response time from four days to four hours, a strong efficiency gain. But the more important measure is whether those faster responses improved resolution rates and customer loyalty. Without that lens, the ROI story is incomplete.

From Cost-Per-Contact to Cost-Per-Resolution: The Crucial Shift

Forget metrics based on the number of calls handled or agents saved. What really matters is whether a customer’s issue gets resolved, and how much it costs to do that. That’s the power of Cost per resolution (CPR): take every expense tied to resolution, agents, tools, tech, overhead – and divide it by the number of issues successfully closed. That gives a business meaningful insight into actual outcomes.

Why this matters so much now:

• Customers value having problems fixed, not just conversations logged.
• CFOs are watching metrics like re‑contact rates, refund leakage, and overall time‑to‑resolution. Those measures expose the real cost of chasing the same issue over and over.
• It unlocks smarter benchmarking and sharper ROI models, far beyond top‑line cost-per-contact comparisons.

Take the move some vendors are making toward resolution-based pricing. Companies like Ada are now charging for each resolved issue rather than every conversation, aligning incentives with the outcome businesses care about most

A public-sector proof point adds real weight. Barking & Dagenham Council saw their cost per enquiry drop from £4.60 to just 5p, reaching 533% ROI in six months, thanks to AI assistance that resolved most queries upfront. Beyond the direct savings, customer satisfaction surged, pushing the value narrative further.

It’s a simple truth: volume of conversation doesn’t equal resolution. One resolved interaction is worth far more than five half-handled ones. Cost per resolution aligns metrics with customer outcomes, and it’s the backbone of any credible Agentic AI ROI model.

Beyond Cost Per Resolution: The Scorecard for Agentic AI ROI

Cost per resolution is a crucial focus point for the new Agentic AI ROI scorecard, but it’s only one part of the puzzle. Here’s how to build a framework that CFOs and COOs would actually value, one that measures real business outcomes, manages AI risk, and proves why automation pays.

Step 1: Calculate Total Cost of Ownership (TCO)

Start by comparing digital labor TCO with human FTE costs. The cost equation includes more than you might think:

• Licensing fees, integration work, and LLM/token usage
• Tooling for orchestration, governance, and observability
• Security, compliance, and ongoing oversight

Those numbers give you a CFO-ready comparison: how automation stacks up against hiring, in both cost and control.

Step 2: Identify Tangible Benefits

Every ROI model needs clear financial impact signals:

• Fewer recontacts. That means less handling and smoother operations.
• Less refund leakage through first-time resolution.
• Faster time-to-resolution, which improves customer satisfaction.

Two examples show this in action:

• Zota used Agentforce to lift resolution rates noticeably, showing that faster doesn’t just mean cheaper, but better.
• NiCE and PSCU automated 71,000 financial transactions a year, delivering clear productivity gains.

Step 3: Include Intangible Benefits

There’s a growing case for measuring less tangible returns too:

• Avoided compliance violations. GDPR can carry fines of up to 4% of global turnover or €20 million, whichever is higher.
• Brand trust. Audit trails and observability show customers and boards that AI behavior is transparent.
• Organizational resilience. ROI should include decision-making speed and agility in a crisis.
• Employee value. Bots handling repetitive tasks mean higher job satisfaction and lower turnover.

These benefits don’t sit on the P&L, but they matter at the board level, particularly when combined with insights into metrics like cost per resolution.

Step 4: Embed Feedback Loops

Without data, there’s no optimization.

• Use observability dashboards like Salesforce Command Center and Scorebuddy to track agentic performance in real time.
• Run regular audits, using ROI calculators like Salesforce’s to validate your assumptions.
• Maintain control groups to measure true gains and risk exposure.

Also, ask for direct feedback from both employees and customers. How is agentic AI improving their experience, and where is it creating friction points?

Step 5: Remember the Governance and Observability Angle

Every conversation about automation eventually comes back to risk. When AI makes the wrong decision, the cost isn’t just operational; it can trigger compliance fines or cause serious damage to brand reputation. For finance and service leaders, that makes governance and observability non-negotiable.

The EU AI Act has raised the stakes. It requires organizations to show why AI systems made the choices they did, and to prove they are fair, safe, and explainable. That means audit trails, transparency logs, and clear escalation paths need to be part of every Agentic AI ROI calculation.

The business case is straightforward. A system that resolves tickets but cannot explain its decisions exposes the company to hidden liabilities. That exposure needs to be factored into the Digital Labor TCO. Compliance safeguards and observability tools may add cost, but they also reduce AI risk and protect against reputational fallout.

Case Studies: Proof of Resolution-Driven ROI

Real business value lives in real stories, showing that companies are moving beyond efficiency, to focus on tangible gains and growth opportunities.

Look at Oldenburgishce Landesbank, a privately owned regional bank that introduce AI-powered solutions into customer service. They achieved a 15% reduction in wait times (showing efficiency gains of 510%). However, they also achieved a 5 increase in Net Promoter Score.

Satellite leader Echostar saved team members more than 35,000 hours of work annually with AI and automation, but even more importantly, the company reduced the price of sales-related calls from $26 per hour to just $2.

With Ada, Neptune Flood reduced their ticket resolution times by 92%, minimized cost per ticket by 78%, and earned $100k in operational savings in the first year. On top of all that, they set the foundation for new workflow automation flows that are primed to add value to the business and unlock new avenues for revenue.

Cost Per Resolution and Agentic AI ROI

Customer service leaders can’t afford to measure automation in the same way they did a decade ago. Cost per resolution is quickly becoming the number that matters, because it shows whether issues are fixed properly the first time and what that costs the business. It links directly to loyalty, refund accuracy, and the true cost of keeping customers happy.

For finance leaders, the comparison between human labor and AI agents is no longer simple. Digital Labor TCO has to include not only licensing and model costs but also the spend on oversight, compliance, and monitoring. Those controls reduce AI risk and protect against the reputational damage that follows when customers lose trust.

Payback can still come quickly. Independent studies suggest most automation programs deliver returns in as little as 12 to 18 months. After that, the benefits usually grow as adoption rises and workflows mature. But the organizations seeing the strongest returns are those that build guardrails into their models from day one.

That’s the direction of travel for Agentic AI ROI. It’s not about shaving minutes off handle time. It’s about proving to boards and regulators that every resolution is reliable, efficient, and accountable. Companies that measure ROI through that lens will not only show faster payback but will also protect the trust that underpins their brand.

Announced at the vendor’s flagship Refresh event, the updated platform includes the following three fresh capabilities:

• Vertical AI Agents
• Freshdesk Command Center
• Freddy AI Insights

Together, the three tools are designed to enable CX teams to lower response times, boost resolution rates, and gain real-time visibility into the issues slowing growth and efficiency.

The updated solutions are partly in response to Freshworks’ Cost of Complexity Report, which outlined ‘uncustomizable workflows’ and ‘too many tools to toggle between’ as two of the biggest software-related challenges currently impacting customer service agents.

These findings speak to the wider issues of over-complication and solution fatigue that have been prominent in recent times, and can prevent agents from truly maximizing the benefits of AI.

This point was raised by Srini Raghavan, Chief Product Officer of Freshworks, when discussing his company’s latest releases.

“CX leaders want to scale instant, empathetic service without sacrificing quality or time. Yet fragmented systems, outdated tools, and redundant processes waste hours of their teams’ time,” he said.

“Freshworks is breaking that cycle of complexity by uniting how teams work and helping them reclaim hours of lost productivity, enabling teams to meet customer needs with greater speed, and giving leaders an easy way to uncover growth drivers and detractors proactively.”

So, let’s take a closer look at whether the revamped capabilities can deliver on Raghavan’s promise.

Vertical AI Agents

Freshworks has introduced new Vertical AI Agents for eCommerce, fintech, travel, and logistics within the Freddy AI Agent Studio, a workspace for building, testing, and monitoring AI agents.

The agents come with more than 50 prebuilt workflows, reducing the setup effort typically required when deploying industry-specific automation.

Designed to handle tasks as well as respond to inquiries, they integrate with systems such as FedEx, Shopify, and Stripe.

Users can also create custom agentic workflows, enabling the agents to deliver end-to-end resolutions aligned with sector-specific processes.

The latest move from Freshworks is another example of a major customer service and experience vendor choosing to move into the industry-specific agent arena.

Indeed, at the beginning of the year, Salesforce released Agentforce for Retail, a skills library for industry-specific AI agents. This was followed by the launch of Agentforce for Public Sector and Agentforce for Manufacturing back in August.

Talkdesk is another vendor that has targeted specific verticals in the past, havign released AI agents for Healthcare and Finance earlier this year.

In doing so, all of these vendors are looking to differentiate themselves in a crowded space and leverage AI to make their tools more effective.

Freshdesk Command Center

The enhanced Freshdesk Command Center consolidates multiple customer service channels – email, chat, WhatsApp, and social media – into a single workspace, reducing the need for agents to switch between applications.

It combines AI assistance with process automation to streamline operations, helping teams retrieve relevant customer data and respond more efficiently.

AI capabilities within the platform also provide real-time insights, including conversation sentiment, SLA deadlines, and access to customer information such as purchase history, subscription details, FedEx tracking updates, Stripe payments, and Shopify product data.

In addition, agents are able to access Freddy AI Copilot, the platform’s AI assistant, from directly within the command center.

They can use the copilot to summarize email threads, suggest responses, and recommend actions.

Single-click operations can trigger end-to-end processes, including refunds, replacement orders, and activity logging, enabling faster resolution of customer requests without leaving the command center.

Freddy AI Insights

Freddy AI Insights offers real-time visibility into support operations, helping leaders monitor performance trends and detect anomalies before they affect the customer experience.

The platform provides alerts for spikes in support volume, SLA breaches, and workflow bottlenecks, alongside built-in root cause analysis that highlights why changes occur.

Visual dashboards present performance shifts clearly, enabling teams to identify critical patterns, assess which groups are impacted, and take timely action.

Designed as a continuous analytics tool, Freddy AI Insights also translates operational data into actionable intelligence to support proactive decision-making.

Breaking the Cycle of Complexity

While each of the three capabilities addresses different aspects of the customer service and experience tech sector, the overall trend is clearly to make these tools more user friendly.

With the incredible advances that AI has brought to the customer service and experience sector in recent times, some vendors can sometimes be guilty of being blinded by their own shiny new toys.

It doesn’t matter how impressive a vendor’s new feature is; if it isn’t easily accessible and navigable, agents will offer resistance.

As more and more frontline agents struggle with tool fatigue, vendors must prioritize usability.