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.

They’re also a must-have in an era where first-party data has become crucial. In retail and eCommerce, CDPs now decide whether personalization feels seamless or invasive. For healthcare teams, they determine if data moves fast enough between portals, contact centres, and clinics to help patients instead of slowing them down. In finance, they’re becoming a line of defence -unifying records, flagging anomalies, and proving compliance to regulators.

What Is a CDP and Why It’s Not Just a CRM Upgrade

For years, the CRM has been treated as the central hub for customer data. But its design is narrow. It logs contacts, tracks deals, and manages service cases.

What it doesn’t do is unify every fragment of data a company holds – website visits, app behaviour, consent records, transactions, service interactions, marketing touches – into one, live profile that can be activated anywhere. That is the role of the Customer Data Platform.

A Customer Data Platform pulls first-party data from every corner – websites, apps, call logs, sales systems – and brings it into one place. The mess gets cleaned, duplicates are resolved, and what’s left is a living record of the customer that keeps updating. From there, it can do a lot: fuel AI models, trigger automated steps in the background, or simply give a frontline agent the context they usually waste time piecing together.

As Salesforce describes it, the platform moves data “from disconnected silos to real-time engagement.” Adobe takes it further, positioning CDPs as the only way to deliver “personalization at scale, powered by first-party data.”

For many CX teams, CDPs are now the foundation of any strategy for future growth. That’s part of why the market is growing so fast, at a rate of around 39.9% CAGR.

Measurable Customer Data Platform Benefits and ROI

Technology investments rise and fall on one question: do they pay back? In the case of Customer Data Platforms, the answer is increasingly clear. CDP benefits are measurable, and enterprises are using the data to justify spending at the board level.

The financial case is strong. A Forrester Total Economic Impact study of Oracle Unity found that companies achieved an ROI of 158% with payback in just seven months. The study pointed to efficiency gains in segmentation, campaign orchestration, and service response times.

The pattern repeats across industries.

• Telecoms: Spark NZ, working with Tealium and Snowflake, cut campaign delivery time by 80%. That speed translated into millions saved in media spend and higher response rates.
• Membership services: At AAA Washington, Salesforce Data Cloud unified data across channels to power faster, more relevant roadside support. The result was a jump in satisfaction scores and measurable cost savings as manual lookups disappeared.
• Retail: Klaviyo’s customers often cite faster payback than expected. At DKNY, Klaviyo helped to increase click rates and improve conversions by enabling end-to-end personalization.

But the benefits of a CDP often go further than expected, particularly in the age of AI and automation.

Customer Data Platforms as the Foundation for AI, Automation and Orchestration

Artificial intelligence and automation are boardroom priorities, but both depend on the quality of the data underneath. Without a trusted foundation, even the smartest models deliver the wrong outcomes. That is why CDP benefits now reach beyond marketing: they are powering AI decisioning and workflow orchestration across the enterprise.

Salesforce describes its Data Cloud as a “zero-copy CDP,” able to unify data across systems and activate it in real time. In practice, that means generative AI assistants like Einstein or Agentforce can draft responses, recommend actions, and even guide self-service troubleshooting – because the context is accurate. Adobe has taken a similar line, stressing that a CDP is the only way to make personalisation “real-time and scalable.”

Case studies bring this to life. Vodafone used Tealium to launch what it called an “AI-powered customer revolution”. By unifying subscriber data, the telecom giant improved cross-channel engagement by 30% and created new opportunities for automated retention campaigns.

Castore, a fast-growing sportswear brand, turned to Klaviyo’s CDP to scale its customer journeys and marketing campaigns, branching into four new SMS territories, and increasing click-through rates for AI-powered, automated campaigns.

Trying to automate on top of scattered data just makes the noise louder. With a CDP in place, orchestration feels sharper: actions run faster, responses land with more relevance, and journeys stay consistent. The same profile sits underneath everything – whether it’s an AI copilot, a bot handling routine work, or a self-service flow guiding a customer through a fix.

Customer Data Platform Benefits for Compliance, Security and Trust

Data is now under the same scrutiny as financial records once were. A misstep doesn’t just create headlines; it creates fines that hurt. Since GDPR came into force, regulators have handed out more than €5.6 billion in penalties, and the pace has only picked up in the last two years. For banks, insurers, and healthcare providers, fragmented data is a liability.

This is where a Customer Data Platform earns its keep, allowing enterprises to build compliance into the data layer itself. Every profile carries its own consent state. Every interaction can be logged against that consent. Sensitive fields can be masked automatically before they ever reach a campaign or a dashboard. The result: marketers can move quickly, while legal and risk teams know the audit trail is intact.

There are practical examples. Legal & General uses its CDP to improve real-time engagement, but compliance was part of the business case from the start. Data policies are enforced centrally, so every department works from the same set of rules.

A CDP doesn’t guarantee compliance on its own, but it gives organisations the framework they need to protect customers, and themselves.

Improved CX: Personalization and Omnichannel

Most people don’t notice a database. What they do notice is the experience: the email that lands with the wrong name, the app that asks them to log in again, the service agent who doesn’t know what happened last week. A Customer Data Platform exists to stop those seams from showing. That’s where some of the clearest CDP benefits come through.

The promise is simple enough: take signals from across the business, stitch them into a live profile, and feed them back into every channel. In practice, it means fewer repeated questions, less wasted spend, and more relevant offers. Analysts at Aberdeen have noted that organisations with a CDP in place see campaign response rates more than double.

An example? L’Oréal turned to Tealium to power personalization and improve media efficiency worldwide. The CDP became the bridge between consented first-party data and campaigns running in dozens of markets, ensuring local teams could tailor messages without compromising privacy.

Fisher and Paykel used the CDP from Salesforce to improve customer experience in another way – by giving clients a more efficient self-service experience. Agentforce now handles 65% of the company’s interactions, and 45% of customers book directly through AI. The company also benefits from a 50% reduction in call handling times, that means agents have more time to focus on other tasks.

CDP Benefits for Employees and Service Teams

Customer Data Platforms are usually sold on their marketing value, but the upside for employees is just as important. When data is scattered, staff spend time chasing records, re-entering details, or trying to match information between systems. It drags down productivity and morale. Unifying that data is one of the simpler benefits of a CDP, yet it can be the most visible inside the organisation.

For agents on the service desk, a unified profile means they no longer need to piece together a caller’s history from multiple screens. Instead, they see the journey in context – recent transactions, open tickets, even preferences collected elsewhere. Handle times drop, but so does frustration on both sides of the line.

There are clear examples. AAA Washington turned to Salesforce Data Cloud to give service agents a single view of member profiles. The organization described how AI agents could act on those profiles to dispatch roadside help faster and more accurately, without the risk of burnout.

Customer Data Platform Benefits for Business Leaders: Unified Insights and ROI

For executives, the challenge is rarely a lack of reports. It’s the opposite. Every department has its own dashboards, each with different numbers, often telling different stories. Decisions slow down because no one is sure which version of the truth to trust. This is where one of the more overlooked benefits of a CDP comes in: a single, reliable view that leadership can use with confidence.

There’s a strategy angle too. Once unified profiles start feeding into AI, executives can pick up churn risks before they hit the numbers, forecast with more certainty, and shift spending based on what customers are actually doing right now. It cuts down the boardroom surprises – and avoids the frantic catch-up when forecasts and reality don’t match.

The Adecco Group is a case in point. By implementing Salesforce Data Cloud across its global workforce solutions business, the company gave executives access to a 360-degree view of both clients and candidates. Its Chief Digital & Information Officer explained that AI now powers recommendations across the business, backed by trusted data. The CDP benefits in this case weren’t limited to marketing metrics; they reshaped how Adecco planned and delivered strategy.

The Customer Data Platform Benefits Enterprises Can’t Overlook

Much of the discussion around CDP benefits centers on personalization or marketing efficiency. Important, but not the whole story. Some of the biggest advantages are the ones that don’t always make it into vendor slides.

One is data quality for AI. Models are only as strong as the information they’re trained on. A Customer Data Platform acts as a safeguard, filtering personal data where it shouldn’t be used and ensuring inputs are consistent. That makes AI outputs more reliable – and less risky when it comes to privacy or bias.

Another is collaboration. Increasingly, organizations are using data clean rooms – neutral environments where they can share insights with partners without exposing raw records. CDPs can plug directly into these environments, allowing a retailer and a brand, or a hospital and a research partner, to coordinate using anonymised datasets.

Governance often gets left until last, but a CDP brings it right into the data layer. Consent tags travel with the customer wherever their information is used. If someone withdraws permission, that state follows them, blocking future use automatically.

These Customer Data Platform benefits aren’t flashy like a dashboard or a campaign, but they carry more weight. They shape whether AI scales responsibly, whether data partnerships work, and whether trust with customers holds firm.

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.

The communications platform disclosed its Q3 earnings on Monday, highlighting a strong growth from its customer experience portfolio. 

Zoom has also revealed its plans to grow product revenue further by enhancing its existing products with additional AI capabilities to drive AI-first customer experiences. 

During the earnings call, Zoom announced that the platform would be evolving from its traditional customer experience platform to an AI-focused one, aiming to drive productivity and relationships. 

Eric Yuan, CEO and Founder of Zoom, revealed that after its strong quarterly results, Zoom would be able to move forward with this vision. 

He said: “This performance reflects the durability of our business driven by the growing value we are delivering for customers as we evolve from a communications leader to an AI-first platform for work and customer experience. 

“Our vision is to be the AI-first work platform for human connection.” 

Zoom expects to accomplish this transformation by following its three strategic priorities: enhancing its existing products with AI, driving growth in AI products, and scaling AI-first customer experiences. 

Enhancing Existing Products 

During Zoomtopia 2025, the communications platform unveiled AI Companion 3.0, an updated version of AI Companion that utilizes agentic AI not only to respond, but also to act, advising on tasks such as meeting preparations, freeing up time, and call follow-ups. 

Zoom has embedded various AI capabilities and tools, including AI Companion, across its platform foundation, including: 

• Zoom Meetings: Zoom’s AI Companion, a proactive AI assistant tool, offers meeting summaries, follow-ups for next steps, and drives work forward. 
• Team Chat: Rising by 20% in active monthly users year over year, AI Companion supports the messaging product by providing customers with chat summaries, composition tools, and simplified search options for higher productivity. 
• Zoom Phone: This tool now offers Voice Intelligence for call transcription, summaries, noise cancellation, call routing, and analytics and insights for customer data collection, with over 10 million users now paying for Zoom Phone as of early Q3. 
• Zoom Contact Center: Working as Zoom’s cloud-based contact center solution, this platform has adopted AI tools such as Virtual Agent, an agentic AI chatbot offering complex tasks and responses for customers, and AI Expert Assist, allowing agents to utilize AI support in real-time with summaries and translations and offer possible agent responses during customer interactions. 

In fact, AI Companion usage has grown four times year-on-year, revealing that these AI features are seeing value from user activity, resulting in rapid adoption. 

By adding AI to these already-established products, customers are more likely to accept these capabilities once they’ve been integrated into the software. 

Driving Growth in AI Products 

By moving beyond its core communication tools and investing in greater agentic abilities, Zoom offers its customers further access to its AI tools to personalize them to their needs. 

This allows Zoom the chance to drive AI product revenue with product monetization, generating financial growth rather than just adding tools to products. 

In fact, 90% of Zoom’s top CX deals involve paid AI features to contribute to product revenue, offering both subscription and consumption models to suit the customer. 

This includes the development of AI tools such as Custom AI Companion, a paid version of the standard AI Companion model targeted towards enterprise-tier customers, allowing businesses to customize the tool to meet specific demands and policies. 

This also includes similar products such as Virtual Agent and AI Expert Assist, as well as Zoom’s recent acquisition of BrightHire. 

Scaling AI-First Customer Experiences 

Through utilizing tools such as Virtual Agent and AI Expert Assist, Zoom is using AI to transform interactions between customers and enterprises by expanding these products across the platform for automated workflows. 

These tools will involve automating routine requests and advise agents during workflow automation, voice, chat, and video calls for faster results. 

Zoom has also implemented a feature that allows enterprises to install either Zoom’s or a third-party’s AI tool, encouraging them to become familiar with AI usage while tailoring it to their needs. 

This strategy will also involve Zoom working with its largest customers to move AI agents into deployment; however, this may prove difficult. 

During the earnings call, Zoom noted that despite this upsurge in AI tool adoption, its net dollar expansion rate stayed at 98%, 2% lower than expected, likely suggesting that large customers had not been spending as much as hoped on Zoom’s products, with renewals on larger accounts proving difficult to resume. 

Zoom Key Earnings Results 

Zoom’s earnings results showed some strong areas of performance across enterprise and cashflow revenue results 

• Zoom’s total revenue reached $1.23BN, up 4.4% year-on-year 

• Its enterprise revenue grew 6.1%, totalling 60% of Zoom’s total revenue 

• Average monthly churn increased by 2.7%, similar to Q3 2024 

• Its operating cash flow increased to $629MN, up 30% year-on-year 

Optimizely’s latest report, AI & The Click-less Customer, surfaces the scale of the shift.

According to its research, 52% of consumers frequently use AI to research products, and 14% now begin their shopping journey on platforms like ChatGPT and Gemini.

For younger shoppers in particular, this isn’t niche behavior; it’s now the default. The study notes that shoppers aged 18-44 are “around three to four times more likely to use AI daily to research products and services than those aged 55 and over.”

Another striking, and arguably worrying, statistic from the report, given the inconsistencies of AI summaries, is that 42% of consumers are willing to trust AI-generated product summaries without clicking through to a website.

If search engines once served as the high street for digital commerce, AI is fast becoming the front door. Brands, however, are largely standing inside, hoping customers still choose to knock.

A Discovery Channel Businesses Don’t Control

As AI tools work their way into shopping behavior, organizations must realize that the starting conditions of the customer journey are very different. Customers now get a summary, not a set of links. They see a shortlist, not a funnel.

In discussing the report, Optimizely’s SVP of Marketing, Tara Corey, captured the stakes clearly:

“AI isn’t just changing how people shop, it’s rewriting the rules of how brands are found.”

“The moment a shopper decides to learn more, they expect instant clarity, trust, and speed. If you’re not visible or ready in that moment, someone else is.”

That immediacy is crucial. AI-generated answers effectively compress discovery, comparison, brand familiarity, and early consideration into a single exchange.

If customers don’t feel compelled to click through, the traditional levers that retailers rely on (UX design, A/B-tested product pages, meticulously crafted landing pages) don’t matter until much later, if at all.

This is why Optimizely highlights the rise of GEO (Generative Engine Optimization) as a new battleground.

Rather than concentrating on ranking, brands need to prioritize representation by featuring accurately in AI summaries, with consistent product data and clear value propositions that AI can interpret and surface.

For Corey, Black Friday reinforces this point, claiming that the shopping holiday “has always been a test of performance. Now it’s a test of discoverability.

“The brands that understand how to show up in AI platforms, not just search engines, will be the ones consumers engage with first.”

ChatGPT Pushes Further into Product Research

The timing for this shift is amplified by OpenAI’s rollout of shopping research in ChatGPT, a feature designed specifically to guide buying decisions.

According to the company, the feature allows users to simply describe what they want with a prompt, such as “Find the quietest cordless stick vacuum for a small apartment”, and ChatGPT will “ask smart clarifying questions, research deeply across the internet, review quality sources, and build… a personalized buyer’s guide in minutes.”

OpenAI claims that the tool “turns product discovery into a conversation,” pulling up-to-date information on price, availability, reviews, specs, and images.

Indeed, the company didn’t mince words about how widespread this behavior already is, stating that “hundreds of millions of people use ChatGPT to find, understand, and compare products.”

The tool is built on a specialized version of GPT-5 mini trained for shopping tasks. It “reads trusted sites, cites reliable sources, and synthesizes information across many sources to produce high-quality product research,” while adjusting recommendations based on user feedback.

It’s also transparent:

“Your chats are never shared with retailers. Results are organic and based on publicly available retail sites.”

With nearly unlimited usage made available across ChatGPT plans for the holiday season, OpenAI has effectively turned its product research feature into a mass-market shopping assistant at the exact moment Black Friday demand peaks.

OpenAI is also narrowing the gap between product discovery and conversion with the introduction of Instant Checkout, a new feature that allows U.S. ChatGPT users to buy items directly within the chatbot.

The rollout begins with Etsy sellers, with Shopify merchants such as Glossier, SKIMS, and Spanx set to follow.

The system currently supports single-item purchases and will expand to multi-item carts and additional regions over time. It’s powered by the open-sourced Agentic Commerce Protocol, developed with Stripe, which enables secure transactions between AI agents, shoppers, and merchants.

Stripe users can activate it with minimal code adjustments, while others can connect through the Shared Payment Token API or the Delegated Payments specification.

For shoppers, the experience is seamless: product recommendations appear organically, payment and shipping details are confirmed inside the chat, and purchases are completed without switching platforms.

Merchants retain full control as the merchant of record, managing payments, fulfillment, returns, and support through their existing systems. OpenAI notes that while sellers pay a small fee on completed transactions, customer pricing remains unchanged.

The Journey No Longer Starts with the Brand

Optimizely’s data shows that 66% of consumers still start on search engines, but AI is eating into that lead quickly.

When AI tools handle product discovery before a brand’s website ever loads, the customer journey begins in a space the brand doesn’t own and can’t directly shape.

And while 45% of marketers say they have a GEO strategy, only 27% feel fully prepared for customers who first encounter them via AI.

That gap is becoming critical. Optimizely’s data from Black Friday 2024 underlines the intensity of peak-season traffic, which sees a 65% increase in website visits, 99.98% uptime, and over 7,400 A/B tests run across the weekend.

These numbers reflect how much brands still rely on their own channels to convert interest into purchase. But if AI increasingly determines whether customers ever reach those channels, discovery could become redundant.

Consumers are already signaling what they trust, with 31% saying they’re more likely to trust an AI-generated summary if it comes from a known brand, and another 31% preferring a mix of brand and product information.

It is clear that right now, the brand still matters – AI just mediates the introduction.

What Retailers Need to Do Next

Across Optimizely’s report, the following three priorities emerged:

1. Structure Product Data for AI, Not Just Search Engines

Optimizely stresses that AI platforms are becoming “the new front door to digital experiences” and that GEO ensures brands “show up accurately in AI-generated answers.”

Accurate and accessible product data is foundational for that.

2. Treat AI Summaries as an Extension of Your Brand

Optimizely notes consumers will often trust AI summaries without clicking, and trust increases when information comes from a brand they know.

This implies brands must manage how they appear inside AI answers – which is the core of GEO.

3. Prepare Your Site for Late-Stage Arrivals

The concept fits the report’s narrative: customers come to a brand’s site later, after AI does the early filtering.

Optimizely also highlights the need for site performance during peak loads (traffic surges, uptime, speed).

A New Era of Shopping Behavior

ChatGPT’s new feature doesn’t replace retail websites, but it reshapes their role. They’re no longer hubs for early research; they’re destinations customers reach after an AI-guided shortcut.

AI is removing friction from the top of the funnel, but it’s also removing brand influence. The brands that adapt quickly will treat conversational AI as the new storefront, not a secondary channel.

And the next generation of shoppers is already there.

The IRS will use Agentforce across the Office of Chief Counsel, Taxpayer Advocate Services and the Office of Appeals, Paul Tatum, Executive Vice President of Global Public Sector Solutions at Salesforce, told Axios.

The move follows major staffing cuts at the agency implemented by the Department of Government Efficiency (DOGE), which has since been quietly disbanded according to reports, as well as furloughs during the recent government shutdown. The IRS lost around 25 percent of its staff between January and May, shrinking from roughly 103,000 employees to about 77,000, according to a report from the Treasury Inspector General for Tax Administration (TIGTA).

The US Congress has reduced the IRS’s funding under the Inflation Reduction Act (IRA) from $80BN over a 10-year period to $37.6BN. And proposed budgets for the 2026 financial year would reduce the agency’s annual funding by around 20 percent.

“Completing IT modernization projects, providing quality service to taxpayers, and enforcing tax laws with a reduced workforce and budget will be challenging for the IRS,” the TIGTA said.

That’s where Salesforce comes in. The vendor received FedRAMP High Authorization back in June for Agentforce alongside its Data Cloud, Marketing Cloud, and Tableau Next offerings. It launched its Agentforce for Public Sector edition in August, which provides government agencies and local authorities with tailored, pre-built and AI agents.

Salesforce has already been working with the IRS to modernize some basic technology in those departments and will now add AI agents to handle tasks like generating case summaries and searching data to reduce the time it takes to handle customer interactions.

The aim is to help employees process cases so that taxpayers get the information they need faster, rather than to replace them, Tatum said.

The TIGTA pointed out that the agency still needs human staff to manage its complex remit:

“Despite numerous ongoing automation projects, the IRS still needs skilled and experienced employees to interpret tax law changes, investigate criminal activity, prevent fraudulent refunds, and implement complex coding changes for its information systems.”

That puts pressure on employees to deliver all this while providing a satisfactory service to taxpayers.

According to a separate TIGTA report in October, the Inspector General received nearly 250 complaints from taxpayers during the 2024 filing season, from January through May 2024, about interactions they had with IRS representatives.

The TIGTA found that IRS contact center representatives were typically courteous and professional in their interactions with taxpayers. But it identified “some instances where improvements are needed”. In 11 percent of call recordings, taxpayers received poor customer service, such as unprofessional behavior from IRS representatives, long wait times on hold or disruptive background noise. Another 15 percent of calls were either dropped or disconnected.

Why Automated Platforms Are Emerging as a Fix for Government Contact Center Shortages

AI platforms like Agentforce could provide a solution for government organizations like tax agencies that are facing a challenge in staffing contact centers to provide service to millions of taxpayers, especially during peak seasons.

Efforts to boost contact center productivity eventually hit a natural limit, because employers face a challenge in maintaining full staffing. “There’s a ceiling. You can’t make people give 110%. We work with governmental organizations who are losing employees and haven’t got the power or the money to fire into that space,” James Mackay, Regional Sales Manager at conversational AI firm Rasa, told CX Today in a recent interview.

Faced with thinning ranks from retirements, budget cuts or hiring freezes, these agencies are turning to AI to help frontline employees manage the workload. Mackay laid out the scope of the challenge ahead:

“Not reimagining how it works is an existential threat. It’s not like they need to just pay more for people; they can’t get the people. So now they have to figure out how they rationalize that ability to serve these customers. You’ve got a government who can’t afford the contact center, who legally have to provide that service, how are they going to do it?”

For government organizations, changes in service approaches such as scaling back contact center operations are not simply a business decision.

In the UK, HMRC was forced to backtrack almost immediately last year on plans to reduce its customer helplines in an attempt to funnel taxpayers towards its digital services such as chatbots and online forms. The agency stated that was “halting its plans in response to the feedback” from the public, business groups and politicians.

The Canada Revenue Agency (CRA) has also come under scrutiny in recent months following layoffs earlier this year, given ongoing taxpayer complaints around long wait times, unanswered calls, and contact center agents providing inaccurate tax information. The CRA has hired back some staff, as the issue has been escalated to Parliament and the Auditor General. An assistant commissioner at the CRA, Melanie Serjak, told MPs in a standing committee in October that the agency is looking at AI, among other technology tools, to assist agents in providing accurate responses.

While AI is often touted as a solution that “makes everything better,” supporting staff in government agency contact centers is one area where there is true potential, Mackay added.

“Is AI the answer to that? It might not be. There might be other ways… like outsourcing has been a favorite way of dealing with that. But AI has a real chance, if used well, to help augment and get rid of—to some degree—some of the requirements for bums on seats.”

For an agency that fields millions of questions, complaints and appeals each year, even small improvements in case handling can translate into noticeable gains in service experience for the public.

Summarizing long case files, retrieving policies instantly, drafting communications, and pulling up relevant data are the kinds of small efficiencies that add up to noticeable real-time savings in understaffed environments.

However, the challenge will be using AI solutions appropriately and avoiding the risk of hallucinations related to tax filing or collection, which could have serious consequences for taxpayers.

Organizations will need to establish clear guardrails around how automated systems handle sensitive financial information, making sure that every recommendation or calculation they make is grounded in verified data. Human oversight will remain critical, particularly if AI is tasked with interpreting complex regulations or communicating with taxpayers.

Salesforce, for its part, recommends deploying AI agents for non-critical tasks and where human experts can easily intervene.

“Salesforce doesn’t advocate for a blind AI processing tax returns without a human being involved in reviewing and supplementing it,” Tatum told Axios. “When the agents are built, there’s a lot of guardrails put in … [they’re not] allowed to make final decisions, they’re not allowed to disperse funds.”

For other government organizations, the IRS’s move offers a case study into how AI might fit into their own service delivery approaches, aimed squarely at relieving pressure on overworked teams.

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.”

For many enterprises, the toughest part of navigating the agentic AI wave is determining whether the technology actually solves a real problem. Danny Gunn, Head of Workforce Planning at Bet365, put it candidly:

“Part of the understanding of AI is the use case. It may sound great from the sales pitch, but does it actually work? There are quite a few [solutions] where we tried them and they don’t actually work, whether that’s because we’re not ready and our backend processes can’t use all of that or the technology isn’t quite as good as the sales pitch that we get to see.”

For organizations operating under financial constraints, the hype can create tension to turn to AI as a cure-all to deliver cost savings. Kim Baker, Head of Operational Support Services at UK housing association L&Q, noted that leaders are under “huge pressure to not spend too much money and save as much as we can.”

“Everyone just says AI as if it’s now the answer to everything, but I don’t think people really fully understand what AI is and what it might be able to do for them.”

Baker added a critical reminder that any organization considering agentic AI for automation needs to address “simple truths” before jumping on the bandwagon:

“There’s no point launching AI if your data is not right in the first place, because where’s it going to look to answer these questions?”

Without reliable data, even the most advanced agentic AI implementation will deliver inconsistent results, and undermine trust in the technology.

Understanding Where AI Truly Adds Value

The pressure to “have AI” is evident across the industry, often overshadowing the need for alignment with real organizational challenges. Keith Griffin, Cisco Fellow VP, noted how easily organizations default to AI without planning or frameworks: “It is very much about ‘we need to have some AI capability’ but not think deeply about where there’s evidence of where it [gets results] and some of the reasons why AI adoption scores.”

“Mismatched use cases, expecting AI to do things that it’s not very good at, or assuming that it can do more than possible,” all result in failed implementations, Griffin added. “People are getting caught up with which AI models should be used, and it really doesn’t matter… as long as it’s safe to use and an appropriate use for the organization.”

Chris Rainsforth, Director of Learning & Innovation at contact center industry body The Forum, noted that AI has become “a catch all” for any operational challenge and highlighted the pitfall of rushed deployments:

“What we’ve seen a lot of examples of, unfortunately… people trying to deploy something without understanding the problem they’re trying to solve. In the first instance, they spend a lot of money, they spend a lot of time, spend a lot of effort doing something, and then it doesn’t get the results.”

Leadership often grows frustrated when a costly AI project fails to deliver results, questioning why the investment isn’t paying off. Untangling those underlying issues then becomes a difficult and time-consuming process.

But encouragingly, more organizations are beginning to pause and reassess, Rainsforth said.

“On the flip side, we are starting to see more people take a more considered approach, going, ‘what are the outcomes? What am I trying to solve? Let’s then work back from that to understand what technology can enable us to deliver it.’ And AI might not be the answer to every problem. It might be something else.”

“People are starting to have those conversations be a bit more kind of thoughtful about that approach, rather than just wasting time and effort and money,” Rainsforth said.

Putting the Customer First in Tech Decisions

Ultimately, when leaders pay close attention to what their customers need, rather than what the market is hyping, they gain a clearer sense of which tools will genuinely improve experiences and which investments aren’t worth pursuing, several speakers emphasized.

Listening to customers provides the grounding needed to make purposeful, informed choices about where and how to deploy new technology, whether that’s agentic AI or other systems.

Frontline experience shows that customers don’t tend to share the industry’s fixation on AI-driven speed and automation; they’re simply looking for problems to be solved efficiently. As David Holmes, Director of Sales at UK utility SSE observed:

“I don’t have customers tell me, ‘I hope you hurry up with that AI’. I don’t have customers saying, ‘I hope you handle my call quicker’; customers care about the resolution, they care about the time on the phone. They do care about simplicity and I think most sales could benefit from all of that, and that’s where technology can help.”

Across discussions on the show floor, that theme consistently resurfaced. The path to meaningful AI adoption starts with understanding customer needs. When enterprises anchor technology decisions in real-world pain points rather than hype cycles, they’re more likely to avoid missteps and deliver measurable improvements.

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.

With global enterprises like BT, Deutsche Telekom, and EE already seeing results, the discussion explores how hybrid human-AI teams, composable experience design, and data-driven automation are shaping the contact center of 2026 — and how a unified CX platform can future-proof operations and drive measurable business value.

In this CX Today interview, Rob Scott sits down with Shrenik Jain, VP of Product Management for CCaaS at Sprinklr, to explore what’s next for contact centers and how Sprinklr is taking a platform-first approach to transform customer experience.

Jain explains why Sprinklr didn’t follow the legacy voice-first route, how unification enables smarter AI, and what the shift from assistant AI to agentic AI means for tomorrow’s CX workforce. If you’re rethinking your contact center strategy for 2026, this one’s worth a watch.

Key Discussion Points:

• The real reason CCaaS is saturated: Why traditional cloud migration is no longer enough, and what customers now expect.
• Connected intelligence over channel sprawl: How Sprinklr integrates social, voice, messaging, and AI into a single platform.
• The rise of agentic AI: What moving from reactive assistants to autonomous agents means for skills, roles, and workforce planning.
• Actionable insights, not just dashboards: How AI is shifting from descriptive to prescriptive, and even autonomous decision-making.

To explore Sprinklr’s unified CXM platform and how it enables smarter, scalable, AI-powered customer engagement, visit sprinklr.com