But customers? They’re not exactly throwing confetti about it.

In fact, Gartner found 64% of people would rather companies didn’t use AI for service, full stop. Plus, only 24% of customers trust AI with anything messy, like complaints, policy decisions, those emotionally-charged “your system charged me twice” moments. So there’s this weird split: companies rushing forward, customers dragging their heels, and everyone quietly hoping the bots behave.

That’s the real issue, honestly. AI doesn’t warn you it’s going wrong with an error code, at least not all the time. It goes sideways in behavior. A chatbot invents a refund rule. A voice assistant snaps at a vulnerable caller. A CRM-embedded agent quietly mislabels half your complaints as “general enquiries.”

This is why AI behavior monitoring and real AI governance oversight are really becoming the only guardrails between scaling CX AI, and watching it drift into places you really don’t want headlines about.

Why AI Governance Oversight Is Critical for CX Success

Most CX teams think they’re rolling out “smart automation,” but what they’re actually doing is handing decision-making power to systems they don’t fully understand yet. That’s just where the industry is right now. The tech moved faster than the manuals.

This is exactly why AI behavior monitoring, AI governance oversight, and all the messy parts of CX AI oversight are suddenly showing up in board conversations. It’s pattern recognition. The problems are becoming glaringly obvious. We’ve all seen a bot make a weird decision and thought, “Wait… why did it do that?”

Ultimately, we’re starting to bump against a very real trust ceiling with AI and automation in CX.

KPMG’s global study found 83% of people expect AI to deliver benefits, but more than half still don’t trust it, especially in markets that have seen its failures up close.

Unfortunately, business leaders aren’t making it easier to trust these systems either.

Here’s where things get dicey. PwC’s 2025 research shows only a small fraction of companies feel “very effective” at AI risk monitoring or maintaining an inventory of their AI systems. That’s not just making customers skeptical, it’s opening the door to countless problems with security, data governance, and even AI compliance.

What Off-the-Rails AI Looks like in CX

It’s funny, when people talk about AI risk, they usually imagine some Terminator-style meltdown. In reality, CX AI goes off the rails in more subtle ways:

Hallucinations & fabricated information

Hallucinations sound like this mystical AI thing, until your bot confidently invents a cancellation policy that’s never existed and suddenly, you’re handing out refunds like coupons.

2025 observability research keeps pointing to the same pattern: hallucinations usually come from messy or contradictory knowledge bases, not the model itself. A tiny change in wording, an outdated policy page, and suddenly the AI “helpfully” fills in the blanks.

This is where AI drift detection becomes so important. Hallucinations often creep in after small updates to data pipelines, not major system changes.

Tone errors, “cold automation” & empathy failures

Efficiency without empathy doesn’t win customers.

Brands aren’t losing customers because AI is wrong, they’re losing them because the AI feels cold. It encourages negative response. Research found 42% of Brits admit they’re ruder to chatbots than humans, and 40% would pay extra just to talk to a real person during a stressful moment.

Tone errors don’t even have to be outrageous, just off-beat. This is absolutely part of CX AI oversight, whether companies like it or not.

Misclassification & journey misrouting

Smart routing can absolutely transform CX. It might even be the secret to reducing handling times. But if your intent model falls apart:

• Complaints get tagged as “general enquiries.”
• Cancellation requests bounce between departments.
• High-risk customers get routed to low-priority queues.
• Agents spend half their time rewriting what the AI misread.

When companies adopt agentic systems inside CRMs or collaboration platforms (Salesforce, Teams, Slack), misclassification gets even harder to catch because the AI is now initiating actions, not just tagging them. Behavioral drift in these areas builds up subtly.

Bias & fairness issues

Bias is the slowest-moving train wreck in CX because nothing looks broken at first.

You only notice it in patterns:

• Certain accents triggering more escalations,
• Particular age groups receiving fewer goodwill gestures,
• Postcode clusters with mysteriously higher friction scores.

A survey last year found 63% of consumers are worried about AI bias influencing service decisions, and honestly, they’re not wrong to be. These systems learn from your historical data, and if your history isn’t spotless, neither is the AI.

Policy, privacy & security violations

This is the failure mode that’s getting more painful for business leaders:

• A bot accidentally quoting internal-only pricing.
• A Teams assistant pulling PII into a shared channel.
• A generative agent surfacing sensitive case notes in a CRM suggestion.

None of these will necessarily trigger a system alert. The AI is technically “working.” But behaviorally, it’s crossing lines that no compliance team would ever sign off on.

Drift & degradation over time

Here’s the thing almost nobody outside of data science talks about: AI drifts the same way that language, processes, or product portfolios drift. Gradually. Quietly.

Models don’t stay sharp without maintenance. Policies evolve. Customer context changes. And then you get:

• Rising recontact rates,
• Slowly dipping FCR scores,
• Sentiment trending down month over month.

Organizations that monitor drift proactively see significantly higher long-term ROI than those who “set and forget.” It’s that simple.

Behavior Monitoring Tips for AI Governance Oversight in CX

AI is making decisions, influencing outcomes, and shaping journeys, yet for some reason, companies still aren’t paying enough attention to what goes on behind the scenes. It takes more than a few policies to make AI governance oversight in CX work. You need:

A Multi-Layer Monitoring Model

With AI, problems rarely start where you’d think. If a bot is rude to a customer, it’s not a chat app that’s usually the problem, it’s something underneath. That’s why you need to monitor all the layers:

• Data layer: Here, you’re watching for data freshness, schema changes, versioning of your knowledge base, inconsistent tags across channels, and omni-data alignment across channels. Poor data quality costs companies billions a year, but unified data reduces service cost and churn.
• Model layer: At this level, useful metrics include things like intent accuracy, precision/recall, hallucination rate, and AI drift detection signals like confidence over time. Think of this as your AI’s cognitive health check.
• Behavior layer: Here, you’re looking at escalation rates, human override frequency, low-confidence responses, weird tool-call chains, anomaly scores on tone, sentiment, and word patterns.
• Business layer: This is where you see how AI activity correlates to results like CSAT/NPS scores, re-contact rate, churn levels, cost-per resolution, and so on.

The Right CX Behavior Metrics

If you forced me to pick the non-negotiables, it’d be these:

• Hallucination rate (and how often humans correct it)
• Empathy and politeness scores
• Sentiment swings inside a single conversation
• FCR delta pre- and post-AI deployment
• Human override and escalation rates
• Percentage of interactions where the AI breaks policy
• Cost-per-resolution

If you only track “containment” or “deflection,” you’re not monitoring AI properly.

A Holistic Approach to Observability

The teams doing this well have one thing in common: end-to-end traces that show the whole story.

A trace that looks like this: Prompt → Context → Retrieved documents → Tool calls → Model output → Actions → Customer response → Feedback signal

If you can’t replay an interaction like a black box recording, you can’t meaningfully audit it, and auditing is core to AI ethics and governance, especially with regulations tightening.

You also need:

• Replayable transcripts
• Decision graphs
• Versioned datasets
• Source attribution
• Logs that a regulator could read without laughing

If your logs only say “API call succeeded,” you’re not looking deep enough.

Alerting Design & Behavior SLOs

Most orgs have SLOs for uptime. Great. Now add SLOs for behavior, that’s where AI governance oversight grows up.

A few examples:

• “Fewer than 1 in 500 interactions require a formal apology due to an AI behavior issue.”
• “0 instances of PII in AI-generated responses.”
• “No more than X% of high-risk flows handled without human validation.”

Alerts should trigger on things like:

• Sharp drops in sentiment
• Spikes in human overrides
• Unusual tool-call behavior (especially in agentic systems)
• Data access that doesn’t match the pattern (teams/slack bots can be wild here)

Instrumentation by Design (CI/CD)

If your monitoring is an afterthought, your AI will behave like an afterthought.

Good teams bake behavior tests into CI/CD:

• Regression suites for prompts and RAG pipelines
• Sanity checks for tone and policy alignment
• Automatic drift tests
• Sandbox simulations (Salesforce’s “everse” idea is a great emerging model)
• And historical replay of real conversations

If you wouldn’t deploy a major code change without tests, why would you deploy an AI model that rewrites emails, updates CRM records, or nudges refund decisions?

AI Governance Oversight: Behavior Guardrails

Monitoring AI behavior is great, controlling it is better.

Behavior guardrails are a part of AI governance oversight that transform AI from a clever experiment into something you can trust in a live customer environment.

Let’s start with some obvious guardrail types:

• Prompt & reasoning guardrails: You’d be amazed how much chaos disappears when the system is told: “If unsure, escalate.” Or “When conflicted sources exist, ask for human review.”
• Policy guardrails Encode the rules that matter most: refunds, hardship cases, financial decisions, vulnerable customers. AI should never improvise here. Ever.
• Response filters: We’re talking toxicity, bias, PII detection, brand-voice checks, the things you hope you’ll never need, but you feel sick the moment you realize you didn’t set them up.
• Action limits Agentic AI is powerful, but it needs clear boundaries. Limits like maximum refund amounts or which CRM fields it can access matter. Microsoft, Salesforce, and Genesys all call this “structured autonomy”, so freedom in a very safe box.
• RAG governance guardrails: If you’re using retrieval-augmented generation, you have to govern the source material. Versioned KBs. Chunking rules. Off-limits documents.
  Use connectors (like Model Context Protocol-style tools) that enforce: “Use only verified, compliant content. Nothing else.”

The Automation / Autonomy Fit Matrix

The other part of the puzzle here (aside from setting up guardrails), is getting the human AI balance right. Before any AI touches anything customer-facing, map your flows into three buckets:

• Low-risk, high-volume: FAQs, order status, password resets, shipping updates. This is where automation should thrive.
• Medium-risk: Straightforward refunds, address changes, simple loyalty adjustments. Great fit for AI + guardrails + a human-on-the-loop to catch outliers.
• High-risk / irreversible: Hardship claims. Complaints with legal implications. Anything involving vulnerable customers. Here, AI is an assistant, not a decision-maker.

To keep these AI governance oversight boundaries solid, implement a kill-switch strategy that includes when to turn off an agent, pause a queue or workflow, or freeze updates to avoid further damage.

The Role of Humans in AI Governance Oversight

There’s still this strange myth floating around that the endgame of AI in CX is “no humans required.” I genuinely don’t know where that came from. Anyone who’s watched a real customer interaction knows exactly how naive that is. AI is remarkable at scale and speed, but when a conversation gets emotional or ambiguous or ethically tricky, it still just acts like software. That’s all it is.

AI governance oversight in CX still needs humans, specifically:

• Humans-in-the-loop (HITL): Any high-risk decision should get a human’s eyes first. Always. HITL isn’t slow. It’s safe. Good AI behavior monitoring will tell you exactly where HITL is mandatory: wherever the AI hesitates, contradicts itself, or hits a confidence threshold you wouldn’t bet your job on.
• Human-on-the-loop (HOTL): Here, the human doesn’t touch everything; they watch the system, the trends, and the anomalies. They’re basically the flight controller. HOTL teams look at anomaly clusters, rising override rates, sentiment dips, and the subtle cues that tell you drift is beginning. They’re the early-warning system that no model can replace.
• Hybrid CX models: We know now that the goal isn’t to replace humans. It’s to let humans handle the moments where trust is earned and let AI tidy up everything that doesn’t require emotional intelligence. Stop striving for an “automate everything” goal.

Another key thing? Training humans to supervise AI. You can build the best monitoring stack in the world, but if your agents and team leads don’t understand what the dashboards mean, it’s pointless.

Humans need training on:

• How to read drift signals
• How to flag bias or tone issues
• How to escalate a behavior problem
• How to give structured feedback
• And how to use collaboration-embedded ai assistants without assuming they’re always right

Embedding AI Governance Oversight into Continuous Improvement

AI behaves like a living system. It evolves, it picks up quirks, it develops strange habits based on whatever data you fed it last week. If you don’t check in regularly, it’ll wander off into the digital woods and start making decisions nobody signed off on.

That’s why continuous improvement isn’t a ceremony; it’s self-defense. Without it, AI governance oversight becomes a rear-view mirror instead of an early-warning system.

Commit to:

• Continuous testing & red-teaming: If you’ve never run a red-team session on your CX AI, you’re genuinely missing out on one of the fastest ways to uncover the weird stuff your model does when nobody’s watching. Red-teamers will shove borderline prompts at the system, try to inject malicious instructions, and stress-test policy boundaries, to show you gaps before they turn into real problems.
• Tying monitoring to predictive CX & customer feedback: If you want to know whether your AI changes are helping or quietly sabotaging the customer journey, connect them to your predictive KPIs. Watch what happens to CSAT, NPS, predicted churn scores, likelihood-to-repurchase, and customer effort.
• Knowledge base integrity review: 80% of hallucinations probably start in the knowledge base, not the model. One policy update slips through without review, or a well-meaning team member rewrites an FAQ with different wording, and suddenly your AI is making decisions based on contradictory inputs. Regular KB governance should become as normal as code review.
• Data quality & lineage checks: The model can only behave as well as the data it’s seeing, and CX data is notoriously chaotic: different teams, different taxonomies, different CRMs duct-taped together over several years. To keep AI honest: consolidate profiles into a CDP with one “golden record,” enforce schemas, and define lineage so you can actually answer, “Where did this value come from?”

The organizations doing this well treat AI like any other adaptable system. They run a full loop: Monitor → Detect → Diagnose → Fix → Test → Redeploy → Report. Simple as that.

AI Governance Oversight: The Only Way to Scale CX AI Responsibly

If there’s one thing that’s become clear while watching CX teams wrestle with AI over the past two years, it’s this: the technology isn’t the hard part. The model quality, the workflows, and the integrations all come with challenges, but they’re solvable.

What really decides whether AI becomes a competitive advantage or a reputational hazard is how well you understand its behavior once it’s loose in the world.

That’s why AI governance oversight, AI behavior monitoring, guardrails, kill switches, and human review models matter more than whatever amazing feature your vendor demoed last month. Those safeguards are what keep the AI aligned with your policies, your ethics, your brand personality, and, frankly, your customers’ tolerance levels.

You can’t prevent every wobble. CX is too complicated, and AI is too adaptive for that illusion. But you can design a system that tells you the moment your AI starts drifting, long before the customer feels the fallout.

CX is just going to keep evolving. Are you ready to reap the rewards without the risks? Read our guide to AI and Automation in Customer Experience.

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.

Why Traditional Customer Experience KPIs Metrics Aren’t Enough 

Many organisations are still leaning on traditional metrics like Net Promoter Score (NPS), Customer Satisfaction (CSAT) or First Contact Resolution (FCR) as their primary customer experience KPIs. But in an era of real-time and predictive operations, these indicators increasingly miss the moment when you could intervene and influence. Instead of measuring solely speed and volume, you should be tracking trust, outcomes and lifetime value. That means you need a CX measurement framework not simply anchored in “how did we do” but in “what’s going to happen next”. 

What Predictive CX Looks Like 

Predictive customer experience means using real-time insights, AI-driven modelling, customer journey analytics and automation triggers to anticipate needs, identify friction before it surfaces, and act in the moment.  

“Organisations that forecast needs, reduce effort proactively and engage before a customer raises a ticket gain a significant advantage over their competitors.”

So, what benchmarks define ‘real-time readiness’? Below are three categories every CX leader should be keeping their eye on: 

Friction-avoidance

Average Customer Effort Score (CES) trending lower (i.e., less effort) compared to peers, and a high ratio of issues resolved before they require a customer-initiated interaction. For automation-enabled contact centres, fewer inbound contacts per thousand customers in predictive models signals maturity. 

Predictive action adoption 

Percentage of customer interactions where CX AI tools triggered a next-best-action or intervention, versus purely reactive responses. For example, what proportion of service cases were flagged by predictive routing or agent assist automation?  

Business-outcome orientation 

Conversion, retention or revenue increases tied to CX automation and predictive interventions. For instance, tracking how many customers avoid churn due to predictive outreach, or how many upsell opportunities were initiated by predictive insight rather than campaign alone. The shift from “ticket closed” to “customer retained or expanded” marks genuine progress. 

Assessing Your Position 

Start by mapping your current set of customer experience metrics and overlaying them with these questions: 

• What share of your issues are discovered proactively versus via customer complaint? 

• How much of your customer journey analytics feed into automated triggers or next-best-action flows? 

• When you implement AI in customer experience, how do you measure its actual impact on retention, lifetime value or cost-to-serve? 

Then, benchmark against industry indicators. Top performers usually maintain CSAT in the high-70s, CES (on a 1-5 scale) around 1.5, and above industry retention rates significantly better than average. Use those numbers as directional, then identify your gaps. The impact isn’t abstract – companies leading in CX grow revenue 41% faster than peers. 

How to Scale Your Predictive CX AI

Align data and architecture: Without a unified data backbone and real-time access, predictive CX won’t scale. Many organisations are now re-architecting with vendor-neutral layers to power AI-driven experiences.  

Define measurement frameworks tied to outcomes: Shift from “calls answered in 30 seconds” to “calls prevented, or revenue preserved via predictive intervention.” 

Pilot and scale automation and AI use cases: Identify lean use-cases (for example, churn-risk triggers or next-best-action in onboarding). Track the ratio of automated/predictive interactions versus baseline. 

Iterate on the journey analytics: Deploy customer journey analytics to detect drop-off points, then layer in predictive signals to intervene. 

Monitor, optimise and embed learning loops: Predictive models degrade over time if not retrained. Embed feedback loops so your automation and AI refine themselves. This transforms CX automation from a cost-cutting exercise into a catalyst for growth. 

Turning Insight into Competitive Advantage 

With customer expectations sky-high, the brands that are winning are those operating with foresight, not hindsight. Leveraging CX AI and establishing clear benchmarks allows you to move from reactive service to proactive experience design.  

Ready to move from reacting to customer issues to predicting them? Read our Ultimate Guide to Automation & AI in CX 

Why Staying Still Hurts Customer Retention 

When companies stick to reactive customer experience strategies, the cost of customer churn begins to mount. Research finds that many customers will walk away after just a single bad experience, so maintaining a high retention rate demands more than just firefighting when problems surface. Reactive support doesn’t just lose customers; it inflates costs with longer calls, repeat issues, and compensation efforts. 

By contrast, a proactive model intercepts issues before escalation, reducing the amount of support required and increasing the efficiency with which issues are resolved. In fact, organizations that adopt a proactive support strategy see ticket volumes drop by 20–30% over 12 months, and 25% lower support operating costs. 

The Cost of Reactive Customer Service 

When your business’ customer service strategy remains reactive, the hidden costs include: 

Rising churn: Customers who feel unsupported or undervalued will quietly drift away. 

Lost lifetime value: Retention is cheaper than acquisition; every percentage drop-in retention rate is revenue left on the table. 

Higher support costs: Fixing problems after they’ve occurred is often more expensive than prevention. 

Reputation damage: Negative experiences spread; poor service becomes part of your brand story. 

Innovation stagnation: A reactive model focuses on “putting out fires” rather than designing better journeys. 

Why Predictive CX Pays Off 

A proactive approach to CX means anticipating needs, spotting friction points ahead of time, and intervening early.  

Anwesha Ray, CX Today:

“Stay one step ahead of your customers’ needs … rather than waiting for them to contact you.” 

That kind of mindset shift matters for three inter-linked metrics: customer satisfaction, customer retention, and customer retention rate. 

1. By anticipating and preventing friction, you keep customers happier (higher satisfaction). 
2. Happier customers are more likely to stay (higher retention). 
3. Maintaining a higher retention rate reduces the churn cost and boosts lifetime value. 

When companies move from reactive to proactive service, they see fewer support escalations, lower costs, and stronger brand reputation. 

How AI Predicts Customer Needs 

Artificial intelligence has become the backbone of modern predictive customer experience. By analysing patterns in customer behaviour, sentiment, and interaction history, AI enables brands to anticipate issues before they arise. Predictive CX analytics can identify when a user is likely to churn, when a product might fail, or when satisfaction levels begin to drop – allowing businesses to intervene early with tailored solutions.  

These capabilities not only boost customer satisfaction but also improve customer retention by transforming reactive support into pre-emptive engagement. AI-driven insights give organisations the foresight to act with precision rather than urgency, helping them deliver value faster while reducing the cost of customer support.  

Implementing Predictive CX

Implementing predictive customer experience doesn’t mean starting over – it’s about improving your strategy and mindset. Here’s how to make the change, step by step. 

1. Map the customer journey: Identify the key touchpoints where customers are most at risk of frustration. Create a “living” map, not just a static diagram. 
2. Leverage customer data: Use feedback, behaviour analytics and support ticket trends to spot warning signals. That gives you the early warning system you need.  
3. Empower your teams: Equip frontline employees with the tools, metrics and authority to act before escalation. Proactive culture matters.  
4. Embed proactive outreach: Automated reminders, maintenance alerts, and tailor-made suggestions are effective ways to strengthen retention.  
5. Track the right metrics: Beyond support volume or resolution time, monitor rising or falling retention rate, churn cost, customer satisfaction trends. These provide the business case for change. 

Act Now or Pay Later 

If you’re evaluating whether to adopt predictive customer experience, it’s important to keep one hard truth in mind – the cost of inaction isn’t often visible until damage is done. Ignoring the shift from reactive to proactive may preserve the status quo today, but it risks higher costs, lower customer satisfaction, and weaker retention tomorrow. Thankfully, AI can help you predict customer issues before a ticket is ever raised.  

Ready to stop firefighting and start future-proofing your CX?

Check out our Ultimate Guide to AI & Automation in Customer Experience

In its quarterly report on Wednesday, the technology company revealed several high-value investments in its AI products. 

In the earnings call, Cisco emphasized that this rapid growth in AI product adoption indicates a rising demand for secure networking. 

Customer-Centric Strategy 

Over the past year, Cisco’s quarterly results have demonstrated high levels of growth after several previous declines, and it is now reaping the benefits of its increased customer spending and investment. 

This has included various AI products and suites, as well as investments in data centers to support the demands for AI-driven workloads and cloud networking. 

However, the attention has turned towards its customers and their willingness to adopt these products into their workflows. 

Despite this growth in demand, a Cisco study revealed that only a third of companies are certain that their IT infrastructure can safely integrate their AI projects, which Cisco views as favorable for them. 

With its extensive networking portfolio, the company believes it is on track to deliver the critical infrastructure to its customer enterprises, enabling them to adapt to the AI era. 

Modernizing Customer Experience 

In response to the study, Cisco has acknowledged that many companies are still far off from where they’ve been expected to be with AI. 

Charles Robbins, CEO and Chairman at Cisco, recognized the readiness gap between planning and execution when it came to adopting AI. 

He said:

“We know many customers still have a lot of work to do to ensure they have the modern, scalable, secure networking infrastructure to support their AI goals.” 

Cisco has already begun its move toward a modernized customer experience through various upgrades and expansions, allowing for simpler large-scale AI deployments. 

This has included its global network and infrastructure upgrades, allowing Cisco to enhance its enterprise switching, routing, and Wi-Fi to conduct large-scale AI and data-intensive workloads with fast, scalable, and secure performance. 

From this, Cisco expects its enterprise customers to switch from legacy networking equipment to its newer systems, collectively spending billions as part of its multiyear, multibillion-dollar refresh opportunity. 

With global data expansion, Cisco has established numerous regional data centers worldwide, as well as a European customer-based sovereign critical infrastructure portfolio, focusing on a global scale-up with region-focused deployments. 

By supplying software and cloud-native transformation, customer enterprises can also receive automated network surveillance and deliver secure, scalable customer experiences. 

In addition, Cisco offers end-to-end security integrated into the network, supporting modernized infrastructure for reliable and capable traffic pattern management. 

Workloads with Agentic AI 

Cisco’s earnings call reported a surge in agentic AI activity, with the number of queries through agentic AI measuring at 25x higher in network traffic than chatbots. 

And demand for AI has increased with it, with Cisco expecting AI infrastructure to generate $3BN in revenue for fiscal year 2026. 

A contributing factor is the AI workloads needing the necessary models and infrastructure to process locally. To support this, Cisco announced the release of its Unified Edge last week, as part of its strategy to process AI at a speedier and secure level. 

This platform offers integration for compute, networking, and storage into one system, enabling enterprises to receive real-time predictions and decisions for secure AI management. 

Another recent release is the Cisco Data Fabric architecture, which allows for the unification and management of various machine data sources, enabling companies to create more innovative AI models, adding to Cisco’s value when it comes to technology investments. 

Cisco Webex Winter 2025

Cisco has also published its Webex Winter 2025 press release, detailing its recent updates in CX technologies. 

Some key results from the season include: AI translation capabilities now expanding to 120 languages for meeting summaries; its regional cloud infrastructure locations such as the UK, Saudi Arabia, South Africa, and the UAE; a 3D workspace designer for visual blueprints; and AI Assistant for Calling for live and post-meeting summaries. 

These help to enable higher productivity levels, improve global coverage, and drive flexible working systems, with Webex allowing customers to use meeting rooms, calls, and contact center through one platform. 

However, not all these features are available for deployment as of yet. 

In conversation with CX Today, Tim Banting, Head of Research at Techtelligence, discussed Cisco’s decision to strengthen its overall CX stack across AI, global scale, and device flexibility. 

He said, “The move aligns with current Techtelligence buying-intent data showing a 19% rise in enterprise research around UC productivity and automation, involving more than 29,000 companies actively investigating process and workflow automation in communications suites. 

“However, Cisco faces an execution challenge. Several key AI and automation capabilities remain in the “coming soon” category, creating a perception gap in a market that rewards immediacy and credibility. 

“Techtelligence data shows that buyers are rewarding vendors delivering deployable automation and measurable risk controls now – not future roadmap promises. 

He added: “For CX buyers, the practical value lies in features that are globally available and compliance-ready today. The platform consolidation trend is undeniable.  

“Cisco’s success will hinge on whether it can deliver AI responsibly, at scale, and ahead of rivals who are already reshaping perception around “secure AI collaboration.” 

Cisco Key Earnings Results

After enterprise customers’ strong demand for its AI products, Cisco has risen above estimates for the quarter. 

• Cisco’s revenue is up to $14.9BN, increasing 8% year-over-year  
• Its product orders are up 13% year-over-year, with growth across all markets and geographies 
• AI infrastructures currently stand at $1.3BN 
• Service revenue increased by approximately 2% 
• Product revenue increased by approximately 10%

When “Just Faster” Becomes “Just Worse” 

Historically, lowering average handle time was the gold standard of call centre productivity: shorter calls meant more handled interactions, and more handled interactions (so the logic went) meant lower labor costs. For years, contact centre operations focused on driving down talk time, hold time and after-call work (ACW) in pursuit of efficiency. But the mantra of “faster = better” carries a risk: push agents to cut corners, and you risk eroding first contact resolution, increasing repeat calls and ultimately weakening customer satisfaction. 

Rebekah Carter, CX Today:

“Every contact centre leader wants to reduce average handling time. Yet business leaders are searching for smarter ways to reduce AHT without compromising on customer, employee and business outcomes.”

The Real Cost of Inflated AHT 

So, what are the hidden costs linked to excessive handle time? 

Labor inefficiency: Every extra minute an agent spends on a call means fewer handled contacts per shift. With staffing as your largest cost component, higher AHT directly inflates your cost per contact.  

Customer churn and loyalty risk: Customers don’t just want faster; they want right the first time. If an interaction drags, they’re more likely to abandon the call, repeat the issue later or switch providers. 

Agent burnout and attrition: When agents are under pressure to hit aggressive AHT targets, the quality of work suffers, stress rises and turnover increases. That adds hiring and training costs, and often higher AHT as new hires get to grips with systems. 

Opportunity cost: A contact centre defined purely by service becomes trapped in a cycle of cost-control, missing opportunities to evolve into a revenue-oriented organisation. 

Why Agentic AI Matters 

AI is no longer a peripheral add-on to call centre operations – it’s fast becoming the backbone of smarter call centre productivity and customer experience (CX) strategy. Modern contact centre AI solutions help organisations move beyond tactical efficiency gains to achieve measurable improvements in service quality, speed, and scalability.  

Here’s how AI can optimise your CX operation: 

Real-time guidance for agents: AI listens to live interactions and offers contextual prompts, next-best actions, and dynamic scripting to help agents resolve queries faster and more accurately. 

Automated summarisation and after-call work reduction: Intelligent summarisation tools automatically generate notes and CRM updates, cutting minutes from post-call wrap-up time. 

Knowledge surfacing and retrieval: AI can instantly fetch relevant policies, product details, or past interaction data, reducing search time and cognitive load during customer calls. 

Intent detection and smart routing: Calls and chats are triaged by AI before they reach an agent, ensuring that each issue lands with the right person or bot on the first try – driving down average handle time (AHT) and repeat contacts. 

Quality assurance at scale: Instead of manual auditing, AI analyses 100% of interactions for compliance, tone, and satisfaction cues, helping leaders identify friction points that extend handle time. 

Proactive coaching and training: AI-driven analytics flag skill gaps, coach agents in real time, and accelerate onboarding for new hires, which is key to long-term call centre cost reduction. 

Predictive workload management: Forecasting algorithms predict call spikes and suggest optimal staffing or automation levels to sustain consistent call centre productivity. 

Minimising Average Handle Time Costs  

Minimising hidden AHT costs without sacrificing service quality requires a smarter, more holistic approach to performance management. Rather than fixating solely on average handle time, contact centres should reframe success metrics to include first-contact resolution, time-to-resolution, and customer effort – giving a fuller picture of efficiency and satisfaction.  

Empowering agents with context is key – when they have access to unified cross-channel customer data and AI-driven recommendations, they spend less time searching for information and more time solving problems. AI-powered agent assist tools can further streamline operations by handling after-call work, drafting responses, summarising interactions, and routing issues intelligently.  

Optimising training and onboarding through AI-driven coaching can also reduce the lengthy “high AHT” period that typically accompanies new hires. Finally, maintaining a balance between speed and quality is essential; incentives and goals should encourage thorough, customer-centric resolutions, ensuring that average handle time remains a useful measure, but not the only one used to drive great customer experiences. 

The Smarter Path to Sustainable Efficiency

The conversation around average handle time (AHT) needs a refresh. It’s not enough to simply shave seconds off a call without improving the service your agents offer. The hidden costs of high AHT – efficiency loss, poorer CX, agent attrition – are real. But by embracing contact centre AI and the next gen of agentic AI, you can reduce call centre costs, increase call centre customer satisfaction and raise call centre agent productivity without compromise.  

Efficiency is evolving — is your contact centre keeping up?

Find Out in Our Ultimate Guide to AI & Automation in CX 

This new partnership will have both companies co-developing AI-focused strategies to market to global customers. 

This partnership will also allow both to expand AI productivity on their respective platforms. 

ServiceNow and NTT DATA have begun their joint plans to market AI-driven solutions, including developing and selling these products to transform how AI is used in the workplace. 

These are set to include various enterprises, commercial companies, and mid-market segments. 

This new strategy emphasizes the companies’ previous partnerships, focusing on their commitments to advise other enterprises to adopt and execute AI into their businesses. 

ServiceNow also intends to use the expanded partnership to place NTT DATA as a delivery partner in its services to guide customers in safely deploying AI-powered automations and enhance operational efficiency. 

Amit Zavery, President, COO, and CPO at ServiceNow, highlighted how this new partnership development will improve AI across businesses, stating:

“ServiceNow and NTT DATA are expanding access to AI-powered automation across any industry and any geography to achieve measurable business impact for organizations at every stage of the AI journey, 

“Together, we’re transforming how the world’s leading enterprises operate, making work simpler, smarter, and more resilient with the ServiceNow AI Platform.”

NTT DATA will be using the partnership to escalate the ServiceNow AI platform to its business to improve its levels of productivity, efficiency, and make improvements to its customer experience across the enterprise, by adopting its AI agents and Global Business Services. 

Abhijit Dubey, President, CEO, and Chief AI Officer at NTT DATA, emphasizes how the partnership can benefit both NTT DATA and other enterprises. He said:

“Expanding our partnership with ServiceNow is a key milestone in our mission to build the world’s leading AI-native services company,”  

“By combining ServiceNow’s agentic AI platform with NTT DATA’s global delivery scale and industry expertise, we’re enabling enterprises to accelerate innovation, enhance productivity, and achieve sustainable growth.” 

Who is NTT DATA?

The technology services company provides enterprises and governments with responsible innovations for cloud, AI, security, data centers, connectivity, and application services. 

This latest partnership allows NTT DATA to access ServiceNow’s generative AI and workflow automation, enabling it to supply improved offerings to its customers by integrating more complex solutions, positioning itself as a leader of enterprise AI transformation within the industry. 

NTT DATA can also use this opportunity to expand its market reach by co-developing AI tools with a well-established company, especially in the US and Europe, where ServiceNow holds strong brand recognition. 

What This Partnership Means

This partnership expansion reflects the increasing intensification of AI-led transformation investments amongst current major vendors. 

The collaboration allows both companies to become more central in their position as leaders in enterprise software strategy development, highlighting to other vendors where they stand to build better relationships instead of developing capabilities independently. 

By fully aligning operations such as products and delivery services, more companies can join the competition and adapt to trends cost-effectively. 

Of course, in the year 2025, when we’re discussing the latest tech phenomenon, we can only really be referring to AI.   

And, like the long list of tech advancements that have come before it, AI isn’t delivering as advertised.   

Indeed, for many contact centers, the reality has been a tangle of tools, dashboards, and alerts that leave agents feeling more burdened than empowered.  

However, despite these shortcomings, much of the customer service and experience sector appears to be doubling down on AI.  

This leaves an opening for companies that are brave and creative enough with their AI strategies to differentiate themselves in a crowded marketplace.   

One such company is Graia, which focuses not on replacing agents but on helping them do their best work.  

“When you’re redesigning the customer experience, it’s about how humans and technology coexist,” says Sahil Rekhi, CRO of Graia.  

“Our vision has always been to give agents ownership of the customer relationship, while providing the right technology to help them solve problems faster and more effectively.” 

It’s a philosophy that may strike a chord with many contact center leaders today.  

With agent turnover at record highs and tool fatigue spreading, organizations are beginning to realize that true efficiency comes not from automating people out of the process, but from augmenting human capability.  

The Problem with Current AI Tools  

The contact center industry has no shortage of AI solutions.  

From call transcription to performance analytics, the technology stack has grown dense. Yet many leaders are finding that more technology doesn’t always mean better outcomes.  

Rekhi notes that this “AI overload” often creates more friction than it removes.  

“Companies need to take a holistic view of what they’re trying to achieve. Too often, AI is rolled out as a quick fix – something the board wants to see in action – without a broader change management wrapper around it,” he explains.  

“That’s how you end up with tool fatigue and disengaged agents.”  

Instead, Rekhi argues for a phased, empathetic approach, stating that “AI isn’t a binary switch; it’s a journey over 12, 24, or 36 months. And that journey has to include people.”  

The Graia man believes that this people-first mindset must start with communication. He emphasizes the need for leaders to be transparent about why AI is being adopted and how it benefits employees.  

“There’s a lot of fear out there, agents wondering, ‘Is AI coming for my job?’ The reality is, it’s there to make their roles more valuable.” 

Agent Empowerment Through Empathic AI  

Graia’s platform is built on what Rekhi calls “empathic AI,” tools that simplify the agent’s job rather than complicate it.  

“The first thing we ensure is that the agent owns the interaction,” he says. “Technology should empower them to deliver faster resolutions, with the right context, tone, and empathy.”  

That context awareness is a cornerstone of Graia’s design. Through real-time voice and chat analysis, the platform tracks over 50 different emotional states, offering live guidance to agents on how best to engage.  

If compliance risks arise, it can flag them instantly. If sentiment dips, it nudges the agent toward a more constructive tone.  

“It’s like having a co-pilot that understands both the customer and the conversation,” Rekhi explains.  

“Agents come out of those interactions with a genuine sense of achievement, feeling like they delivered on what they were hired to do.”  

In multilingual environments, that support becomes even more powerful.  

Indeed, Rekhi details an example of Graia’s BPO customers, where agents serve ten or more markets.  

By deploying the vendor’s real-time voice and chat translation, which covers more than 100 languages, agents can deliver seamless service without actually speaking all those languages.  

“That’s where the idea came from: they’re not superhuman, just using Graia,” Rekhi says, referring to one of the company’s tag lines.   

Transforming Retention, Training, and Performance  

Another side effect of the influx of AI tools for agents has been a greater emphasis being placed upon the agent experience, which has emerged as a top driver of customer satisfaction.  

Rekhi believes that AI can actually play a pivotal role in helping to improve the agent experience, particularly when it comes to retention and training.  

“Happier agents mean happier customers, better revenue, and less churn. That’s proven in this industry,” he says.  

By using AI to automatically surface relevant knowledge, suggest next steps, and summarize calls, Graia helps reduce onboarding time and training overhead, as Rekhi explains:  

“No agent needs to be a subject matter expert anymore.” 

“They just need to know how to use the platform. AI takes care of the rest – tone, brand guidance, compliance prompts – all in real time.”  

For new hires, that means faster confidence and fewer early dropouts. For managers, it means consistent service quality without micromanagement.  

Rekhi adds: “It saves them precious time they can use to focus on high-value engagements, which ultimately drives better results for the business.”  

Beyond onboarding, Graia’s analytics feeds continuous improvement. By analyzing past interactions, both human and automated, the system identifies where agents typically need help, then refines training materials accordingly.  

Rekhi gives the example of a major automotive client using Graia to analyze dealership and roadside assistance calls, automatically generating training content and knowledge articles based on real customer interactions.  

Human Potential, AI-Assisted  

As AI becomes a fixture of the modern contact center, Rekhi believes the winning formula lies in alignment, transparency, and consolidation.  

“Try to bring everything behind a single agent desktop,” he advises. “That one-time pain of integration is worth it. The ongoing pain of swivel-chair setups and attrition isn’t.”  

For Rekhi, the goal isn’t to create superhuman agents; it’s to make ordinary agents feel capable, confident, and supported:  

“It’s about supercharging the employee, not replacing them. Companies that take that view will see AI adoption that truly sticks.”   

 

You can learn more about Graia’s approach to empathic AI by reading this article.   

You can also discover the company’s full suite of services and solutions by visiting the website today. 

Companies that once viewed automation as cost-cutting now see it as a growth catalyst. The numbers speak for themselves: AI-enabled contact centres are reducing handling times, boosting efficiency, and driving customer satisfaction (CSAT) to record highs. That means stronger loyalty from your customers and measurable impact for your company’s bottom line.

This guide will help you understand:

• What Do AI and Automation in CX Really Mean?
• Why Reactive CX No Longer Works
• Choosing the Right CX AI Provider
• How to Adopt AI Into Your Business
• Getting Real Results from AI & Automation
• AI & Automation Trends for 2026
• AI Support With a Human Touch
• FAQs
• Your AI & Automation Journey

What Does AI and in CX Really Mean?

AI and automation in customer experience means a lot more than simple scripted chatbots. Today’s AI & automation toolkit includes technologies that learn, reason and orchestrate complex workflows to boost both efficiency and human connection with your customers:

Generative AI: Uses artificial intelligence to create original, personalized content that make customer interactions feel more human and engaging. It helps businesses reply naturally, recommend products, and deliver faster, more relevant experiences.

Agentic AI: Refers to AI systems that can take initiative, make decisions, and perform tasks autonomously to achieve specific goals without constant human direction. This is AI that acts proactively, anticipates customer needs and resolves issues on its own.

Workflow Optimisation: Robotic process automation (RPA), and AI powered summarisation tools streamline repetitive tasks such as data entry, case routing and after-call notes. This frees agents from mundane work, meaning they can focus on the things they do best.

Predictive Customer Insights: Predictive models analyse interaction patterns, sentiment and purchase history to forecast churn risk, recommend the next best action or identify up-sell opportunities. Traditional call centres wait for customers to raise their hands; proactive CX flips that script and predict issues before a ticket is ever raised.

Why Reactive CX No Longer Works

The days of waiting for customers to raise a support ticket are thankfully over. Reactive CX strategies aren’t just outdated – they’re risky. Early warning signals like declining engagement or negative sentiment can now be detected long before a complaint lands. Automation can then send a helpful update, initiate a refund or route the customer to a specialist. Even simple notifications, such as a delivery delay alert, can defuse frustration and build trust between you and your customers.

The Benefits of Proactive Engagement

Proactive CX has a range of benefits. It’s been shown to reduce inbound volumes, lower cost per contact and strengthen customer loyalty. Agents spend less time on repetitive troubleshooting and more time on meaningful conversations. Automating just 20 percent of support tickets can increase repeat purchase rates by eight points, showing that small automations can yield significant returns.

Avoiding Over Automation

It’s easy to get over-excited about the potential for AI automation, but overzealous deflection can push high value customers into self-service loops and miss valuable cross-sell opportunities.

Start by segmenting interactions by value. Automate low complexity tasks, offer hybrid options for mid-value cases and prioritise human agents for high stakes interactions to ensure your customer are getting the best support possible.

Choosing the Right AI Provider

The best CX AI partner isn’t necessarily the one with the flashiest demo, it’s the one that aligns technology with your vision of customer excellence. Look for providers that demonstrate measurable ROI, robust security standards, and a clear track record of success in your industry.

“A reliable CX vendor will offer both scalable infrastructure and human-centred design – ensuring AI tools enhance empathy, not replace it.”

Integration flexibility is critical; prioritise platforms that connect seamlessly with your CRM, analytics, and omnichannel communication stack through open APIs or low-code orchestration.

When comparing vendors, evaluate these four key factors:

Accuracy and adaptability: Assess how often the provider updates its AI models, retrains with new data, and applies techniques like retrieval-augmented generation for grounded responses.

Integration: Confirm the solution can be seamlessly integrated with your existing tools and doesn’t create new data silos.

Transparency and compliance: Check for clear data-handling policies and adherence to privacy regulations like GDPR. This ensures both you and your customer’s data stays safe.

Support and scalability: Ensure the vendor offers training, change-management resources, and scalable architecture that can evolve with your growth.

“Above all, AI should enhance empathy, not erase it. The future of CX isn’t machine-driven – it’s human-led, AI-powered.”

How to Adopt AI Into Your Business

Bringing AI into your business might sound daunting, but with the right strategy, it can become your most powerful growth engine. Follow these steps when planning your AI implementation:

Define clear goals: Establish success metrics before deployment (e.g., CSAT, AHT, FCR). Track baselines and measure change over time.

Start with high-impact use cases: Pilot automation on frequent, low complexity tasks such as FAQs or routing. Quick wins build momentum and confidence.

Keep knowledge bases fresh: RAG and generative AI depend on accurate data. Outdated content undermines trust and increases hallucination risk.

Ensure seamless hand offs: Use unified desktops and orchestration tools so AI and human agents share context. Customers should never have to repeat information.

Invest in change management: Train staff to understand AI tools as allies. Address fears about automation replacing jobs and emphasise how AI enhances empathy and creativity.

Prioritise security and compliance: Choose vendors that meet GDPR and industry specific standards and ensure transparent handling of customer data.

Mapping AI Technologies to the Customer Journey

AI isn’t just transforming customer interactions – it’s reshaping the entire journey from first contact to long-term loyalty.

Here’s how key AI technologies align with each stage of the customer experience:

Onboarding

Chatbots and self-service portals guide registration and answer simple questions. Low code automation can integrate account creation with back-end systems.

Growth and Loyalty

Personalisation engines and predictive analytics identify upsell opportunities and churn risk, triggering timely outreach. Proactive, AI driven notifications build trust and loyalty.

Support and Recovery

Technologies such as agent-assist and sentiment analysis resolve complex issues quickly whilst generative and agentic AI bots provide accurate answers grounded in verified data.

Getting Real Results from AI & Automation

Technology adoption must translate into measurable business outcomes. The following metrics and practices help link AI investments to CX impact:

Performance Metrics

Customer Satisfaction (CSAT)/Net Promoter Score (NPS): AI enabled contact centres report CSAT improvements of around 37 percent and even revenue increases of 30 percent.

Average Handle Time (AHT)/First Contact Resolution (FCR): Automation slashes AHT by 12%, surfacing relevant information and routing tasks efficiently. Gartner projects that conversational AI in contact centres  will cut agent labour costs by $80 billion by 2026. Read our guide on reducing Average Handle Time using AI here.

Agent Retention and Productivity: Offloading repetitive tasks to AI boosts agent efficiency and reduces staff turnover. Studies show that generative AI assistants  increase agent productivity by 14 percent on average.

Operational Cost Reduction: Companies using generative AI report savings across the board. Automating a portion of support tickets can reduce costs per contact, while AI powered systems have led to jumps in customer satisfaction and increases in retention.

AI & Automation Trends for 2026

The future of AI and automation in customer experience (CX) is being shaped by five major trends that will redefine how businesses operate and engage with customers.

Agentic AI Systems

CX is shifting from reactive automation to autonomous orchestration, driven by agentic AI that can independently analyse data, make decisions, and execute customer-facing actions in real time. These AI systems no longer wait for human prompts – they proactively identify issues, coordinate across tools, and deliver outcomes without manual intervention.

By 2026, leaders will view AI not just as a digital assistant, but as a trusted operations partner capable of resolving complex service requests, personalising offers, and continuously optimising journeys at scale.

AI-Driven Orchestration Models

Rather than adding automation into legacy workflows, enterprises are re-architecting CX around AI as the operating system for decision-making and coordination. These orchestration models let AI route conversations, prioritise tickets, trigger fulfilment, and align marketing, sales, and support into one adaptive system.

 Ethical & Trust-Centred AI

As AI takes on more customer-facing responsibility, trust is becoming the currency of great CX. Brands must ensure algorithms are transparent, explainable, and free from bias, especially in service recovery, pricing, or claims processes. By 2026, organisations that prioritise AI will win customer confidence and protect long-term brand equity.

Human + AI Collaboration

Despite the rise of automation, the human role is CX becoming more strategic than ever. AI will handle scale, speed, and data-driven precision, while human agents focus on emotional intelligence, complex judgment, and creative problem-solving.

By 2026, hybrid teams – where humans supervise, train, and collaborate with AI systems – will define the gold standard of experience delivery, blending efficiency with empathy in every interaction.

AI Support with a Human Touch

Agent-assist platforms act as intelligent, real-time copilots, helping customer service teams work faster, think clearer, and connect more deeply. These systems free agents from repetitive tasks and cognitive overload, allowing them to focus on what they do best.

Real-Time Transcription and Analysis

Speech-to-text tools capture every nuance of a conversation while sentiment analysis detects emotion and intent. This immediate feedback loop helps agents adapt their tone, pacing, and strategy mid-conversation – turning reactive exchanges into proactive, empathetic service moments.

Knowledge Retrieval

Instead of searching through endless databases or documents, the AI surfaces the most relevant FAQs, product information, or policy references in real time. This instant access not only boosts accuracy and speed but also ensures customers receive consistent, up-to-date guidance.

Intelligent Responses and Next-Step Suggestions

AI-generated replies and recommended actions act as starting points that agents can review and personalize. This results in faster resolution times, a unified brand voice across customer communications, and more room for agents to bring their own judgment and warmth into every message.

What Agent-Assist Can do For Your Business

Agent assist is far from a fad – companies that deploy agent assist solutions are seeing measurable results. According to Microsoft research reviewing AI agents across sectors, organisations reported a 12% reduction in average handling time. Additionally, 10% of cases that typically required colleague collaboration were resolved independently with the help of virtual assistants. Together, these improvements drive lower costs, higher morale, and a better customer experience.

Your AI & Automation Journey

AI and automation are not about replacing people; they’re about amplifying human potential. When thoughtfully implemented, technologies like conversational AI, predictive analytics and low code orchestration enable personalisation at scale, proactive engagement and emotionally intelligent service.

To succeed:

1. Define clear goals and metrics.
2. Select technologies aligned with your CX strategy.
3. Keep data accurate and knowledge bases current.
4. Empower agents with AI rather than replacing them.

By following these principles, organisations can transform customer experience from reactive service into proactive, data driven relationships that deliver real business impact. The future of CX belongs to companies that embrace AI and automation in customer support while keeping the human at the centre of every interaction.

FAQs

How Does AI Improve Customer Experience?

AI enhances CX by personalising interactions, predicting needs and resolving issues faster. For example, AI enabled contact centres reduce average handling time by about 21 percent, boost agent efficiency by 20 percent and raise customer satisfaction by 37 percent.

What’s the Difference Between Generative and Agentic AI?

Generative and agentic AI each play distinct but complementary roles in transforming customer experience. Generative AI focuses on creating content based on learned patterns from data, allowing brands to deliver highly tailored, human-like interactions at scale. Agentic AI, on the other hand, takes this a step further by combining reasoning, decision-making, and autonomous action; it doesn’t just generate responses but proactively executes tasks across systems to resolve customer needs.

Will AI Replace Human Agents?

The recent wave of layoffs in customer experience and support roles suggests that automation is no longer just a theoretical threat – it’s already here. While humans remain part of the customer-service equation, the nature of their work is changing – the routine queries are increasingly being handled by machines, and human agents are being reserved for more complex, nuanced interactions.

How Should Organisations Begin Their AI Journey?

Start with a clear objective and a manageable scope. Pilot AI on high volume, low complexity tasks, measure results and iteratively expand. Maintain a clean knowledge base and choose technologies that integrate easily with your existing systems.

Is it Safe to Trust AI with Customer Data?

Yes – provided vendors demonstrate strong encryption, compliance with standards like GDPR and transparent data handling policies. Choose partners that prioritise security and explain how they use and store data.

With CX Today’s Rhys Fisher, we break down how to phase in native features without losing the specialist tools you rely on.