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.

This feature is designed to further the Amazon Connect Email platform, utilizing built-in capabilities that enable agents to deliver quicker response times. 

The workflow tool is the latest addition to AWS’s email and contact center capabilities. 

Released in November 2024, Amazon Connect Email is an omnichannel support feature that enables customer service agents to respond to and divert customer emails all within the same system as voice and chat, allowing agents to handle customer queries in a single space. 

The AI-enhanced email workflow tool enhances Amazon Connect Email by allowing service agents to expedite email customer service through automation capabilities. 

By utilizing large language models (LLMs), these AI-powered workflows can allow Amazon Connect to analyze emails, detect customer intentions, assess potential risks or complexity from the interaction, and evaluate next steps. 

After analyzing the email, the tool will provide a summary of the customer’s profile and any previous activity with the enterprise, the determined query category, and a brief rundown of the email to help tailor the response accurately. 

This tool also grades the received email based on how confidently it understands the message with Amazon Bedrock API and Claude AI, factoring in clarity, tone, topics, risk assessment, and time sensitivity, while also considering whether the customer is part of any premium packages by retrieving the user’s profile. 

This profile retrieval will include the customer’s current credit score, service level, and contact history to help the tool further evaluate the email score. 

After this, the tool implements a two-step process, where the LLM produces binary outputs for each negative factor it recognizes, using an embedded mathematical function to ensure the calculations align with the scoring evaluation. 

Enterprises can also personalize the scoring framework to fit their needs and determine the number of emails routed to an agent. 

Once a score has been determined, the email will receive either a generated response (if the score is 80 or higher) or be routed to an agent’s inbox for a personalized response (if the score is lower than 80), whilst also providing an explanation detailing how the tool determined the score. 

To simplify future interactions, the tool can automatically generate a case detailing the email and information previously gathered, allowing agents to view conversation history from a single place where email handling has occurred with generative AI.  

Agents can also personalize AI-generated responses to keep human intelligence in the loop and can add AI-powered workflows to Amazon Connect Email via Amazon Bedrock. 

What This Tool Means For Customer Experience 

The Amazon Connect AI-enhanced email workflows allow enterprises to bridge the gap between customer demand and agent availability through automating repetitive email tasks and filtering complex queries to agents, providing customers with human responses and agents with enhanced productivity where needed. 

These AI-powered workflows outrun traditional automation systems by understanding context clues behind interactions, including emotion and characteristic human responses by analyzing a customer’s profile. 

This avoids agents from partaking in repetitive research tasks and instead solving complex, human-based problems to deliver solutions that an LLM cannot solve, resulting in higher levels of meaningful work for the agent. 

This also resolves issues with email backlogs, customer survey responses, and agent exhaustion, driving improvements in customer service. 

The problem usually isn’t the model. It’s the data behind it. When knowledge bases are out of date, fragmented, or inconsistent, even the smartest AI will confidently generate the wrong answer. This is why knowledge base integrity and RAG governance matter more than model size or speed.

The urgency is clear. McKinsey reports that almost all companies are using AI, but only 1% feel they’re at maturity. Many also admit that accuracy and trust are still major barriers. In customer experience, where loyalty is fragile, a single hallucination can trigger churn, compliance headaches, and reputational fallout.

Leading enterprises are starting to treat hallucinations as a governance problem, not a technical one. Without governed data, AI becomes a liability in CX. With it, organizations can build automation that actually strengthens trust.

What Are AI Hallucinations and What Causes Them?

When customer-facing AI goes off-script, it usually isn’t because the model suddenly turned unreliable. AI hallucinations in CX happen when the system fills gaps left by bad or missing data. Picture a bot telling a customer they qualify for same-day refunds when the actual policy is 30 days. That’s not creativity, it’s a broken knowledge base.

Hallucinations tend to creep in when:

• Knowledge bases are outdated or inconsistent, with different “truths” stored across systems.
• Context is missing, for example, an AI forgetting a customer’s purchase history mid-conversation.
• Validation checks are skipped, so the bot never confirms whether the answer is still correct.

The risks aren’t small. 80% of enterprises cite bias, explainability, or trust as barriers to using AI at scale. In CX, inaccuracy quickly turns into churn, complaints, or compliance headaches.

There are proven fixes. Enterprises just need to know what to implement before they go all-in on agentifying the contact center.

The Real-World Impact of AI Hallucinations in CX

The stakes around AI hallucinations in CX translate directly into lost revenue, churn, and regulatory risk. A bot that invents refund rules or misstates eligibility for a benefit doesn’t just frustrate a customer – it creates liability.

Some of the impacts seen across industries:

• Retail: Misleading warranty responses trigger unnecessary refunds and drive shoppers to competitors.
• Public sector: Incorrect entitlement checks leave citizens without services they qualify for.
• Travel: Fabricated policy details can mean denied boarding or stranded passengers.

The financial burden is real. Industry analysts estimate that bad data costs businesses trillions globally each year, and the average cost of a single data-driven error can run into millions once churn and remediation are factored in.

Case studies show the impact, too. Just look at all the stories about ChatGPT, creating fictitious documents for lawyers, or making up statements about teacher actions in education. Every hallucination is a reminder: without knowledge base integrity and RAG governance, automation introduces more risk than reward. With them, AI becomes a growth driver instead of a liability.

Why Hallucinations Are Really a Data Integrity Problem

It’s tempting to think of AI hallucinations in CX as model failures. In reality, they’re usually symptoms of poor data integrity. When the information feeding an AI is out of date, inconsistent, or fragmented, the system will confidently generate the wrong answer.

Knowledge base integrity means more than just storing information. It’s about ensuring accuracy, consistency, and governance across every touchpoint. Without that, CX automation is built on sand.

Common breakdowns include:

• Outdated articles: A policy change goes live, but the bot still cites the old rules.
• Conflicting records: Multiple “truths” for the same customer, leading to contradictory answers.
• Ungoverned logs: Data pulled in without privacy controls, creating compliance exposure.

Some organizations are already proving the value of treating hallucinations as governance problems. Adobe Population Health saved $800,000 annually by enforcing stronger data controls, ensuring agents and AI systems pulled only from validated knowledge sources.

Building the Foundation: Clean, Cohesive Knowledge

Solving AI hallucinations in CX starts with building a solid data foundation. No model, no matter how advanced, can perform reliably without knowledge base integrity. That means every system, from the CRM and contact center platform to the CDP – has to point to the same version of the truth.

A few steps make the difference:

• Unified profiles: Use CDP to connect IDs, preferences, and history across systems. Vodafone recently reported a 30% boost in engagement after investing in unified profiles and data quality.
• Agent-ready records: Golden IDs, schema alignment, and deduplication stop bots from improvising. Service accuracy depends on knowing which record is the right one.
• Data freshness: Expired knowledge is one of the fastest routes to hallucination. Setting SLAs for update frequency ensures AI doesn’t serve answers that are weeks, or years, out of date.
• Governance layers: Microsoft’s Purview DLP and DSPM frameworks, for example, help enforce privacy boundaries and ensure sensitive data is never exposed to customer-facing AI.

Clean, governed data is what allows automation to scale safely. In fact, Gartner notes that automation without unified data pipelines is one of the leading causes of failure in AI deployments.

The lesson is clear: AI only works if the underlying knowledge is accurate and consistent. RAG governance begins not at the model layer, but in how enterprises treat their data.

Choosing Your LLM Carefully: Size Isn’t Everything

When automating CX workflows, the assumption that “bigger means better” often backfires. In fact, purpose-built, smaller language models can outperform broad, heavyweight counterparts, especially when they’re trained for specific customer service tasks.

Here’s what’s working:

• Smaller, tailored models excel at soft-skill evaluations. In contact center hiring, they outperform general-purpose LLMs simply because they understand the nuances of human interaction better.
• Efficiency is a major advantage. Smaller models require fewer computational resources, process faster, and cost less to run, making them ideal for real-time CX workflows.
• They also tend to hallucinate less. Because they’re fine-tuned on targeted data, they stay focused on relevant knowledge and avoid the “overconfident bluffing” larger models can fall into.
• Distillation, teaching a smaller model to mimic a larger “teacher”, is now a common technique. It delivers much of the performance without the infrastructure cost.

Choosing the right model is a strategic decision: smaller, domain-specific models support RAG governance and knowledge base integrity more effectively, without blowing your budget or opening new risks.

RAG Governance: Why Retrieval Can Fail Without It

Retrieval-augmented generation (RAG) has become a go-to strategy for tackling AI hallucinations in CX. Companies like PolyAI are already using RAG to make voice agents check against validated knowledge before replying, cutting down hallucinations dramatically.

Instead of relying only on the model’s training data, RAG pulls answers from a knowledge base in real time. In theory, it keeps responses grounded. In practice, without proper RAG governance, it can still go wrong.

The risks are straightforward:

• If the knowledge base is outdated, RAG just retrieves the wrong answer faster.
• If content is unstructured, like PDFs, duplicate docs, or inconsistent schemas, the model struggles to pull reliable context.
• If version control is missing, customers may get different answers depending on which copy the system accessed.

That’s why knowledge base integrity is critical. Enterprises are starting to use semantic chunking, version-controlled KBs, and graph-RAG approaches to make sure AI agents retrieve the right data, in the right context, every time.

Vendors are also moving quickly. Google Vertex Agent Builder, Microsoft Copilot Studio’s RAG connectors, and open-source projects like Rasa’s extensions are designed to enforce cleaner retrieval pipelines. Companies like Ada are proving that governed RAG can cut down false answers in sensitive workflows like background checks.

RAG is powerful, but without governance, it risks becoming a faster way to spread bad information. Grounding AI in trusted, validated sources, through structured retrieval and strong RAG governance, is the difference between automation that builds trust and automation that erodes it.

The Model Context Protocol for reducing AI hallucination

Even with RAG governance, there’s still a missing piece: how the model itself connects to external tools and data. That’s where the Model Context Protocol (MCP) comes in. MCP is emerging as a standard that formalizes how AI systems request and consume knowledge, adding a layer of compliance and control that CX leaders have been waiting for.

Without MCP, connectors can still pull in unreliable or non-compliant data. With MCP, rules can be enforced before the model ever sees the input. That means:

• Version control: AI agents only access the latest, approved policies.
• Schema validation: Data must meet format and quality checks before it’s used.
• Integrity enforcement: Broken or incomplete records are automatically rejected.

This is particularly relevant in regulated industries. Financial services, healthcare, and the public sector can’t risk AI fabricating eligibility or compliance-related answers. MCP provides a structured way to prove governance at the system level.

Vendors are already moving in this direction. Salesforce’s Agentforce 3 announcement positioned governance and compliance as central to its next-generation agent framework. For CX leaders, MCP could become the difference between AI that “sounds right” and AI that is provably compliant.

Smarter Prompting: Designing Agents to Think in Steps

Even with clean data and strong RAG governance, AI hallucinations in CX can still happen if the model is prompted poorly. The someone asks a question shapes the quality of the answer. That’s where smarter prompting techniques come in.

One of the most effective is chain-of-thought reasoning. Instead of pushing the model to jump straight to an answer, prompts guide it to reason through the steps. For example, in a travel entitlement check, the AI might be told to:

• Confirm eligibility rules.
• Check dates against the customer record.
• Validate exceptions before giving a final response.

This structured approach reduces the chance of the AI skipping logic or inventing details to “sound confident.”

Other strategies include:

• Context restating: Have the model summarize customer inputs before answering, to avoid missing key details.
• Instruction layering: Embedding guard phrases like “If unsure, escalate” directly into prompts.

Better prompting changes how the AI reasons. Combined with knowledge base integrity and retrieval grounding, thoughtful prompt design is one of the simplest, most cost-effective ways to cut hallucinations before they ever reach a customer.

Keeping Humans in the Loop: Where Autonomy Should Stop

AI is getting better at handling customer requests, but it shouldn’t be left to run everything on its own. In CX, the cost of a wrong answer can be far bigger than a frustrated caller. A single AI hallucination in CX around something like a loan decision, a medical entitlement, or a refund policy can create compliance risks and damage trust.

That’s why most successful deployments still keep people in the loop. Routine questions like order status, password resets, and warranty lookups are safe to automate. But when the stakes rise, the system needs a clear off-ramp to a human; no company should try to aim for limitless automation.

There are simple ways to design for this:

• Flagging low-confidence answers so they’re routed to an agent.
• Escalating automatically when rules aren’t clear or when exceptions apply.
• Training models with reinforcement from human feedback so they learn when to stop guessing.

Real-world examples prove the value. Ada’s work with Life360 showed that giving AI responsibility for repetitive queries freed agents to focus on tougher cases. Customers got faster answers when it mattered most, without losing the reassurance of human judgment for sensitive issues.

The lesson is straightforward: automation should extend, not replace, human service.

Guardrail Systems: Preventing AI hallucination

AI can be fast, but it still needs limits. In customer service, those limits are guardrails. They stop automation from giving answers it shouldn’t, even when the data looks clean. Without them, AI hallucinations in CX can slip through and cause real damage.

Guardrails take different forms. Some block responses if the system isn’t confident enough. Others make sure refund rules, discounts, or eligibility checks stay within company policy. Many firms now add filters that catch bias or toxic language before it reaches a customer.

The goal isn’t perfection. It’s layers of protection. If one check misses an error, another is there to catch it. Tucan.ai showed how this works in practice. By adding guardrails to its contract analysis tools, it cut the risk of misinterpreted clauses while still saving clients time.

For CX teams, guardrails aren’t about slowing automation down. They’re about trust. Customers need to know that the answers they get are safe even when they come from a machine.

Testing, Monitoring, and Iterating

AI systems drift. Policies change, data updates, and customer expectations move quickly. Without regular checks, those shifts turn into AI hallucinations in CX.

Strong CX teams treat testing and monitoring as part of daily operations. That means:

• Running “red team” prompts to see how an agent handles edge cases.
• Tracking hallucination rates over time instead of waiting for customer complaints.
• Comparing different prompts or retrieval methods to see which reduces errors.

Enterprises are starting to put this discipline into place. Retell AI cut false positives by 70% through systematic testing and feedback loops. Microsoft and others now offer dashboards that log how models use data, making it easier to spot problems early.

The principle is straightforward. AI is not a one-off project. It’s a system that needs continuous oversight, just like a contact center workforce. Test it, measure it, refine it.

The Future of AI Hallucinations in CX

Customer experience is moving into a new phase. Contact centers are no longer testing basic chatbots. They are rolling out autonomous agents that can manage full interactions, from checking an order to triggering a refund. Microsoft’s Intent Agent, NICE’s CXone Mpower, and Genesys’ AI Studio are early examples of that shift.

The upside is clear: faster service, lower costs, and better coordination across systems. The risk is also higher. A single AI hallucination in CX could mean a compliance breach or a reputational hit that takes years to repair. Regulators are watching closely. The EU AI Act and ISO/IEC 42001 both push for stricter rules on governance, transparency, and accountability.

The market is responding. Salesforce’s move to acquire Convergence.ai and NiCE’s purchase of Cognigy show how major vendors are racing to build platforms where governance is built in, not added later. Enterprises want systems that are safe to scale, not pilots that collapse under risk.

The reality is that hallucinations won’t disappear. Companies will need to learn how to contain them. A strong knowledge base, integrity, tight RAG governance, and frameworks like MCP will differentiate brands that customers trust from those they don’t.

Eliminating AI Hallucinations in CX

The risk of AI hallucinations in CX is not going away. As enterprises scale automation, the cost of a wrong answer grows, whether that’s a compliance breach, a lost customer, or a dent in brand trust.

The good news is that hallucinations are not an unsolvable problem. They’re usually data problems. With a strong knowledge base, integrity, clear RAG governance, and frameworks like MCP to enforce compliance, organizations can keep automation reliable and safe. Guardrails, smarter prompting, and human oversight add further protection.

Together, these measures turn AI from a liability into an asset. Companies that treat governance as central will be able to roll out advanced agents with confidence. Those that don’t risk being left behind.

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

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

The Limitations of Traditional ROI Metrics

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

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

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

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

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

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

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

Why this matters so much now:

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

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

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

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

Beyond Cost Per Resolution: The Scorecard for Agentic AI ROI

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

Step 1: Calculate Total Cost of Ownership (TCO)

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

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

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

Step 2: Identify Tangible Benefits

Every ROI model needs clear financial impact signals:

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

Two examples show this in action:

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

Step 3: Include Intangible Benefits

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

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

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

Step 4: Embed Feedback Loops

Without data, there’s no optimization.

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

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

Step 5: Remember the Governance and Observability Angle

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

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

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

Case Studies: Proof of Resolution-Driven ROI

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

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

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

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

Cost Per Resolution and Agentic AI ROI

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

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

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

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

Below, we evaluate four leading CX AI providers, comparing their data architecture, model performance and integration maturity – key traits for decision-makers comparing customer experience tools.  

What Matters When Choosing Predictive CX AI 

Before looking into specific vendors, it’s useful first to step back and remember what you need to consider. Below are three factors it’s important to keep in mind: 

Data architecture & consolidation – The ability to ingest, unify and analyse multiple touchpoints (web, mobile, contact centre, social) is critical. Without a real-time data layer your predictions will lag. Building a “vendor-neutral data layer” is increasingly becoming a central piece of the CX tech stack.  In fact, whilst 94% of businesses are investing more in AI, only 21% have fully embedded AI into their operations. 

Model performance & actionability – It’s not sufficient to just surface insights; models must forecast behaviour (churn risk, next-best-action) and feed downstream workflows. AI can anticipate customer needs before they arise by analysing vast amounts of customer interaction data.  

Integration maturity & scalability – Even the most sophisticated models mean little without seamless integration. The best customer experience tools plug into existing systems (CRM, contact centre, ERP) and scale across both geographies and channels.  

Building on these criteria, here are four vendors worth serious consideration in 2026.

NICE Ltd. 

NICE has evolved its flagship customer experience platform into an AI-native environment. It now markets its “Enlighten” engine and the “CXone Mpower Orchestrator” which combine real-time analytics with automation across the customer journey. The company claims to deliver predictive routing and next-best-action engines that trigger human or bot responses depending on customer sentiment and context.

From a data architecture perspective, NICE supports global scalability and voice/omnichannel integration, addressing both model performance and real-time action. While the vendor may come with a legacy contact-centre heritage, its recent focus shows strong alignment with modern CX technology trends. 

Strengths 

• Mature platform with global scale and broad CX footprint 

• Predictive routing and sentiment-based decision making built into live workflows 

• Strong partner ecosystem and integration credentials 

Considerations 

• Because of its breadth, implementation may require more time than niche tools 

• Buyers should validate the “predictive” claims carefully  

Google LLC (Customer Engagement Suite) 

Google has pushed hard into the CX space, driven by its growing range of conversational and engagement tools. It stands out for its ability to detect intent and entity across channels. While most known for conversational AI, Google’s platform also has predictive capabilities – for instance, forecasting contact-volume spikes and routing accordingly. 

Google offers strong cloud-data architecture and granular real-time analytics, giving teams the infrastructure to scale predictive CX models. Organizations with significant investments in cloud-native data stacks will find Google LLC a particularly compelling choice. 

Strengths 

• Cloud-native, scalable architecture with strong analytics underpinnings 

• Strong brand and ecosystem support for advanced data/machine-learning pipelines 

• Good fit where CX AI is part of a broader digital transformation 

Considerations 

• May require additional implementation effort (data-ops, custom models) for full predictive use-case 

• For contact-centre-specific workflows, you may need partner overlays 

Kore.AI  

A rapidly emerging leader in predictive CX, Kore.ai has become synonymous with intelligent virtual assistants that combine conversational and predictive automation. The company’s XO Platform delivers intent prediction, emotion analysis, and autonomous task completion across digital and voice channels.  

Kore.ai’s strength lies in merging dialogue analytics with predictive insights – enabling enterprises to move from scripted responses to proactive engagement. Its open integration framework connects with CRMs, ITSM, and workforce platforms, ensuring predictions become immediate actions. 

Strengths 

• Predictive intent and emotion detection within conversations 

• Low-code interface accelerates deployment and training 

• Flexible integration with existing customer experience tools 

Considerations 

• Still maturing in large, multi-lingual deployments 

• Requires clear governance to manage automated decisioning 

Genesys 

Genesys has re-established itself as one of the top predictive analytics vendors in CX, driven by its AI-powered orchestration layer and “Predictive Engagement” suite. It analyses customer behaviour in real time, from web interactions to voice analytics, then determines the next best action, agent or channel to optimise outcomes. 

Strengths 

• Innovative approach geared toward proactive, autonomous workflows 

• Good fit for organisations willing to experiment and scale up quickly 

• Lighter implementation burden possible 

Considerations 

• Fewer large-scale reference deployments than incumbents 

• Buyer should validate predictive-model maturity, underlying data pipeline and integration readiness 

How to Use This Guide 

At the evaluation stage, your task is to match vendor capability against your requirements. Use this article to: 

• Short-list 2–3 vendors based on architectural fit & strategic alignment 

• Map each vendor to your priority use-cases (e.g., churn prediction, real-time routing, next-best-action) 

• Ask vendors detailed questions about their model performance, integration time, and proof-points 

Key Questions to ask Vendors 

• How is the data architecture structured to support real-time modelling and activation? 

• What actual measurable outcomes (reduced churn, increased NPS, cost avoidance) can you share? 

• How quickly can you integrate into our CRM/contact-centre stack and deliver pilot value? 

• How do you avoid “AI washing” (i.e., vendors re-labelling basic analytics as predictive)?  

Choosing your Predictive CX Customer Service Tools

Ultimately, in 2026, the leading customer experience vendors will be those that pair advanced CX AI with strong predictive capabilities and mature integration frameworks. The four vendors we’ve discussed bring different strengths: whether it’s global scale, cloud-native agility, analytics depth or innovation speed.  

Use your evaluation criteria to match vendor capabilities with your organisation’s needs. With the right decision you’ll move from reactive service to proactive experience – meaning more value, happier customers and measurable business impact. 

Choosing a vendor is just step one. Choosing the right CX strategy is everything. Find out more in our Ultimate Guide to AI & Automation 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%

Christian Draeger, who runs passenger experience there, talks about it in a way that’s surprisingly down-to-earth. “We’re not just starting at the airport door,” he says. “We’re already looking at customers, how they can get prepared for their travel, even days ahead of the actual travel plans.”

That’s a different way of thinking about travel, one where the airport is part of the journey, not a pause in it. Draeger’s rule is simple: “Put the passenger in the center.”

That idea is becoming more important. Around two-thirds of travelers now use AI tools to plan their trips, and most say they want services that adjust to them, not the other way around. Berlin’s answer is to mix technology with empathy, using automation to remove hassle, not humanity, and turn the everyday airport routine into something that actually works for people.

Understanding & Designing for the Modern Traveler

Christian Draeger has spent a lifetime around airports. More than thirty years in aviation have given him a deep sense of how people move, wait, and connect. During his time with Star Alliance, he helped shape what millions of passengers now recognize as the modern travel experience. When he joined Berlin Brandenburg Airport, he came in ready to rethink that experience from the ground up.

Berlin handles around 25 million passengers a year, so it’s big enough to be busy, but small enough to still care. “We also operate our premium services: two business-class lounges and an ultra-premium lounge where you get à la carte dining and a chauffeur service to the aircraft,” Draeger said.

That same care for detail extends to the parts of the journey most people barely notice. The airport also took control of its own security operations, because, as Draeger puts it, “We felt that the mandate of the federal police didn’t provide enough attention towards the passenger experience.”

Now there are 32 security lanes, 24 fitted with advanced CT scanners, so passengers can keep laptops in their bags and carry small amounts of liquid without delay. “It’s about having a consistent experience across the whole area of the airport,” he says.

Every choice is made with the passenger in mind. “It starts really by knowing our customers,” Draeger says. “If we have a family that’s traveling once a year on holiday, their prerogatives are different from a business-class customer focused on getting through as efficiently as possible.”

That balance, efficiency for some, discovery for others, is at the heart of personalization in travel, and it’s essential. A recent study found that 93 percent of travelers now expect some form of tailored service. Berlin’s approach proves those numbers translate into real-world design decisions: better security flow, less queuing, and even duty-free areas reimagined as “specialized marketplaces.”

Dual-Terminal Strategy: Two Philosophies, One Vision

A walk through Berlin Brandenburg Airport reveals something a bit different. Its two terminals don’t just separate airlines; they reflect two completely distinct types of travelers. One is designed for comfort, the other for speed. Together, they show how personalization in travel can be built into the physical space, not only into digital systems.

“The level of automation that you will find with low-cost carriers is more in focus than with a legacy carrier,” says Draeger. Terminal 2 is the efficient, minimalist one: smaller, sharper, and geared toward travelers who value simplicity and price over perks. “Terminal 2 is geared to simplicity and generating additional revenues through add-on services,” he explains.

Think self-service kiosks, intuitive wayfinding, and a layout that helps people move quickly from curb to gate. “The utilization of busses is less, you have more walk boardings,” he adds.

Terminal 1, meanwhile, is a different rhythm altogether. “It’s about efficiency and comfort, both guided by digital tools.” Business and frequent flyers pass through airport automation that’s designed to make the process seamless. Over a hundred self-service kiosks are spread across the terminal, complemented by digital signage and premium lounges.

It’s the physical version of a digital truth: no two passengers want the same thing. Some want to breeze through with a coffee and a boarding pass on their phone. Others want time, space, and a glass of something cold before they fly. Both deserve an experience that feels intentional.

That’s what Berlin is building, a new kind of airport customer experience where infrastructure itself becomes a form of personalization. Different terminals, different tools, same philosophy: know who’s traveling, and design accordingly.

AI and Automation Enhancing Personalization in Travel

Like most airports, Berlin once relied on a traditional call center. It worked, but just barely. “We were looking at our call center and we weren’t completely happy,” says Draeger. “It was limited, inconsistent, and expensive.”

That frustration turned into an opportunity. Berlin decided to replace its call center entirely with a generative AI-powered system. The result was “Berry”, Berlin’s always-on virtual assistant.

“Customers can call the AI hotline and have a conversation just like we’re having right now,” Draeger says. It took just six weeks to build and launch, and within a few months, the results were striking: satisfaction above 85 percent, costs down 65 percent, and service available 24/7.

The human element didn’t vanish; it just found a new home. Instead of waiting in phone queues, travelers get answers right away. Lost something? Need flight details? Berry, the airport’s AI agent, takes care of it and loops in a person if the question needs a human touch. It’s simple to use too: one phone number on Berlin Airport’s website connects straight to Berry.

Building the AI Layer with Berry

Behind the scenes, Berry learns fast. “After six to eight weeks we reached an acceptable level… then you could see week-to-week improvements as GenAI learned,” Draeger explains. His team fed the system with real passenger questions and prioritized the most urgent topics first, like the classic “I left my laptop on the aircraft.” “We prioritized major customer concerns to ensure correct routing from day one,” he says.

Now the airport is preparing for the next step, chat. “We want to also offer the ability to get in touch with our AI agent through chat functionality,” says Draeger. QR codes will soon appear throughout the terminals, linking passengers directly to Berry via chat, integrated into the website and app. “If you’re standing in the arrivals hall, we’ll know based on the QR code where you are, and tailor the information accordingly.”

The idea is to build truly contextual assistance: a passenger in departures might ask about gate directions or restaurants, while someone at baggage reclaim could get help locating transport or lost luggage. “Customers can switch between voice and chat depending on environment or age. My children would prefer to talk; someone in a crowded terminal might prefer to chat,” Draeger says.

Operational AI and the Quest for Seamlessness

A lot of what makes Berlin Brandenburg Airport work isn’t something you can see. It happens on the tarmac between the terminal and the runway, where planes turn around for their next flight, and timing is everything.

“We also have others more on the ramp side,” says Draeger, referring to a system the airport now uses to track ground operations in real time. Cameras watch every stage of the turnaround, feeding data to an AI that predicts how long the process will take and where it might go wrong. “They can predict turnaround durations and steer additional resources if required,” he explains. “If a baggage belt is missing upon arrival, they can autonomously act on that and resolve bottlenecks.”

This is the kind of work that truly shapes the airport customer experience. When flights leave as scheduled, lines move faster, and connections fall into place without drama. Most travelers never think about the coordination behind it all. Yet every new piece of technology adds a layer that must fit perfectly with the rest.

But every new layer of technology brings its own challenge. “We always want to have this seamless experience for our customers,” Draeger says. “As we introduce more technology, we’ll have the challenge of combining it with legacy systems.”

Airports, after all, are built to last, and that means old baggage systems, decades-old software, and miles of wiring that can’t just be swapped overnight. “Traffic is increasing significantly, and we have limited infrastructure,” he adds. “We need simpler processes and better technology to absorb growth.”

Behind the polished front end of any airport automation project lies a balancing act: new tools talking to old systems, innovation working around concrete and cables.

The Future for Personalization In Travel: Digital Handholding

When asked what he thinks the future of travel looks like, Christian Draeger doesn’t mention drones or driverless terminals. He talks about something far simpler: help that is steady, thoughtful, and personal. “We always like to call it digital handholding,” he says. “A digital entity that’s completely informed, taking the customer by the hand and guiding them through the journey.”

Many agree that this is exactly where AI in the travel industry is heading. Gartner predicts that more than 80% of all customer interactions will be AI-assisted by 2029. The difference now is how personal that assistance can become.

“In the future, we see customers having their own personalized digital agents,” Draeger says, “on mobile, VR glasses, or other interfaces.” Those agents will be able to do a lot. “They’ll be able to rebook flights, change hotels, handle issues,” he explains. “We’ll need to provide them with the knowledge base and interconnectivity so they can act.”

He describes a world where these personal assistants talk to each other. “We’ll see a marketplace developing for agent-to-agent interaction,” he says, a network where your digital travel companion can speak directly to an airline, a hotel, or even the airport itself to smooth out the details before you notice them.

Some of that is already visible in small ways. Berlin is already imagining using augmented reality to help people find their way through the terminal. “If you come to Berlin Airport, sometimes you’ll find too many information boards,” Draeger admits. “Imagine augmented reality guiding you through the airport.”

It’s easy to see where this leads: toward an airport customer experience that blends technology with intuition. The idea isn’t to overwhelm passengers with data, but to take away the stress of travel entirely.

Personalization in Travel and Airports as Experience Ecosystems

Christian Draeger talks about air travel the way some people talk about music, not as noise and movement, but as rhythm. Airports, he says, are meant to keep that rhythm steady. When they do, everything else feels effortless.

“It’s all about making travel easier,” he says. “Like when you take a train, you just arrive and go, that’s the overarching ambition.”

Mostly, Berlin Brandenburg Airport is just pushing for a calmer travel experience. From the moment a traveler checks in to the moment they leave the gate, the goal is to take away the small frictions that make airports stressful. Berry, the AI voice agent, is part of that. So are the self-service kiosks, the CT-scan security lanes, and the quiet bits of software that keep aircraft turning on time.

“It’s not about one technology: Gen AI, robotics, biometrics, or AR,” Draeger says. “It’s about combining them to make travel much simpler.”

That line sums up Berlin’s whole approach to personalization in travel. It isn’t about showing off what technology can do; it’s about how little the traveler has to notice it.

That’s the real future of airport customer experience: an ecosystem that looks complicated underneath but feels beautifully ordinary on the surface, the kind of simplicity only achieved when someone’s been obsessing over every detail on your behalf.

These are clear signs that AI for the CMO is already being woven into day-to-day execution – not parked in innovation labs. 

For those who want to explore how AI is becoming an unmissable part of the modern marketing team, here are 3 examples of how: 

AI-Powered Personalization: Turning Data into Revenue Growth 

Why it matters now
Personalization isn’t a nice-to-have; it impacts revenue. In a recent HubSpot survey, 44% of marketers said offering customers a personalized experience “increased sales significantly.” That’s a striking proof point for CMOs trying to justify deeper investment in data, decisioning and creative ops.  

How AI is optimizing the work
Generative and predictive models help teams scale what used to be hand-built. AI-powered chatbots can resolve queries with brand-safe answers, while still delivering a unique personal experience. 

Meanwhile enterprise landing pages can now automatically adapt to context (target source, segment, behaviour) without manual production of hundreds of variants.  

Use case
Vervoe, an HCM skills platform, used Twilio Segment to personalise ad copy to a target’s specific job role and objectives. The company reported a 2x – 5x average lift in campaign conversions. And a 25% reduction in customer acquisition cost. All from switching to dynamic, role-specific messages.

Predictive Analytics: Smarter Insights, Stronger ROI 

Why it matters now
Salesforce’s 2024 research tied the rise of AI in marketing directly to two families of use cases: generative AI and predictive AI, noting that over half (54%) of AI-using marketers apply predictive tools today.  

For CMOs that don’t want to be left behind, they should be considering how AI and analytics can identify moving trends and changing attitudes. Further down the buyer journey, analytics can also inform leaders about churn risk – an upgrade on legacy dashboards. 

How AI is optimizing the work
Modern analytics platforms surface patterns no human eye will catch – and do it at speed. For example, it can scan to detect sentiment shifts on social media, or even in conversations with customers.  

Analytics can also identify hidden links between customers, helping teams to then refine segmentation. This also helps to schedule interventions with at-risk customers. High-risk customers can be flagged and routed towards ‘save plays.’  

Case study
NinjaCat adopted 6sense AI solutions to sharpen targeting for its “Big Data Day” campaign. By analyzing the relevant LinkedIn community, the team engaged 397 high-value accounts – a 422% increase on prior campaigns. All while cutting cost-per-click by 48%. That’s the practical value of predictive account selection meeting precise media activation. 

Generative Content Creation: Scaling Output Without Losing Relevance 

Why it matters now
Content demand is exploding – and AI is the only way many teams can keep pace according to HubSpot’s 2025 State of Marketing report. A breakout tactic is using AI to turn text into multimedia assets such as demos, presentations and podcasts. This accelerates production without sacrificing personalization.

Adobe’s 2025 trends report echoes the pressure: customers expect relevant offers, at the right time, consistently across touchpoints.  

How AI is optimizing the work
Canva’s Magic Studio can translate prompts into on-brand visuals and video variations, while workflow features keep assets aligned to brand guidelines at scale.  

For video, Synthesia lets spokespeople or trainers produce multilingual clips from scripts – ideal for localized launches and support.  

And when offers are personalized in real time, generative models can render copy variants that fit a “best-next-offer” without manual rewrites.  

Case study
Lab-tech firm Cphnano previously produced one video a year using an external crew. After adopting Synthesia, it now creates 10x more videos. Not only that, but scripts can be updated without reshoots, and there was a 50% increase in SEO visibility within three months. Concrete proof of AI turning content velocity into discoverability.  

The CMO’s Next Steps 

AI for the CMO is no longer a speculative line item – it’s a force multiplier.  

The evidence shows three repeatable wins:

• Personalization that measurably lifts conversion while reducing CAC
• Predictive analytics that concentrate spend on high-yield accounts and steady the forecast
• Content creation pipelines that produce and localize assets at speed.

The common denominator is disciplined data and workflow design. Get that right, and AI doesn’t just make marketing faster; it makes it smarter, cheaper and closer to the customer. 

To discover more insights into the latest & greatest tools driving productivity, dive into our Ultimate Guide to Sales & Marketing Technology. 

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.