At Microsoft Ignite 2025, the tech giant announced its plans to transform how it implements AI and how to improve general productivity. 

Microsoft has offered various AI agents for specific systems and companies for personalized success. 

Microsoft Copilot 365 

Within Office 365, Copilot acts as an assistant to support teams in preparing and managing their communications smoothly, working separately across each application to reduce overall preparation times. 

In Outlook, Copilot can organize a user’s inbox from high volumes of emails to summarized and highlighted information that needs immediate attention, as well as gather any information relevant to the user’s current task for quick response times and correct information gathered. 

In Word, customers can utilize Copilot’s assistance with drafting and refining content, providing summarized notes of material, and lengthening notes into documents, allowing teams to prepare for meetings and reports with limited information. 

In PowerPoint, users can create highly efficient presentations through Copilot’s capabilities to develop slides, draft content, and reposition information to fit their audience’s needs, allowing for clearer presentations with effective outcomes. 

In Excel, Copilot can break down data information through summaries and identify common trends within the spreadsheet, allowing for quick turnarounds in analysis explanations for other customers or employees. 

Copilot Studio

Within Copilot studio, teams can design personalized agents to fit their needs to work inside Microsoft 365. 

This tool can be used support connections between Microsoft’s own content as well as external systems through APIs, meaning customers can utilize the agent outside of Microsoft’s products for quicker results. 

It offers natural language when advising users on tasks, queries, or support with internal workflows and repeatable tasks, minimizing the need for human agent automation. 

For additional security, Copilot studio allows users to manage their custom agents to reassign roles to fit company policies, permissions, and security maintenance, obtaining an Entra Agent ID to verify an agent to operate under secure governance. 

Copilot Mode with Edge 

Copilot Mode can now be used on Microsoft Edge to analyze open tabs to summarize content and research within a browser. 

This reduces time spent tab switching and helps users stay on task by feeding them the relevant information. 

Information can be obtained from articles, texts, and videos, summarizing key points and explanations by interpreting knowledge from different sources at the same time, which can be useful for reviewing widespread customer experiences of products or services. 

Copilot Mode is only entitled to utilize information the user gives it access to, allowing the tool to work within company guidelines and provides the user with the relevant information it receives. 

Agent 365 – provides security and governance for copilot (link back to previous article) (100) 

Working as Copilot’s governance layer, Agent 365 provides organizations with the ability to control AI agents and how they interact within the business. 

Agent 365 utilizes Entra ID to identify each agent within an ecosystem and aligns it with company policies, allowing organizations to set limits and permissions when needed to stop agents from working outside company expectations. 

These tools also allow companies to track AI activity to apply compliance rules to avoid data loss on behalf of the agent, allowing organizations to maintain control while the AI agents continue to complete their tasks.  

Copilot Business 

Working as a slightly different version of Microsoft 365 Copilot, Copilot Business is designed to support small to mid-sized organizations using AI tools. 

Similarly to the 365 Copilot tool, this feature allows businesses to summarize their material, advise on email management, and create presentations using the provided Microsoft 365 apps to reduce time spent on administrative tasks. 

This tool, however, does not require a complex setup as the needs required for SMB don’t necessarily demand the amount of compliance and data needs to function. 

Copilot Business can but used to work at a basic level, whilst continuing to provide analysis and reports of data and communications to increase productivity within smaller businesses. 

Microsoft Ignite 2025 

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

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

The cloud-based software company announced its third-quarter earnings report on Monday, revealing strong results across the board. 

This has led to ongoing investments into current and future AI products and initiatives. 

“We remain on track to exceed the $100MN in ARR from new products by the end of 2025,” said RingCentral CEO, Vladimir Shmunis, adding:

“Adoption of our new AI-led products is broad-based across various customer cohorts, from small businesses to large enterprises.

“Our GSP partners are also beginning to sell these new offerings, expanding our reach and accelerating adoption.” 

This expansion has led the company to roll out multiple AI product releases this week, increasing its customer base by branching out into emerging AI trends. The launches are aimed at enhancing communication experiences for both customers and agents.

This has resulted from an R&D spend of $125MN into its new AI portfolio. The company is already seeing consistent profitable growth, in the hopes of exceeding the year-end ARR target. 

RingWEM Adds AI Workforce Tools to Cloud Contact Center

On Monday, RingCentral released its latest AI product, RingWEM, an AI-powered workforce engagement management suite designed to enhance its native cloud contact center, RingCX. 

The suite offers four capabilities to strengthen customer experience across agent performance, customer satisfaction, and operational efficiencies by using AI-powered insights: 

AI Quality Management: Designed to give human agents the skills to improve their overall performance, the quality management tool will use personalized customer quality criteria to evaluate and provide extensive insight and feedback on customer calls. 

The tool is used to analyze and observe full customer interactions and agent workflows, delivering focused guidance for reskilling. 

Furthermore, the tool offers AI-based coaching recommendations to improve agent expertise, allowing enterprises to improve their workforce by viewing their agents’ performance analytics, common communication themes and advise them on next steps using the provided data-driven advice. 

AI Workforce Management: Used to improve quality in customer service, planning for potential challenges, and overall efficiency, this tool combines precise data forecasting and resourceful scheduling to align staff with probable tasks to tackle targeted demand. 

By using precise data forecasting, the AI workforce management tool can use algorithms to analyze past and current data trends and business drivers to predict the likelihood of contact quantities, allowing time for agents to tackle abnormal spikes before they occur. 

With intelligent scheduling, the workforce tool can generate actionable schedules for agents to follow that include agent preferences, adjustable service level actions, and business requirements to keep agents on track with tasks. 

These can be modified to fit irregular changes in working conditions to ensure that service levels stay the same to avoid customer friction. 

Additionally, customer agent supervisors can run probable scenarios to analyze the effectiveness of staff models and company changes before they are implemented. 

AI Interaction Analytics: This tool provides enterprises with high-level insights into customer satisfaction with interactions, compiling data taken from surveys and summaries from the interactions themselves to address negative customer experiences. 

AI Interaction Analytics can dissect customer conversations through voice tone, language preferences, and patterns in speech to assess satisfaction. 

The tool can use this and other conversations to further analyze key customer trends and issues as a whole, allowing businesses to proactively address these concerns before escalation. 

Screen Recording: Similar to the AI Quality Management capability, this tool allows supervisors to evaluate customer-agent interactions by collectively linking calls and screen recordings for a wider range of information into quality of conversation and workflow efficiency. 

These tools can be utilized to address underlying issues with agent performance and customer satisfaction and elevate operations in contact centers to deliver smarter service. 

RingCentral Debuts Agentic Voice AI Suite

RingCentral has also released its agentic voice AI communications suite, encompassing three tools that enhance communication experiences across the lifespan of each customer interaction. 

AI Receptionist (AIR): Before a conversation begins, this tool ensures that calls are not left unanswered. 

Using the voice AI ability to interact with customers, this AI agent can comprehend a customer’s reason for calling, answer questions, hand off real-time interactions to agents with summarized caller context, and identify and log potential opportunities that may require a human agent to follow up. These can help to avoid customer friction and repeated information. 

For scheduling interactions, AIR provides multi-calendar support across a company to integrate employee schedules and harmonize teams. 

Sales opportunities are collected and stored for future use in Salesforce, HubSpot, or with AIR’s own database. 

AIR can also be used on any SIP-based phone, allowing AI customer handling to be dealt with across the cloud, any premises, or hybrid setups. 

Brian Tucker, Chief Digital Officer at Televero Health, is a customer of RingCentral’s AIR tool. 

He said, “Using RingCentral’s AI Receptionist, the results are undeniable. We saw our monthly appointments increase 14 percent in the first four months, an increase in monthly revenue of over $200,000. 

“That kind of growth and return on investment is exactly what we need.”

AI Virtual Assistant (AVA): During a conversation, AVA can provide an agent with real-time assistance across customer interactions by implementing four key capabilities: 

• Real-Time Calls and Meeting Summaries: Identify the relevant information, questions, and actionable tasks during the span of a call or meeting, generating summaries and highlight reels to allow agents to keep track of the interaction’s objective during the call. 
• AI Writer to Create and Translate Communications: This capability can draft, edit, and translate conversations in multiple languages, allowing for seamless and customer-focused messaging. 
• Multi-Use Assistance Across Workflows: By adapting to a user’s communication method, via phone, text, or chat, this tool can provide intelligent prompts and relevant actions for each task. 
• Product Adoption and Feature Discovery: AVA can advise discovery and management methods to improve RingCentral’s overall customer enterprise experience. 

Kira Makagon, President & COO, RingCentral, explained the value of AVA in an enterprise workflow: “By putting trusted voice intelligence at employees’ fingertips, AVA makes work more productive and empowering,

“AVA is your personal virtual assistant that enables you to work smarter, faster, and more efficiently.”

AI Conversation Expert (ACE): After a conversation, ACE steps in to offer evaluated business insights from these interactions and adds it into one analytics and insights layer for a simplified outlook. 

It provides real-time insight into current customer satisfaction, trends in revenue, and overall agent team performance, giving context to performance data to allow leaders to act quickly. 

When requested, ACE can turn compound data into written summaries, recommend actions and examples of improvement, and be used an interactive interface to allow leaders to inquire related queries with instant results. 

Zach Jecklin, Chief Information Officer at Echo Global Logistics, and customer of ACE, uses the tool for improving company knowledge on customer calls and data trends. 

“AI Conversation Expert provides us with the detailed coaching for individual calls, and the dashboard connects the dots by rolling up all that data into a clear, concise view of the major trends impacting the entire business,” Jecklin said.  

“We used to have call data. Now, we have business intelligence. It’s that simple.”

RingCentral Pairs New AI Tools with Solid Growth

RingCentral has launched the new AI tools in conjunction with the announcement of its strong third earnings quarter. 

During its earnings call, the company reported a total revenue result of $639MN, seeing a growth of 5 percent from the previous year. 

Subscription revenue also increased, rising by 6 percent to $616MN, with a 23 percent rise of $130MN in free cash flow, which it intends to increase during the rest of the year.

An IBM study has revealed that 62% of UK enterprises are unable to use AI’s full potential for successful adoption, with many employees having limited to no experience with the software. 

A similar study conducted by Teradata also revealed that while almost all organizations have implemented AI in some form, only nine percent have fully advanced its place in the workforce. 

These two surveys highlight detailed insights into why companies struggle when adopting AI tools. 

Race for ROI – IBM

The IBM study highlights that AI reskilling knowledge and investment gaps are still underprioritized in the workforce. 

In fact, 62% of organizations have still not managed to leverage AI to utilize full workplace transformation, despite having adopted the software. 

Taken from 3,500 business leaders’ insights across EMEA, the study found that AI across UK industries is expanding productivity levels, with many leaders applauding the software’s efforts to free up employees’ creative and management schedules to allow focus on high-priority tasks. 

And whilst IBM emphasizes AI’s growing position amongst the UK workforce, businesses often face barriers when adopting it, and in turn not receiving the amount of ROI benefits as expected. 

But what are these barriers? 

Inadequate Company Investment in AI Development

Employee preparation for AI deployment continues to create company shortfalls and stalls progress expectations after integration. 

In fact, 67% of UK business leaders agree that there is an internal resistance amongst their workforce to activate pilot AI projects, with many reasons involving cultural restraints. 

On top of that, just 45% of UK organizations offer a company-wide or function-focused AI training, with only 38% providing upskilling services for all its employees. 

This highlights an important issue: AI can often be dismissed by companies due to a lack of knowledge on the software. 

This leaves ROI results imperceptible for the majority of businesses, with only 27% of UK senior leaders seeing any cost returns or financial benefits from productivity in AI implementation. 

Unable to Meet AI Trend Demands

Customer demand for the latest AI deployments across industries is forcing companies to look ahead of the newest trend curve or risk being left behind. 

However, with rapid technological changes, keeping up with the latest trends requires continuous investment in pilot projects for companies that can spare the resources. 

In September, a previous IBM UK consumer survey revealed that 74% of customers were happy with AI-powered assistants being involved in their consumer experience, with an expectation of transparency and trust with the automated agent. 

This illustrates a future AI-focused approach to CX with rising customer trust, as 79% of customers believed that chatbots would deliver trustworthy results, as well as 72% showing satisfaction with using them. 

Furthermore, the AI agents provide consumers with accelerated response times, resulting in customer convenience, which 40% of consumers valued, and demand for high levels of data protection and privacy for 37% of consumers. 

It is clear from these results, that when deployed correctly, cutting-edge AI-powered solutions can deliver measurable CX improvements and help companies achieve ROI; however, smaller enterprises will likely struggle to meet these demands. 

Key Constraints in AI Deployment 

Additionally, companies have shown signs of significant challenges when it comes to measuring their ROI after adopting AI. 

Notably, senior leaders surveyed in the study outlined the following as the biggest challenges to achieving ROI: 

• High Upfront Investment Costs: 37% of leaders agreed that AI spending is too high for companies to measure their short-term ROI. 
• Difficulty in Attribution: 35% of leaders found it difficult to measure performance outcomes from AI integration due to the number of factors contributing to its success. 
• Lack of Skills or Knowledge: 31% of leaders acknowledged the gaps in knowledge and skill could be a barrier to accessing AI’s financial impact. 

Companies must therefore assure that their AI investments are focused on its core business objectives to focus on efficiency, growth, and enhanced customer experience. 

Not all Doom and Gloom

Despite the significant difficulties outlined above, the IBM study found that AI across UK industries is improving productivity levels, with 66% of senior leaders seeing high levels of productivity thanks to AI.

Indeeed, many leaders applauded the software’s efforts to free up employees’ creative and management schedules to allow focus on high-priority tasks.

This also includes 63% of leaders expressing higher efficiency rates across operations.

Leon Butler, Chief Executive of IBM UK and Ireland, highlighted the productivity benefits of company AI training investment, stating:

“UK businesses are clearly seeing the productivity benefits of AI, with two-thirds already reporting significant gains. But the real opportunity lies ahead – unlocking even greater value through workforce transformation and upskilling.  

“By investing in AI skills training across all levels, organizations can not only outperform their peers but build a future-ready workforce that drives innovation and resilience.”

Sue Daley OBE, Director of Tech and Innovation at techUK, echoed Butler’s sentiments, emphasizing the requirements needed for businesses to use AI to its full potential:

“To succeed in the long-term, businesses must make AI reskilling a key part of their employee development strategy.  

“This will empower employees to embrace and leverage AI’s potential, unlocking its full value across the whole business.”

The discussion around the importance of AI reskilling is particularly interesting following the recent layoffs at Accenture, where the company let go of 11,000 employees as part of a restructuring based on its reskilling strategy.

Speaking at the time, Accenture CEO, Julie Sweet, stated:

“We are investing in upskilling our reinventors, which is our primary strategy. Those we cannot reskill will be exited.”

Agentic AI Survey – Teradata

The Teradata study painted a similarly gloomy picture on the current state of AI in CX.

The report emphasized that despite agentic AI’s rate of adoption, many companies cannot access crucial data and display a lack of trust towards their governance structures to manage AI agents. 

Implementation appears to be a top constrainer for many of Teradata’s enterprise respondents, despite 74% remaining hopeful in the AI’s ability to improve the CX space. 

Taken from 500 established AI-relevant executives with more than 100 employees and $500MN in annual revenue, the survey reveals how the potential issue with AI adoption falls with company resources, rather than employees themselves. 

For or Against CX Trends?

Trending agentic AI tool adoption remains mixed amongst enterprises, with over 50% of respondents eager to get their company established in the CX competition, more than a third remain cautious to wait for these trends to be proven reliable, up from 22% the previous year. 

To avoid unnecessary AI ‘hype’ before committing, many companies prefer to wait for AI solutions to implement them. 

This tactic, whilst beneficial from a financial perspective, can damage the overall speed rate of AI adoption if companies rely on one another to improve their capabilities. 

Although 91% of surveyed companies have not yet fully adopted AI agents, 81% organizations that had were reporting soaring confidence levels in its ability to improve customer experience. 

The conclusion? Confidence in AI adoption comes from implementing it to its full potential. 

Data, Governance, and Skilling Conflicts 

Obtaining data for AI in CX proves to be a demanding challenge for many enterprises, with only four percent having consistent and accurate data access on hand, with the 96% having experienced delays and inconsistencies. 

Furthermore, when it comes to priority concerns, 34% agreed that data accuracy was a major issue, followed by 32% suggesting security was a crucial concern, meaning enterprises will need to aim for truth and safety when it comes to AI data handling. 

In regard to governance, 93% reported challenges when creating frameworks and guardrails to manage AI. 

This appeared to be an issue for larger organizations in particular: 42% of enterprises with more than $20BN annual revenue saw the biggest challenges when creating effective governance, implying a greater difficulty for implementing reliable AI when enterprises increase. 

Similar to the IBM survey, gaps in knowledge and skills remain a key barrier to AI adoption across internal teams, with 73% not having the basic skills to advance AI initiatives. 

AI Adoption Recommendations

While both reports offered insights and data on AI adoption struggles in the CX space, IBM also highlighted five recommendations to help combat these issues: 

1. Establish an effective operating model for AI: With a clear and consistent framework for AI transformation, companies can measure its business outcomes easily to drive ROI. 
2. Cultivate AI literacy and a culture of innovation, from the Board to entry-level: By educating all employees on how to use AI tools, staff can more effectively implement AI across most aspects of their workday. 
3. Get comfortable with uncertainty and rapid change: The AI environment is growing at an extraordinary rate, meaning companies will need to adapt to trends where possible, such as embedding tools and products into a variety of search engines, apps and devices and remain competitive. 
4. Understand the risks around AI deployment: As a newer technology, AI poses a number of risks towards enterprises, meaning AI governance should be deployed to handle and monitor daily threats such as data misuse or cyberattacks. 
5. Establish a cross company “AI Board” to mitigate risk: This is a dedicated oversight body that assesses risks before AI use cases are added, as well as identifying them as responsible and safe to use. 

Both CX surveys have exposed the shortage of AI knowledge between organizations across their companies.

As noted in the IBM survey, companies do not always have the resources to accommodate their employees’ AI reskilling strategy.

However, money and company size may not always be the solution, as Teradata identified that even strong, established companies show visible restraint to AI implementation when it comes to governance.

One commonality, however, is that both have observed its respondents’ unwillingness to fully scale AI within a business, with AI execution on the backburner until deemed safe to implement.

Despite AI’s recent upsurge in popularity in the CX space, not all companies have yet built trust with the software, delaying the agents’ potential within a company.

Only once these issues are resolved will companies see better if any results from AI adoption.

The cause? Both a rapid decline in customer experience interactions and a similar decline across BPO partners who have stopped evolving. Providers that once promised transformation, but over time, have begun to settle for maintaining the status quo. 

This isn’t another story about cost-cutting gone wrong. It’s about revenue: measurable, P&L-destroying losses that CFOs and revenue leaders are only now beginning to trace back to a decision made years ago in procurement. 

The $2.4 Million Question Nobody Asked 

Consider a global technology brand, one of those names everyone knows. The company was losing $2.4 million a year through its service booking process. 

Their product wasn’t faulty; their pricing wasn’t wrong. Somewhere along the line, nobody had stopped to ask the fundamental question, “Is our BPO partner actually making us better?” 

The process by which the company scheduled, confirmed, and serviced customer repairs and installations, had become tangled in inefficiencies. Error rates crept into double digits. Each mistake meant rework, refunds, and customer frustration. 

When Transcom was brought in to help, they led a complete workflow mapping process. What they uncovered was striking. The previous provider had failed to evolve with the business requirements, and worse, had failed to act as a proactive business partner. 

Workflows were outdated, knowledge was inconsistent across teams, and no one had taken responsibility for driving shared business goals. Transcom guided the redesign, standardized processes across regions, and built a clear, unified knowledge base. 

Within months, the error rate dropped to just 3%, less than half the original target of 7%. The annualized $2.4 million leak stopped almost overnight. 

This wasn’t a technology problem. It wasn’t a staffing issue. It was a partnership problem masquerading as both. 

The Stagnation Tax 

Ericka Heligman, who’s spent nearly two decades working with and for global BPOs, has seen this pattern play out time and again. “Too many providers stop evolving,” she says. “They’re not bringing new ideas and they’re not driving smarter ways to create value for clients.” 

When that happens, the results are predictable. Multi-million-dollar relationships quietly disappear because someone, somewhere, decided the status quo was good enough. 

Right now, she’s seeing the same story unfold with a global powerhouse brand whose long-time BPO partner has gone dormant, functionally asleep, while the market races ahead. The client’s frustration isn’t just about missed efficiencies; it’s about a lack of proactive involvement to drive ongoing, substantive improvement and real value. 

This is the true stagnation tax, not just the cost of inaction, but the opportunity cost of failing to lead. 

That’s where Transcom takes a different approach. As Jeff Blair, Chief Growth Officer at Transcom, explains: “We’re pragmatic and client-first. We take immense pride in being a proactive partner who acts as a seamless extension of our clients’ business.” 

Transcom supports a tech-agnostic model which means the company partners with the best AI and automation providers in the market and only develops proprietary tools when no suitable solution exists. This flexibility ensures clients always get the right technology for their business goals, not whatever happens to be sitting on a provider’s shelf. 

The result is a partnership built on progress, not product sales. 

What CFOs Actually Care About (Spoiler: It’s Not Customer Happiness) 

Let’s be direct. CROs don’t invest in CX because they’re sentimental about customer happiness, they do it because CX has a measurable, proven impact on revenue. 

The data is impactful: 82% of customers say they stay and spend more with brands that deliver great experiences. On the flip side, 85% have stopped or reduced spending after a poor interaction.  That’s not just a customer satisfaction problem, but a revenue retention crisis. 

CX is either your growth engine or your churn accelerator. If your BPO partner isn’t actively, constantly working to tip that balance in your favor, they’re costing you more than their monthly invoice. 

The Advisory Gap 

This is where most traditional BPO relationships collapse into irrelevance. They deliver the service you specified, at the price you negotiated, with the KPIs you defined three years ago. And then they stop. 

Transcom’s CX Advisory practice flips the script. Their focus is on auditing your business, finding hidden inefficiencies, pinpointing overlooked KPIs, and uncovering opportunities for innovation before your competitors even see them coming. 

Think about that for a moment. Instead of a partner who waits for your RFP to tell them what to fix, you get a partner who tells you what’s broken before you realize it’s costing you money. 

For that tech company with the repair and instillation errors, the breakthrough didn’t come from better agents or smarter AI. It came from business process mapping, an unsexy, detailed, forensic analysis of where knowledge gaps and inconsistent workflows were creating expensive mistakes. Then the team implemented knowledge standardization across internal teams and external partners, deployed through a coordinated regional campaign. 

Call it what it is: this isn’t outsourcing in the traditional sense. It’s a strategic partnership. 

The Next-Generation BPO Imperative 

The BPO industry is sometimes viewed with skepticism. Too many providers treat outsourcing as a cost arbitrage play: find cheaper labor, lock in a contract, and coast on renewals. 

Here’s what’s changing: enterprise buyers are no longer shopping for the cheapest option. They’re looking for partners who can deliver competitive advantage. Partners who won’t just execute the playbook you handed them, but who will help you rewrite it every quarter based on what’s happening in your business and your market. 

The leading tech brand referenced earlier didn’t just need better execution. They needed someone to notice the problem, quantify the impact, redesign the process, and coordinate implementation across multiple geographies and stakeholder groups. That’s consultancy-level work wrapped in BPO-level scale. 

The Bottom Line (Literally) 

If you’re a revenue leader, a CFO, or anyone responsible for growth, here’s the uncomfortable question: Is your current BPO partner making you more competitive, or just less expensive? 

In 2025, those are very different things. The market is moving too fast, customer expectations are rising too quickly, and AI is democratizing too much for “good enough” to remain viable. 

Your CX partner should be obsessed with your P&L. They should be able to draw a straight line between their work and your retention rates, your upsell opportunities, and your churn reduction. They should be bringing you insights, innovations, and improvements you didn’t ask for, because they understand your business well enough to see what you can’t from the inside. 

The brands winning in this environment aren’t the ones with the fanciest AI stack. They’re the ones who’ve figured out that CX is a revenue engine, and their BPO partner is either fueling that engine or silently draining it. 

The only question that matters: which one is yours? 

“The AI gave inaccurate or irrelevant insights,” he noted. “Managers still had to manually check tickets, and the feedback wasn’t useful to agents.

“After four weeks, we concluded the tool didn’t provide the expected value,” and Iterable shut the pilot down.

Since then, many other contact center leaders have shared similarly disappointing experiences with Auto-QA solutions.

Chris Crosby, Founder of VenturesCX, spotlighted this trend in a recent LinkedIn post. He wrote:

Weekly now, I talk to a company that is “unimpressed” with the AI or Automated QA from (insert their vendor here).

So, what’s causing these unsuccessful deployments, and how can contact centers ensure their deployments deliver the expected results?

The Fundamentals Need to Be in Place First

Justin Robbins, Founder & Principal Analyst at Metric Sherpa, regularly speaks to contact center leaders and quality analysts.

Recently, one leader told him something profound:

If we’re not getting it right with the 0.5 percent of customer contacts we are currently monitoring, why would I ever believe automating it will fix problems?

In this sense, if contact centers don’t have the fundamentals in place first, they won’t drive sustained improvement with an automated solution.

For Robbins, those foundations include establishing a root cause analysis cycle and continuous identification of predictive, proactive actions.

“Whatever we observe in the quality process shouldn’t happen again,” he said.

The goal isn’t to keep observing the same issues forever; it’s to drive business improvement. Simple, but easy for people to lose sight of. It’s not about catching someone doing something wrong today.

To drive that business improvement, Robbins, Crosby, and other industry pros advocate for a more connected learning strategy, which sets the stage for Auto-QA success.

Developing a Connected Learning Strategy

A connected learning strategy ensures analysts and coaches work together to define performance standards, identify and close gaps, and run post-training reinforcement.

In doing so, analysts and coaches agree on what an excellent service experience looks like across common specific scenarios, engaging in calibration sessions.

From there, the analysts spot performance improvement opportunities and share those with the coach to inform their training. The analyst then tracks the training’s impact and any change in agent performance.

When everyone is pulling in the same direction, the value of Auto-QA rises. Indeed, analysts not only spot opportunities for improvement – such as this agent needs to show more empathy – but can also unearth specific scenarios the agent struggles with. In turn, that’ll drive more targeted coaching.

For example, if a new agent struggles with a particular healthcare plan nuance, the supervisor can focus there, enabling data-driven, targeted, and scenario-specific coaching.

Additionally, contact centers can start to take all that Auto-QA data and consider how to leverage it in new ways. Sharing an example, Crosby told CX Today:

We’re also starting to map the entire agent journey, from recruiting through tenure, using data from QA, HR, attendance, call logs, and more. The goal is to synthesize all of that into a holistic view that improves both performance and retention.

Contact centers may also consider using intent-specific data to inform routing engines, agent-assist prompts, and more. That’s the future of Auto-QA. But, without teams working closely together, these benefits will feel little more than distant possibilities.

Choosing the Best-Placed Solution

In the case of Auto-QA, it’s normally the strategy, not the technology, that fails the contact center. That said, some voice of the customer (VoC) and conversational intelligence vendors transitioning into the space are delivering half-baked solutions.

Making this point, Emmanuel Doubinsky, VP Product at Scorebuddy, said: “As their AI already analyzes most customer conversations, they often try to also analyze the agent side of these conversations to provide what they believe is a suitable Auto QA service. However, they generally hit the following roadblocks, often discovered halfway through their deployment journey.

Per Doubinsky, these roadblocks include:

• Model training mismatch: VoC models are optimized for broad sentiment analysis and theme detections rather than the specific compliance checks and process adherence that QA requires.
• Insufficient scoring granularity: VoC systems can provide high-level sentiment scores suitable for trends, but lack the detailed, multi-criteria scoring precision needed for individual agent performance evaluation.
• Workflow gaps: VoC platforms miss the agent engagement workflows that are key to a successful QA program. They also miss the coaching and learning workflows that QA needs to measure and improve agent performance.

As such, buyers seeking out the best-placed auto-QA solution should challenge potential vendors on these three key elements to safeguard outcomes.

Today, companies aren’t just being measured on speed or resolution rates. They’re being measured on how it feels to do business with them.

That feeling, the tone of a conversation, the empathy in an answer, the confidence in a response; these usually comes down to one thing: how supported the agent on the other end actually feels.

Workforce engagement management is the connective tissue that keeps human-led customer experiences from falling apart under pressure.

Originally born from workforce optimization (WFO) – which focused on labor cost, scheduling, and productivity – workforce engagement management expands the scope. It adds intelligence, coaching, flexibility, and voice-of-the-employee feedback into the equation.

The business case is obvious: high agent churn is expensive. Disengaged teams drag down CSAT and NPS. Frontline burnout doesn’t just affect morale; it affects every metric a CEO cares about.

But the strategy isn’t just HR’s problem. WEM cuts across operations, IT, and customer leadership. In modern enterprises it’s becoming foundational to everything from digital transformation to AI-readiness.

VISIT THE WEM MARKETPLACE

What Is Workforce Engagement Management?

So, what is workforce engagement management?

WEM software is a suite of technologies designed to improve the day-to-day experience of employees and agents. It’s there for the people answering calls, handling chats, resolving issues, or keeping the service engine running behind the scenes.

WEM platforms bring together previously siloed functions like:

• Workforce forecasting and scheduling (WFM)
• Quality assurance and interaction scoring (QM)
• Learning and coaching workflows
• Performance dashboards and gamification
• Real-time feedback and voice-of-the-employee programs
• AI-powered assistants and agent-facing automation

The best platforms do all of this across cloud environments, hybrid teams, and global time zones, with as little admin as possible. What really sets the WEM model apart is that it’s designed for people, not just processes. Vendors are building tools that offer real-time coaching prompts, simplified self-scheduling tools that actually consider agent preferences, and dashboards that monitor burnout.

WEM has become a strategic partner to:

• Operations: by tightening resource alignment and reducing downtime
• CX leaders: by lifting customer sentiment through empowered agents
• IT and digital teams: by enabling automation without alienation
• HR and L&D: by tracking skill development in real time, not quarterly

It also connects tightly to other pillars in the modern CX ecosystem, from ERP systems to business intelligence platforms.

WEM vs. Legacy Workforce Tools: Understanding the Evolution

Talk to any enterprise ops lead or CX architect about workforce software, and you’ll probably hear this first: “We’ve got some of that already.”

What they usually mean is WFO, WFM, or a quality management system that hasn’t been updated in years. Somewhere along the line, it all blurred together. But there are differences to be aware of.

• Workforce Optimization (WFO): This is the legacy stack. Workforce optimization was built for scale, not flexibility. It’s a system of record for making sure the right number of people were in the right place at the right time, with as little waste as possible.
• Workforce Management (WFM): Forecasts, schedules, rosters. If WFO is the umbrella, WFM is the spine. It calculates expected volumes and assigns shifts accordingly, across voice, chat, email, social, sometimes even back office tasks.
• Quality Management (QM): Every enterprise has some version of this. A system that records calls, tracks disclosures, and grades interactions. Some still use spreadsheets. Others have automated tools that flag risky phrases or negative sentiment.

Workforce engagement management changes the lens. It doesn’t just track productivity or performance; it helps create the conditions where productivity actually happens.

It connects scheduling with well-being. Performance with recognition. Coaching with data. It gives agents the ability to swap shifts, track progress, and get live feedback without jumping through three systems and two supervisors.

The best WEM software listens to agents as much as it monitors them. It surfaces burnout risk, not just missed KPIs. Plus, it turns “manager reviews” into live, AI-driven guidance when it matters most, in the middle of a conversation, not the end of the month.

Essential WEM Capabilities for Modern Contact Centers

Plenty of platforms claim to boost engagement. But WEM software is different, not just because of what it includes, but because of how it connects those features into the day-to-day flow of work.

Here’s what defines leading WEM solutions in 2025:

AI-Powered Forecasting & Scheduling

WEM starts with smarter staffing. AI tools predict contact volume across channels (voice, chat, social, email), then build schedules around both demand and agent availability, including preferences and skills. Some systems, like NICE EEM, even auto-adjust shifts intra-day to fill unexpected gaps.

Real-Time Quality Management

Traditional QM scores interactions after the fact. WEM platforms now layer conversational analytics, real-time transcription, and auto-suggestions to help agents course-correct mid-call, not after it’s too late. They go beyond quality monitoring and call recording, with real-time insights.

Performance & Gamification Dashboards

Agents and supervisors get tailored dashboards that show key KPIs from AHT to CSAT, alongside gamified goals, rewards, and badges. Not just for fun – for focus. It works: a Gallup study found gamification boosts productivity by up to 14% when combined with coaching.

Learning & Development, Embedded

WEM software often includes integrated learning management system (LMS) modules, allowing supervisors to assign training based on performance data, not gut feel. Employees get regular microlearning nudges tied to real-time metrics. They’re smart, targeted, and actually used.

Voice of the Employee (VoE) & Sentiment Tracking

Feedback tools are embedded into most leading WEM platforms. Pulse surveys, in-session sentiment tagging, eNPS trend tracking. All designed to give leaders a line of sight into engagement, before it becomes attrition.

Hybrid-Ready Scheduling & Self-Service

Self-scheduling. Mobile shift swaps. At-a-glance schedule visibility. In a hybrid world, these features are increasingly crucial. Companies don’t just need to manage employee schedules; team members need to be able to make shift changes themselves.

Measurable Business Impact: The ROI of WEM Solutions

Ask any enterprise CX leader where they’re losing ground, and chances are the answer isn’t tooling – it’s people. Attrition, disengagement, inconsistent performance. The fundamentals. The truth is, most of it’s avoidable if the systems around the work actually support the people doing it.

That’s the core promise of workforce engagement management in 2025. It’s not just better data or cleaner interfaces, but an operational model built for sustained, scalable performance: one that integrates AI, learning, flexibility, and accountability into the work itself.

Here’s where the return on investment becomes undeniable:

1. Serious Retention Improvements

Contact centers have a talent problem. The work is hard, the pressure is growing, and legacy systems aren’t built to reduce either. The result? Enterprise-level attrition rates of 35%–45% are still considered “normal”, and the true cost per lost agent runs deep:

• $10,000–$15,000+ in replacement and training costs
• Knowledge loss
• Disrupted team continuity
• CX instability, especially for high-value customers

WEM solutions counter this with structure. They allow agents to:

• See progress, not just tasks
• Adjust schedules to fit real lives
• Receive feedback that’s timely and specific
• Access growth paths without needing to chase them

This is what shifts perception from “just a job” to a role worth keeping.

2. CX That Starts From the Inside Out

What drives true loyalty, especially in high-emotion, high-complexity interactions, is the confidence and care on the other end. WEM software makes that possible by:

• Flagging sentiment changes in real time
• Providing AI-suggested prompts during live interactions
• Helping agents manage stress levels and emotional loads through pacing and workflows
• Tying the voice of the employee (VoE) insights directly into customer journey metrics

When agents feel heard, supported, and prepared, they’re more likely to listen, support, and solve.

3. Operational Control Without the Micromanagement

This is one of the reasons contact center leaders actually like WEM: it puts control back in their hands without overloading supervisors or requiring three tools to do one job.

Key benefits from the ops side include:

• AI-powered staffing models that adjust by hour, not just day
• Intraday management tools that detect gaps, absenteeism, and surges in real time
• Blended service support: voice, chat, async messaging; all tracked against unified KPIs
• Integration with CPaaS platforms to connect agent workflows with external customer comms

WEM helps ops teams run smoother shifts, make smarter staffing calls, and prevent fire drills before they start. It turns guesswork into decision science.

4. A Feedback Loop That Actually Works

Traditional employee feedback processes are often slow, vague, and almost never actionable in the moment. WEM makes engagement and feedback part of the actual workflow:

• End-of-shift sentiment pulses
• In-the-moment feedback tagging
• eNPS scores tied to performance windows
• Manager dashboards showing team trends in real time

Instead of waiting for problems to surface, WEM lets companies track friction as it builds, and respond before people check out.

5. A Bridge to the AI-Enabled Future of Work

AI isn’t just for self-service chatbots. The most forward-looking WEM platforms are already embedding it directly into the agent experience, not to replace humans, but to extend their capabilities.

• Smart Assist: Live prompts during customer interactions
• Auto-tagging of performance triggers and coaching moments
• Adaptive learning that updates training based on new behaviors
• Workflow routing that adapts to agent strengths and availability
• Emotional analysis to detect frustration and disengagement on both the agent and customer side

Some companies are even experimenting with systems that empower leaders to manage AI agents and human employee workflows on the same platform.

Leading WEM Vendors and Platform Selection

There are dozens of impressive workforce engagement management vendors worth considering today. Some are blending WEM and business intelligence, others are going all-in on AI. But all of the top providers have one thing in common – a commitment to improving the employee experience.

A few examples of companies to keep an eye on:

• NICE: NICE has been pushing WEM as more than a product; it’s a strategy. Their CXone WEM suite includes AI-powered forecasting, real-time coaching, voice of the employee tools, and gamification in one system.
• Genesys: The Genesys WEM suite is packed with tools for elevating employee experience, AI-powered scheduling and forecasting, automatic quality assurance, workplace scheduling, and even sentiment analysis are all included.
• Five9: Another CCaaS mainstay, Five9’s WEM capabilities have evolved rapidly, especially in AI-assisted scheduling and integrated performance management. It’s a good choice for organizations already using Five9 as a contact center backbone, looking to consolidate their workforce tools under one vendor.

Companies like Calabrio, Verint, and even AWS, all offer their own solutions tied to contact center systems and business intelligence tools.

Need a closer look? Visit the WEM market map.

THE WEM MARKET MAP

Choosing the Right WEM Solution: What to Ask

Before jumping into demos and procurement cycles, smart buyers get clear on three things:

• Where does WEM sit in your wider CX stack? Is it standalone, or does it need to integrate tightly with CRM, CCaaS, or CPaaS tools?
• What metrics are you trying to move? Retention? CSAT? Schedule adherence? Time to competency?
• How usable is it at the agent level? A platform is only as good as the adoption it gets, especially from the people actually doing the work.

Remember innovation, too. Most of the leading WEM vendors in the market today are going all-in on artificial intelligence, governance management, and intuitive automation. Take advantage.

Implementation Best Practices for Maximum Impact

Getting WEM software in place is one thing. Making it part of how teams actually work is more complicated. Here’s what high-performing teams do differently:

Start with Outcomes, Not Features

Before rolling out dashboards or changing schedules, clarify what you’re trying to improve.

• Higher agent retention?
• Smoother hybrid workflows?
• Faster time-to-proficiency for new hires?

Let those outcomes shape the strategy, not the other way around.

Need help planning your rollout? Read: The New Best Practices for WEM

Embed Feedback Loops

Don’t wait for the annual employee engagement survey.

• Use in-session pulse checks
• Track sentiment shifts in real time
• Let agents rate coaching interactions, not just supervisors

The feedback is already there. WEM just needs to catch it, route it, and act on it. Done right, it becomes part of the daily rhythm, not another initiative.

Personalize Performance, Without Playing Favorites

WEM dashboards make it easy to rank people. But the real value comes from coaching individual patterns, not just posting leaderboards.

• Who’s improving fastest?
• Who’s flatlining under pressure?
• Who’s consistently solving complex issues, but missing soft skill marks?

Use that insight to tailor coaching, training, and recognition, not just for fairness, but for effectiveness.

Build for Flexibility, Not Just Control

Self-scheduling, mobile notifications, shift trades: these features are friction reducers. Give agents more control, and watch adherence go up, not down.

Especially for hybrid or global teams, asynchronous flexibility is a major retention lever, and WEM platforms make it manageable without compromising ops integrity.

Integrate with the Rest of the Stack

WEM works best when it’s not a silo. That means tight integration with:

• CRM systems (for customer context)
• Business intelligence tools (for strategy alignment)
• CPaaS or CCaaS platforms (for seamless routing and escalation)

If it’s not connected, it won’t scale.

Workforce Engagement: What’s Next

Workforce engagement management hasn’t finished evolving. In the same way customer experience has evolved from contact resolution to journey orchestration, workforce engagement management is shifting, from dashboards and reports to intelligent, adaptive systems that respond in real time to what’s happening on the floor.

The core function hasn’t changed: WEM exists to help teams perform better, stay longer, and deliver consistently great experiences. What’s changing is how that happens, and the speed with which enterprise buyers are now expected to keep up.

Here’s where WEM is headed next:

• Intuitive AI-driven coaching: Vendors are moving from QA scorecards to live feedback systems. Agents are no longer waiting for weekly reviews to know how they’re doing. With conversational analytics and sentiment detection now embedded in many WEM platforms, coaching happens mid-interaction.
• Personalization at the Team Level: Personalization isn’t just for customers anymore. The best WEM software is already adapting performance plans, learning modules, and scheduling rules based on individual agent profiles, considering skill levels, schedule preferences, and past coaching outcomes.
• Experience Data: Historically, “engagement” was something companies surveyed once a year and filed under HR. But in a high-churn, always-on contact center? That model fails. In 2025, Voice of the Employee (VoE) and EX metrics are treated as critical signals as important as CSAT or NPS.
• Improved Hybrid Orchestration: Hybrid teams aren’t going away. If anything, they’re expanding, especially in regions where talent is global and support demand is 24/7. Modern WEM tools now handle multi-time-zone scheduling, mobile shift self-service, and role-specific dashboards.
• Increased Convergence: The days of WEM as a standalone product are ending. It’s becoming part of something bigger, an integrated experience management layer that spans customer data platforms, CRMs, CCaaS ecosystems, and analytics tools.

Need an up-to-date view of the latest trends in the WEM landscape? Check out the latest research and reports, with insights taken straight from market leaders.

GET THE LATEST RESEARCH

Why Workforce Engagement Management is Crucial to CX

There’s a shift happening in enterprise CX. It’s not just about faster tools or lower handle times. It’s about creating operations that care about people as much as they care about performance. Because in every customer interaction, agents either lift the brand or quietly erode it.

When companies treat engagement as a measurable, manageable part of their CX operation, the benefits stack up fast:

• Lower attrition
• More consistent experiences
• Higher trust from agents and customers alike
• Operational efficiency that doesn’t burn people out to deliver it

WEM doesn’t solve everything. It gives teams the infrastructure to solve what matters, from burnout to broken feedback loops, without building from scratch every quarter.

Looking to upgrade? CX Today is here to help:

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Debuted at Verint Engage 2025, the bots aim to augment contact center workforce management (WFM) operations.

That’s crucial, as the WFM space hasn’t enjoyed the same transformative innovation as other realms of the contact center.

While generative AI may have moved the needle elsewhere, advanced WFM relies on more complex machine learning models and neural networks, especially in regards to forecasting.

As such, many still view WFM technology as an enterprise luxury, while smaller contact centers stick with spreadsheets and Erlang Calculators.

Yet, with hybrid human-AI teams, the already difficult job of a contact center resource planner is only becoming more so.

WFM teams need more accessible innovations to boost their toolkit. That’s what these two specialized bots aim to provide.

The Exact Forecasting Bot

The Exact Forecasting Bot (EFB) scours historical data across different contact center channels to speedily evaluate various forecasting models and apply the “best-fit” to a given situation. 

Such “situations” could be a holiday period, a what-if scenario, or business as usual.

To avoid added research, the EFB also explains why it chose a particular model while giving the planner space to test the model for themselves and make manual adjustments, based on their experience. 

Moreover, the bot will offer updates on new features, current trends, and continually analyze current forecast accuracy, so planners get helpful alerts that may help them to continually improve their forecasting strategy. 

Yet, it doesn’t just recommend models and surface insight; it also studies interaction patterns to recommend relevant adjustments, so it’s less of a static analytics tool and more of an AI assistant. 

The Intraday Spike Bot 

The Intraday Spike Bot (ISB) monitors real-time contact volumes to not only monitor spikes in contact volumes, but also highlight what is driving them.

Without this knowledge, contact centers cannot address the root cause of the issue and stem the flow of demand. Instead, they’re left to draft additional employees, adjust the IVR messaging, and follow other standard operating procedures (SOPs) that can be costly in expense and agent morale.

Yet, the ISB breaks down why customers are contacting, spots trends in their intent, and deduces the root cause. As such, contact centers can address it and ease pressure on the team.

Sometimes, the bot may suggest an action so that contact center managers, supervisors, and resource planners can quickly implement a fix.

Take a classic reason for demand peaks: marketing launched a secret campaign that nobody told the resource planning team about.

The bot could suggest a virtual agent update to deflect much of the incoming traffic. That’s massive for real-time response.

More from Verint

Alongside the new bots, Verint bolstered its portfolio with a Value Dashboard, showing savings from AI initiatives tied directly to KPIs in real time. That’s especially significant in highlighting the impact of AI on critical customer and agent outcomes, not just on specific cost measures.

However, despite these announcements, a shadow somewhat loomed over the event in Verint’s $2BN takeover by Thoma Bravo and the Calabrio merger.

News on what that means for Verint customers and the vendor’s go-forward is still muted. As the acquisition goes through regulatory approvals, that’s understandable.

Nevertheless, the vendor is pursuing its specialized bots strategy and bringing much-needed innovation to the WFM space.

After all, contact center leaders will increasingly manage AI agents, as well as human agents. That changes what WFM means. It’s no longer just tactical scheduling; it’s the orchestration of both humans and machines, understanding where AI alleviates, assists, and automates.

Despite its future uncertainty, few brands can do this as well as Verint.

The move aims to bolster RingCentral’s cloud contact center solution: RingCX, which it launched in late 2023.

Since then, RingCX has accrued over 1,000 customers. Many chose the CCaaS solution because of its close integration with RingEX, the widely implemented unified communications platform.

While that’s its biggest differentiator, RingCentral had all the baseline capabilities of a CCaaS platform apart from WFM, until now.

Already, CommunityWFM’s capabilities are available as “RingCentral AI Workforce Management (WFM)”, which starts at a pricing point of $20 per agent, per month.

Additionally, with RingCX’s automated quality assurance (QA) solution, RingCentral can ensure its solution delivers a broad range of workforce optimization (WFO) capabilities.

Celebrating the move, Kira Makagon, President & COO at RingCentral, said: “By providing CommunityWFM’s AI-driven workforce management capabilities together with our AI-first RingCX platform, we’re giving businesses the complete set of tools to optimize operations while empowering their people, creating the foundation for superior agent performance and effortless customer experiences.

Adding AI Workforce Management to our portfolio allows us to extend our AI-first innovation to a complete portfolio of AI-based products – starting from agentic AI assistance, to real-time guidance, quality management, analytics, and now AI-powered workforce management.

As Makagon referenced, CommunityWFM offers a deep contact center WFM portfolio, which comprises solutions for forecasting, scheduling, intraday management, and review. These tools are more advanced than those most CCaaS providers have attached to their solutions.

Interestingly, CommunityWFM is also a highly collaborative solution, enabling workforce management leaders to share messages and interact with agents and supervisors, so they’re not just the “computer says no” people in the background of the contact center. Instead, they’re an integral part of customer, employee, and business success.

Businesses of all sizes have implemented CommunityWFM, with multiple case studies covering enterprises in financial services, insurance, and utilities.

Excited to join RingCentral, Daryl Gonos, CEO & Co-founder at CommunityWFM, said: “RingCentral uses our Workforce Management platform that is integrated with RingCX to optimize their own customer support operations.

By leveraging AI-driven forecasting to deliver more accurate workforce predictions with significantly less manual analysis, we’re creating an intelligent, unified experience that not only simplifies today’s workforce operations, but also anticipates the future needs of hybrid work environments and evolving customer demands.

Lastly, given that RingCX most often attracts SMBs, this acquisition represents an opportunity for many brands in this segment to advance beyond spreadsheets and Erlang Calculators.

Now, it’s up to RingCentral to showcase the potential value-add these businesses can achieve by implementing a full-fledged WFM platform.

What Does This Acquisition Mean for the WFM Market?

CommunityWFM was one of the few remaining independent WFM market providers.

Indeed, NiCE scooped up Playvox and Dialpad rolled up Surfboard last year. Meanwhile, Verint and Calabrio announced a shock merger last month.

As such, Peopleware (formerly injixo) and Assembled are the only two independent vendors with a significant global presence.

Aspect did spin out of Alvaria last year to offer an alternative. Meanwhile, Eleveo can offer a broader workforce engagement management (WEM) suite.

Nevertheless, the options for third-party solutions are limited.

Some may suggest that, as CCaaS providers develop their own WFM solutions, these are now becoming outdated in this era of contact center convergence.

However, the likes of CommunityWFM, Peopleware, and Assembled can offer much deeper solutions than those offered by the CCaaS stalwarts, bar NiCE and possibly Genesys.

In addition, global enterprises often use different core contact center solutions across locations. For instance, some may use Cisco on-premise in one location, Genesys in another, and Five9 elsewhere. In such enterprises, the ability to overlay one third-party platform that integrates with each and centralize staff management is an attractive proposition.

Typically, they also offer tighter integrations with platforms outside the core CCaaS offering, like Human Capital Management (HCM) platforms, which can help businesses put employee and operational data to work.

As such, third-party WFM solutions have their place. Yet, the options are shrinking. Meanwhile, innovation across the space may suffer, with the lack of independent vendors channeling all their effort into the WFM market.

So, while the move is a smart snap up for RingCentral, which will not only boost its portfolio but initiate the provider with a raft of new customers, it offers some tough questions for the market in a broader sense.

That message isn’t limited to the customer experience space.

AI advocates argue that the technology can deliver such significant productivity gains that it may dramatically alter our day-to-day lives.

Whether through a four-day work week becoming the norm or personalized AI chatbots completing work and everyday tasks on our behalf, there is an often-touted, rose-tinted future.

However, Jensen Huang, CEO of NVIDIA, delivered some bad news in a recent discussion with Fox Business.

When quizzed about AI’s ability to change the world, he predicted: “We’ll actually be busier in the future than we are now.”

Expounding on this claim, the NVIDIA man argued that while AI will make many people’s jobs far more efficient. Ultimately, this will result in workers becoming more creative and experimental rather than embracing more free time.

“I’m always waiting for work to get done because I have more ideas, and most companies have more ideas to pursue,” he added.

The more productive we become, the more opportunities we have to explore those ideas. I expect GDP to grow, and I expect productivity to increase, and I’m hopeful about that.

Although Huang also mentioned that he “hope[s]” AI will enable people to have more leisure time on the weekends and be able to travel more, it is clear that he primarily sees AI as a tool that will help the economy “thrive”.

Regarding its overall impact on society, he admitted that AI would lead to changes. Indeed, much like OpenAI CEO Sam Altman recently predicted, Huang stated:

Some jobs will disappear, but every job will be transformed by AI.

How Might AI Transform Customer Experience Roles?

Huang’s assertion that every job “will be transformed by AI” might be hard to envisage for some sectors. After all, robot plumbers and carpenters turning up at the house still feels otherworldly.

However, AI is already having a tangible impact on the roles of service advisers, sales reps, and marketing associates.

A contact center agent is the classic example.

The Service Adviser 2.0

Customer-facing virtual agents are being deployed to handle simple, transactional customer queries. As a result, human service advisers are taking on complex queries.

Yet, as virtual agents take on more customer contacts, don’t expect the human advisers to disappear altogether.

Thanks to a greater focus on journey orchestration, the role may evolve into more of a customer success position, offering a human touch at carefully defined touchpoints.

As such, they may offer that vital reassurance that ensures retention and delivers upsells and cross-sells.

The Sales Rep 2.0

In sales, AI first targeted administrative tasks, such as scheduling meetings, updating CRM records, and summarizing call notes.

Yet, sales reps are now also benefiting from the development of more mature AI technologies and intelligent orchestration.

Indeed, AI is assisting sales reps in the flow of their work. How? By surfacing the best leads, preparing tailored collateral, and spotting risks in conversations.

In doing so, it will allow sales reps to spend less time wrestling with tools and more time building trust with customers.

The Marketing Associate 2.0

While AI already handles repetitive creative work, like reformatting assets across platforms, it’s also driving a deeper change in how campaigns are built and delivered.

Just as SEO once reshaped digital strategy, brands will soon compete to optimize their visibility within large language models (LLMs), ensuring their products are surfaced when AI tools make recommendations, alongside brand-positive content.

That will require new skills, closer governance, and a workforce blend where AI handles execution while human marketers focus on creativity, ethics, and brand storytelling.

Yet, in all cases – whether in service, sales, or marketing – the pattern is the same: AI takes on the heavy lifting, while people lean into empathy, strategy, and innovation.

For customer experience leaders, establishing that balance could prove a real differentiator.

The traditional mindset of deploy, hit ROI targets, and move on doesn’t always work here.  

As Erik Delorey, Director of Innovation at Miratech, puts it when describing the problems of legacy approaches to tech investments:  

“You invest this money, you get this ROI, and then you move on to the next project.  

“And what happens in AI systems that aren’t maintained, optimized, or reinvested in? They’re going to drift.” 

One problem is that AI isn’t just a piece of software; it’s an ongoing partnership between machine learning and human learning.  

The technology continues to evolve, but the people using it often don’t. 

Budgets get approved for deployment, yet follow-up training rarely happens.  

As a result, systems continue to improve while the humans using them fall behind, creating a slow, almost invisible decay from that initial ‘wow’ moment when ROI looked promising.  

Continuous Attention, Not One-Off Projects  

For Delorey, it doesn’t matter whether you call it ongoing development, iterative deployment, or regular optimization; without “constant attention and investment,” your AI deployment will stall.  

For CX leaders, the takeaway is simple: AI projects aren’t checkboxes; they’re living systems that need technical upkeep and human support to thrive.  

And one of the keys to maintaining a healthy AI lifecycle is transparency.  

“Getting reasoning visibility right is crucial,” Delorey explains.  

“If your platform doesn’t offer it out of the box, push the vendor or build a custom solution. You need to see how key decisions are made.” 

This isn’t just a technical nicety; it’s how organizations can spot gaps, adjust outputs, and make sure AI evolves with customer needs.  

Reviewing reasoning data every two to three months, monitoring performance metrics, and retraining staff keeps the human-machine partnership alive.  

Delorey suggests thinking of AI like a car: skip the oil changes, and you’ll pay later. Skip data updates, agent training, or attribute reevaluation, and the same principle applies.  

Yet budgeting for ongoing AI investment is often the most challenging part. Initial ROI is easy to justify, but continuous improvement often gets overlooked, as Delorey explains:  

“Companies get into subscription models to avoid annual maintenance fees, but where’s the 15% that should go back into continuous improvement?”  

AI ROI as a Journey  

At its core, AI implementation is no different from any other investment. When you get past all the bells and whistles, what businesses really want to see is how it impacts the bottom line.  

The challenge with AI is that its ROI often isn’t as apparent as other tools; it isn’t a single payoff, it’s a journey.  

While stage one might deliver some immediate results, stages two and three are where the real impact often emerges, through refinement, learning, and optimization.  

Companies that embed this mindset into their procurement, project design, and leadership culture are far more likely to get lasting value out of their AI deployments and help employees grow alongside the technology.  

This is the philosophy Miratech applies to its services.  

By combining technical support with human training and iterative improvements, they ensure AI deployments stay aligned with business goals.  

AI becomes less of a static product and more of a part of a dynamic ecosystem, evolving alongside the people who actually use it.  

Asking the Right Questions  

Another way in which organizations can maximize the potential of their AI implementations is by remaining skeptical and curious.   

Leaders should always ask: how will this platform stay relevant six months from now? How are human insights captured to refine AI outputs? And perhaps most importantly, how can efficiency gains be reinvested to fuel ongoing innovation?  

Delorey advises:  

“Don’t take the money and run; reinvest for greater rewards.” 

These aren’t one-off procurement questions – they’re the recurring checkpoints that keep AI effective and aligned with customer needs. 

AI doesn’t fail overnight; it drifts. 

But AI thrives when it is refined, adapted, and paired with human growth.  

Organizations that embrace this mindset can turn temporary boosts into sustained, transformative impacts.

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

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