AI, ROI, and customer pressure: less hype, more results in your veins 🚀
Artificial intelligence is already front and center in practically every serious conversation about technology, collaboration, and customer service. At heavyweight events like Enterprise Connect in Las Vegas, that became even clearer: almost every vendor took some kind of AI announcement to the stage. Even giants like Google and Microsoft, which were not physically present, used the same week to announce AI features focused on productivity in their office suites.
But the mood has shifted. Blind excitement over new features has given way to a much more grounded mindset. Today, anyone buying technology wants clear answers: where does this AI generate returns? How does it cut costs, improve operations, increase revenue, or enhance customer experience?
The message from customers to vendors is simple: enough with the vague promises, show the ROI.
The real state of AI adoption in businesses
A global study from Metrigy, called AI for Business Success: 2025-26, conducted with around 1,100 organizations, paints this picture well. AI adoption is high, with almost all companies using some kind of capability in some part of their operation. But in practice, the return is still not that clear.
According to the research, only 54.2% of companies say that AI benefits have outweighed the costs. In other words, just over half truly see that the investment is financially paying off.
Where does the market see the most value today?
- Increased employee productivity
- Reduction in operational costs
- Improved customer service metrics
- Revenue growth in contact and sales channels
In theory, all of this should be easy to measure. In practice, many projects are still running without clear before-and-after metrics, which makes it harder to prove ROI objectively to the executive team.
From hype to show me the money
While the ROI discussion heats up inside companies, a recent episode showed that this pressure is not limited to the traditional corporate world. OpenAI announced the end of Sora, its video generation tool, precisely due to a lack of adequate financial returns.
Sora grabbed a lot of attention, went viral on social media with creative videos, and generated massive buzz. But in the end, it failed to sustain itself as a product that justified billions of dollars in investment. Being cool was not enough. It had to make money.
The same mindset is very much alive in IT and business areas. Leaders want to see AI applied to concrete problems with measurable impact. Product marketing based only on shiny features, visual flair, or generic promises has lost steam.
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At Enterprise Connect, the shift was clear. Instead of generic AI presentations, many vendors chose to showcase highly specific solutions tied to business pain points, including automation, customer experience, and workflow improvement.
Real-world use cases: AI as a results multiplier
Some examples presented at the event help show how the conversation is more mature and focused on real value.
NiCE and the use of agentic AI for continuous improvement
NiCE showcased innovations in agentic AI, a type of system where AI not only responds but also takes initiative. The idea is to use signals captured from customer conversations to identify opportunities for improvement in processes, service, and operations as a whole.
Instead of being just a passive assistant, the AI analyzes patterns, suggests adjustments, and even automates part of the rollout of those improvements. The focus is clear: optimize the business based on real interaction data and, with that, generate measurable gains in efficiency and quality.
RingCentral and AIR Pro for lead capture and churn reduction
RingCentral went straight into sales and service scenarios with AIR Pro, its virtual AI receptionist solution. The tool sits at the front line of contact, helping companies to:
- Capture more leads without relying only on the human team
- Reduce appointment cancellations and no-shows
- Deliver more consistent and personalized service
The value here is relatively easy to explain: more opportunities captured and fewer empty time slots. In ROI language, that means more revenue and less waste.
Zoom and Agentic AI 3.0 connecting front and back office
Zoom introduced Agentic AI 3.0, focused on automating workflows between front office (customer-facing) and back office (internal operations) teams. The AI helps, for example, turn meeting notes into actionable documents, task lists, and automatic follow-ups.
This cuts the time spent on manual organization tasks, prevents important information from getting lost, and speeds up decision-making. Again, the value shows up in productivity and reduced rework.
What all these initiatives have in common is using AI as a workforce multiplier, automating, optimizing, and connecting processes to deliver a much more tangible ROI.
Verticalization: AI speaking the language of each industry
Another strong trend at Enterprise Connect was the bet on vertical AI solutions, meaning tools designed for specific industries, instead of generic platforms meant for any kind of company.
Vendors such as GoTo and RingCentral itself brought health-focused innovations, aligned with HIMSS, a major healthcare technology conference that was taking place right next to the main event.
In these cases, AI is applied to highly specific workflows such as:
- Managing appointments and confirmations
- Secure communication between patients, clinics, and care teams
- Optimizing internal processes in hospitals and medical offices
When the solution is built around the way a given industry actually works, ROI becomes much easier to prove: fewer missed appointments, fewer process errors, faster service, better patient experience, and smarter use of resources.
Google, Microsoft, and the race for AI-powered productivity
Outside the Enterprise Connect stage, but in the same week, Google and Microsoft also reinforced this new AI phase, more focused on immediate day-to-day value for teams.
Google Workspace with AI in the workflow
Google announced additional AI features in Workspace, aimed at speeding up content creation and manipulation using natural language in tools like Sheets, Docs, and Slides.
In practice, this lets people use simple language to request table creation, document structuring, or presentation building. The obvious target here is tools like Canva and Gamma, which have been gaining ground as AI-first solutions for visual and text content creation.
The goal is clear: reduce the time and effort needed to produce quality content, making the productivity suite more competitive and adding real value to the work environment.
Microsoft Copilot Wave 3 and the new E7 bundle
Microsoft fired back with Copilot Wave 3, expanding Copilot’s capabilities for content creation, agent management, and integration with security and operations.
A key highlight was the launch of the E7 license, which bundles into a single package:
- Microsoft 365 E5
- Copilot
- Agent management features
- Security capabilities
This bundle is not a direct productivity feature, but it can lower the barrier to entry for companies that want to use Copilot at scale, unifying billing and simplifying adoption. In other words, it can help improve cost-effectiveness, which directly impacts the ROI equation.
Automation alone is not enough: processes must be redesigned
A critical point in this transition to more mature AI usage is understanding that simply automating a bad process does not solve the problem. If a workflow is inefficient, full of unnecessary steps, or poorly designed, throwing AI on top of it will just make the mistakes happen faster.
Business process optimization is an old concept, but it gets new life with AI. Instead of just speeding up what already exists, the idea is to use intelligent analysis to redesign the flow, remove friction, reorganize steps, and only then automate what makes sense.
The big ROI leap comes less from blind automation and more from combining process redesign with strategically applied AI.
Open, integrated, and silo-free AI
Another key factor to capturing real value is making sure AI does not live in black boxes inside the organization. An agent, assistant, or chatbot can only reach its full potential if it has broad access to the data it needs.
That means connecting:
- Internal applications, such as CRM, ERP, and support tools
- External sources, through specialized protocols and integrations like MCP and A2A
- Knowledge bases segmented by department, product, and customer type
Fortunately, the market is moving in that direction. More and more vendors are adopting models and protocols that let data flow between systems, boosting both the accuracy and impact of the recommendations, automations, and analytics generated by AI.
What stands out in this new phase of enterprise AI
The takeaway from studies like Metrigy’s and events like Enterprise Connect is very straightforward: AI is no longer a lab toy and has solidified its role as a strategic tool. But that also means expectations have gone way up.
- Companies already use AI at scale, but not all of them see consistent returns
- The focus has shifted from impressive features to use cases with provable financial impact
- Specific industries, such as healthcare, are starting to capture value with vertical solutions
- Giants like Google and Microsoft are doubling down on productivity baked into work tools
- The next big value frontier lies in redesigning processes, not just automating what already exists
- AI that is integrated, open, and connected to the right systems tends to deliver the strongest ROI
At the end of the day, the message from buyers to AI vendors is simple but game-changing: it is not enough to be innovative, you have to prove you pay your own way and still leave profit on the table.
About Metrigy
Metrigy is a research and advisory firm focused on rapidly evolving areas such as workplace collaboration, digital workplace, customer experience, employee experience, and related technologies. Their work combines primary data, metrics, and analysis to support both technology vendors and organizations looking to make better-informed decisions about adoption, strategy, and outcomes measurement.
About the original author
Irwin Lazar is president, principal analyst, and cofounder of Metrigy. He leads research projects, conducts primary studies, and advises companies and vendors on topics such as workplace collaboration, hybrid work, unified communications, video conferencing, operations, compliance, and security. He is a Certified Information Systems Security Professional (CISSP), as well as an author and frequent speaker at events like Enterprise Connect, where he was recognized in 2017 as an Emerging Technologies Fellow by the IMCCA.
