AI Buyers Are Pushing Back on Vendors: Show Us the Real Return on Investment
Artificial intelligence has been at the center of nearly every conversation in the corporate world over the past few years. And for good reason: AI showed up promising to transform how companies operate, serve their customers, and collaborate internally — and it genuinely delivered some impressive stuff along the way.
But the mood is shifting — and fast. 🚀
Anyone who attended Enterprise Connect in Las Vegas noticed a major shift in the tone of the discussions. AI advancements dominated virtually every presentation and vendor booth. Even companies like Google and Microsoft, which notably did not have a physical presence at the event, chose Enterprise Connect week specifically to drop AI announcements aimed at their productivity suites. That tells you everything about how much weight this conference carries in the industry.
But despite all that buzz, the companies doing the buying walked into meetings with a very different question on their minds: where is the real return on all of this?
The hype took a back seat. What is front and center now is ROI — measurable, concrete, and impossible to brush off. Showing off a shiny new feature or a slick demo is no longer enough. The market wants to see specific solutions that solve real problems, drive actual productivity, and justify every dollar spent.
And that is exactly what we are going to talk about here. 👇
The Current State of AI Adoption in the Enterprise
The numbers do not lie — and they paint a pretty interesting picture. The global study AI for Business Success: 2025-26, conducted by Metrigy with roughly 1,100 organizations, revealed that AI adoption is at all-time highs. Virtually every company surveyed is already using AI in some form across their operations. That alone would have been a headline a few years ago, but today it is practically a baseline requirement for staying competitive.
The most revealing finding from the study, though, lies in a different data point: only 54.2% of companies say that AI benefits have outweighed the costs so far. In other words, nearly half of the organizations that invested in artificial intelligence still have not seen a return that clearly justifies the spend. That number explains a lot about the shift in posture happening across the market right now. Companies are not giving up on AI — far from it — but they are getting much more demanding about what they expect from it.
According to Metrigy’s data, organizations see AI’s greatest value in helping employees become more productive. In theory, that leads to measurable reductions in operational costs, along with improvements in customer service metrics and revenue growth. The challenge is turning that theory into documented practice — and that is exactly where many companies are still stumbling.
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The Market Has Turned the Page on Hype
For years, the cycle was more or less predictable: a new technology would appear, the promises would fill conference stages, budgets would get unlocked, and teams would scramble to implement. With AI, that cycle happened at an accelerated pace, possibly faster than anything the enterprise tech world has ever seen. In almost no time, practically every major company on the planet had some kind of artificial intelligence project running — or at least announced. The problem is that announcing and delivering are very different things.
One example that illustrates this reality in a pretty striking way is the case of Sora, OpenAI’s video generation tool. Sora had that undeniable wow factor and flooded our social feeds with AI-generated videos that ranged from jaw-dropping to hilarious. But OpenAI announced it was shutting the tool down due to lack of revenue. Despite billions of dollars invested, the company never managed to turn Sora into a revenue source that justified the spend. It is a powerful reminder that impressing people is not the same as generating business value.
That same perspective extends across the corporate landscape. IT and business leaders want to see AI that delivers real results, often specific to their particular industry. Marketing and product approaches that simply highlight new features no longer work as a starting point for a business conversation. What Enterprise Connect made crystal clear is that executives are tired of investing in initiatives that cannot prove their value in objective terms.
The conversation has shifted from how do we implement AI? to what is this AI actually generating in concrete returns for the business? That transition might seem subtle, but it represents a huge level of maturity in the market. It means companies have learned to question, to measure, and to demand more from their vendors and technology partners. And at its core, that is actually great news for the entire ecosystem.
Specific Solutions Already Delivering Value
Instead of just talking about AI’s potential, several vendors at Enterprise Connect showcased specific solutions powered by AI that are already generating measurable impact on their customers’ businesses. A few examples that stood out:
- NICE introduced innovations in agentic AI that enable agents to automatically identify and implement business improvement opportunities based on signals captured within customer conversations. This transforms everyday interactions into actionable insights for the operation.
- RingCentral launched AI Receptionist (AIR) Pro, featuring sophisticated capabilities that improve businesses’ ability to capture leads, reduce appointment cancellations, and differentiate themselves through enhanced customer service.
- Zoom unveiled Agentic AI 3.0, which enables workflow automation across the front office and back office and allows individuals to turn meeting notes into actionable documents.
In all of these cases, the AI initiatives are designed to let customers automate and optimize work using artificial intelligence as a force multiplier. In theory — and increasingly in practice — this generates measurable ROI. When the focus shifts from experimentation to outcomes, automation stops being an isolated project inside an innovation lab and becomes part of the core business strategy. That is when things get serious — and interesting. 🎯
Vertical AI: Solutions Built for Specific Industries
Another trend that grabbed attention at Enterprise Connect was the push toward vertical AI, with vendors developing solutions tailored to specific industries. Both GoTo and RingCentral made announcements targeting healthcare, taking advantage of the proximity to the HIMSS health technology conference, which took place right alongside Enterprise Connect.
In each case, the vendors are applying artificial intelligence to optimize workflows specific to the healthcare sector, offering concrete potential for measurable returns. This move toward verticalized solutions makes perfect sense when you think about the ROI logic: the more tailored the tool is to the real-world context of use, the faster and clearer the benefit becomes for the customer.
It is no coincidence that the companies demonstrating the best return on AI investment are the ones that chose to tackle very well-defined problems within specific industries. Healthcare, financial services, retail — each sector has its own particular pain points, operational bottlenecks, and regulations. A generic AI solution might help to some degree, but a solution designed with deep understanding of the industry context has an infinitely better chance of generating real impact.
Google and Microsoft Double Down on Productivity
Outside of Enterprise Connect, two announcements drew particular attention and reinforced the trend of what can AI do for me right now?
Google launched new AI capabilities within Workspace, designed to accelerate document creation through natural language interaction with Sheets, Docs, and Slides. This initiative is a direct response to the rise of AI-first content creation tools like Canva and Gamma, which have drastically reduced the time and expertise needed to produce high-quality content.
Microsoft, meanwhile, announced Copilot Wave 3, which adds similar content creation capabilities along with agent management and a new bundled license called E7. This license combines Microsoft 365 E5, Copilot, agent management, and security into a single offering. While the new license is not a productivity improvement in itself, it potentially lowers the cost of entry for customers who want to use Copilot to boost their teams’ productivity.
Both moves show that even the tech giants are adjusting their messaging — and their products — to meet a demand coming straight from buyers: less promise, more tangible delivery.
ROI Is Not a One-Time Number, It Is an Ongoing Conversation
There is a common trap when it comes to ROI on AI projects: companies try to calculate the return before they even understand the problem they are trying to solve. The result is a pretty spreadsheet that does not hold up in practice. Real ROI starts long before the spreadsheet — it starts with precisely identifying the bottleneck, the inefficient process, the repetitive task that eats up time and energy without adding strategic value. It is only when you have clarity about the problem that you can truly measure the impact of the solution.
Forget generic platforms that promise to solve everything at once. What is actually driving results are tools built for specific contexts — whether that is customer service, contract management, document triage, sales data analysis, or internal workflow automation. The more surgical the solution, the easier it is to measure the before and after, and the more compelling the case becomes to keep investing.
Another point that came through loud and clear in the discussions is that ROI on AI rarely shows up all at once. It accumulates. An automation that saves one employee 20 minutes a day might seem small on its own, but multiply that across 200 people over an entire year and you get a number that any CFO will pay attention to. That is why productivity has taken center stage in these conversations — it is the most direct link between what the technology does and what the business feels in its bottom line. 🔄
The Important Warning: Automating Is Not the Same as Optimizing
There is a fundamental caveat that deserves attention, and it came up strongly throughout industry discussions. Letting AI optimize workflows certainly delivers benefits. But the real value likely comes from using artificial intelligence to optimize the design of the workflows themselves, not just execute them faster.
Simply automating a broken, inefficient process is not going to deliver the same value as redesigning and optimizing that process before automating it. The term business process optimization has been around for a long time, but AI offers a renewed promise of delivering measurable benefits through process reengineering when needed.
The most successful companies on this front are the ones that pause to ask the hard question before automating everything in sight: should this process even exist in this form in the first place? When the answer is no, AI enters first as a diagnostic and redesign tool, and only then as an automation engine. That sequence makes all the difference in the final results.
An Open and Connected Future
Finally, a crucial point that is gaining more and more relevance: AI can only be truly beneficial if it is part of an open ecosystem, rather than locked away in isolated silos. An AI agent or chatbot needs access to data both within directly connected applications and from third-party data sources, via protocols like MCP and A2A.
The good news is that vendors are increasingly moving in this direction, expanding the interoperability of their solutions and improving AI’s ability to access information wherever it lives. This is essential for ROI promises to materialize, because an AI that can only see half of the relevant data will inevitably deliver half of the potential value.
Productivity as the Central Metric
If there is one word that ran through every single discussion at Enterprise Connect from start to finish, it was productivity. Not productivity in the sense of doing more with less — that is a reductive take that ignores the human cost of excessive pressure. The productivity the market is chasing now is the kind that frees people up to do what they do best: think, create, solve complex problems, build relationships. AI and automation step in as support, as infrastructure, as the resource that absorbs repetitive, low-value work so teams can operate at their highest level.
This perspective has a direct impact on how companies choose their tools. Instead of going after the flashiest solution or the one with the most aggressive marketing, they are looking at specific use cases within their own operations and asking: is this going to make my team more effective? Will it cut down the time they spend on tasks that do not add value? Will it give me better data to make faster decisions? When the answer is yes and the numbers back it up, the investment justifies itself — no slide deck or sales pitch required.
What is becoming clear is that the companies that managed to translate AI into real productivity gains did not do it by accident. They defined metrics before implementing, tracked results closely, and adjusted course whenever needed. That requires a data-driven culture and a willingness to iterate — two things that, fortunately, the technology itself is helping build within organizations. The cycle feeds itself: more data leads to better decisions, which lead to more efficient implementations, which generate more ROI, which justify more investment in automation.
At the end of the day, what the market is saying — loud and clear — is that the era of blind bets on technology is over. AI has proven it has substance. Now it needs to prove, project by project, process by process, that it can turn that substance into concrete results. And the companies that figured this out early are pulling ahead — not because they have the biggest budget or the most technical team, but because they asked the right questions before pressing the button. 💡
