Generative AI has entered the trough of disillusionment
Generative AI isn’t magic. It has potential, yes, but right now it’s caught in a phase where expectations no longer match the results. Gartner calls it the “trough of disillusionment,” and they’re right. Many companies jumped in fast. They expected turn-key systems that could spit out value with minimal effort. That didn’t happen.
Earlier, you heard phrases like “just feed in your data and it’ll work beautifully.” In reality, using gen AI effectively takes a lot more thought. Without robust performance checks, these systems don’t scale across an enterprise in any meaningful or dependable way.
Enterprises are figuring out that deploying generative AI tools in operational workflows without testing, context tuning, and infrastructure support leads to poor results. And poor results erode internal support fast. There’s now a kind of AI fatigue setting in, not from lack of interest, but from unmet expectations. Quick-win pilot projects didn’t deliver. Senior teams who backed the tech early are now being asked tough questions about impact and ROI.
That doesn’t mean the tech failed. Far from it. The capabilities are there. But many simply misjudged the work required to get business value out of it. You need architecture. Governance. Real performance benchmarks. You don’t get scale from a demo version.
Gartner’s current positioning? They expect generative AI to move out of this disillusionment phase in two to five years. That’s a fair runway. But whether your company gets value now or waits depends on how thoughtfully you’re deploying the tech.
Birgi Tamersoy, Senior Director Analyst at Gartner, put it simply: the hype cycle didn’t reflect the underlying reality. Success takes structured deployment and serious investment in reliability and performance. That’s always true with transformative tech.
Reliability concerns and unpredictable outputs are undermining adoption
Another issue we can’t ignore, users aren’t confident in what they’re getting back from gen AI systems. The output swings from impressive to useless with little consistency. When a tool can’t be trusted to deliver the same quality twice, it gets pulled out of production environments fast.
This problem is better known as “hallucination.” These models generate answers based on probabilities, not truths. So, they confidently get things wrong. That’s fine if you’re brainstorming ideas. It’s not fine if you’re automating customer support or financial analysis.
Executives are seeing this first-hand. You test the system once, looks good. You run it again under pressure, it breaks. That unpredictability stalls adoption. Teams become hesitant. Trust drops.
Dmitry Mishunin, CEO of Doitong, summarized it well: “Generative AI is now a mystery box game. You can get a masterpiece, or you can get something unusable.” That’s real. And it’s costing time and experimentation budget.
This is a warning sign for anyone expecting autonomous systems to take over key processes. If the base models aren’t stable, their agent-based forms, automatic systems working on their own, won’t be reliable either. Push-button deployment isn’t happening yet. Today, you need humans in the loop.
For C-suite teams, the message is this: experiment, but don’t forget validation pipelines and governance models. If your system can’t be trusted for consistent output and your team doesn’t understand when it might fail, then it’s not ready for core workloads. Work closer with engineering, test extensively, build safety walls, and you’ll reduce risk without slowing innovation.
This isn’t failure, it’s iteration. The systems will improve. But your deployment decisions now shape whether those improvements lead to business value or sunk cost later.
High failure rates in AI pilot projects highlight the need for enhanced robustness and scalability
A lot of AI pilots are failing. That’s not surprising. Most of them were rushed. They didn’t have the architecture or checks needed to operate in a live business environment. When a generative AI system can hallucinate at any time, running it without guardrails becomes a liability.
What’s missing most is robustness. You can train a decent model. You can deploy a proof of concept. But when the real-world data hits, and the volume increases, systems buckle. That’s what we’re seeing now. The jump from prototype to production is larger than most predicted.
Enterprise-grade AI doesn’t just need good output. It needs repeatable, high-confidence results. It needs surrounding systems, monitoring, fallback logic, human-in-the-loop interfaces, language controls, and performance tuning repeatable at scale. Without those, your pilot collapses the moment ambiguity enters the input.
Birgi Tamersoy at Gartner framed this clearly. According to him, “What you put around [AI] to increase that robustness and reliability makes a huge difference in terms of success.” Simple models won’t carry high-risk processes on their own. You need layers of control and rigorous testing before moving forward with full deployment.
C-suite leaders should not view pilot failures as technical setbacks, they are strategic signals. If your pilot fails and you don’t understand why, that’s not an AI limitation. It’s a process failure. Establish clear KPIs. Monitor edge cases. Design exit criteria when quality slips. This forces clarity and minimizes risk. If the system doesn’t perform in testing, it’s not ready for business logic. Treating it as such protects the company, budget, and customer trust.
This is the stage where pressure and scale expose weakness. The lesson? Weak infrastructure won’t stabilize a great idea.
Rising energy and computational costs are raising ROI concerns for enterprises
Generative AI isn’t cheap. That’s becoming more obvious as companies move beyond experimentation and try to operationalize at scale. Training large language models, running inference on complex requests, and processing high-volume inputs require real computational power. And that doesn’t come free.
Some companies are now seeing energy bills in the millions just to keep AI systems running. That adds up fast, especially in industries with tight margins or high transaction volume. This raises the central question: is the benefit worth the cost?
The answer depends entirely on how you’re using the tech. If the system is augmenting productivity, improving output accuracy, speeding workflows, or unlocking new business models, then yes, high energy costs can be justified. But if your deployment is limited to one department, or if hallucination rates remain high, every watt starts to count against ROI.
This is about strategic alignment. Deploying gen AI without first calculating the total cost of ownership, including compute, storage, retraining, and integration, is short-sighted. High model complexity, when matched with minimal value output, leads straight to negative returns.
Birgi Tamersoy from Gartner pointed this out explicitly: energy costs can run into the millions, and leaders must determine whether the benefit justifies that burden. Today’s generative models are still evolving, and optimization is underway, but that doesn’t make current costs disappear.
For decision-makers, now is the moment to shift from enthusiasm to economics. Every fraction of a second in model latency has an environmental and financial cost. Use cost-aware architectures. Prioritize model efficiency. Select vendors with energy transparency. And make sure each deployment path is tied to measurable business value. If not, costs will scale faster than benefits.
Usage-based pricing models are constraining experimental innovation in gen AI
One of the biggest sticking points holding back experimentation with generative AI is how it’s priced. A lot of platforms charge per successful output. That approach rewards efficiency, but it severely limits the kind of open-ended testing needed to improve a system over time.
Right now, results in gen AI are inconsistent. That’s widely acknowledged. To fix that, you need trial-and-error. You need testing conditions that don’t penalize iteration. Instead, many pricing models now discourage heavy experimentation because you’re paying for every usable result, even if dozens of prior trials failed.
The cost doesn’t just hit your budget. It also impacts culture. Teams stop trying things. They scale back curiosity. Eventually, the product vision gets narrower. Instead of discovering new use cases, you’re just maintaining what you already have. Progress slows.
Dmitry Mishunin, CEO of Doitong, raised this issue directly. He believes once services start charging for final results only, and allow generosity in experimentation, the pace of industry growth will accelerate. Right now, the opposite is happening. Rigid billing strategies are forcing teams to limit their creativity before the system even hits scale.
Executives should evaluate vendor pricing models not only in terms of pure usage costs, but in terms of what they unlock, or restrict. The ability to run tests without constraints is key. That’s how you train internal teams to trust the model, to learn from iteration, and to build better internal tooling. If every experiment comes with a penalty, fewer people will experiment.
Mistrust in AI agents reflects broader reliability issues in gen AI systems
AI agents, automated systems that act based on generative AI models, are hitting a wall. The reason is simple: people don’t trust them. These agents rely on large language models (LLMs) for decision-making. And when the language model lacks reliability, the agent does too.
If you can’t trust the brain, you can’t trust the automation. Enterprises aren’t ignoring this. In fact, deployment is limited. Many businesses recognize the risk and are holding off until they see stronger capabilities and more evidence of stability.
Mike Sinoway, CEO of Lucidworks, shared a sharp insight: his company’s 2025 State of Generative AI report found that only 6% of e-commerce firms have partially or fully rolled out an AI agent. Even more telling, two-thirds don’t have the infrastructure needed to support agentic systems at all. That’s not reluctance, that’s structural lag.
The reason isn’t fear of automation, it’s the reliability of the AI underneath. Gen AI still hallucinates, lacks predictable context management, and sometimes fails to complete tasks as expected. That behavior doesn’t inspire confidence. Especially not when these systems are expected to act independently across real business workflows.
Birgi Tamersoy from Gartner explained the situation candidly. “You cannot automate something that you don’t trust,” he said. And it’s true. When AI agents are powered by models that can’t be easily interpreted, their decision paths become opaque. That’s when executives hesitate.
For leaders, this presents two actions. First: don’t buy into AI agents until you’re certain the core models supporting them are reliable. Second: invest in the infrastructure, governance, and orchestration tools that improve oversight, transparency, and fallback handling.
The future of AI doesn’t lie in one model solving everything on its own. It lies in coordinated intelligence, where systems exchange roles, share workload, and operate under tighter control. Confidence in that will come with better interfaces, better coordination, and better results. Until then, trust, rightfully, remains earned, not assumed.
Composite AI offers a promising path to overcome the limitations of standalone AI models
Generative AI on its own has limits. On text tasks, it’s strong. But accuracy, context control, and alignment with business logic still fall short. That’s where composite AI comes in. It’s not one system, it’s a structured blend. By combining generative models with other methods like traditional machine learning, rules-based systems, and computer vision, you get more reliable, task-specific outcomes.
This is the approach forward for real-world enterprise use. Rather than relying entirely on one model to understand, generate, and act, composite AI integrates several capabilities. So when one system lacks precision or confidence, another fills that gap. That coordination reduces the noise, improves accuracy, and supports outcomes tied to actual business dependencies.
Birgi Tamersoy at Gartner sees this strategy as essential. He defines composite AI as a mix of multiple AI approaches used together to exploit their individual strengths while compensating for each model’s weaknesses. It doesn’t eliminate risk, but it calibrates it.
Enterprises that take this seriously are redesigning their AI stacks. They’re not replacing gen AI, they’re layering it. Visual recognition and predictive models sit alongside generative tools. Agents are monitored with traditional business rules. And orchestration technologies sit between it all, aligning inputs and outputs to the workflow in a smarter way.
For executives, the takeaway is clear: don’t look for a single AI product to handle your operation. Build your system by design, not by default. Prioritize frameworks that allow you to plug in different AI components, test their reliability, and coordinate their usage. That’s how you reduce disruption, increase valid outputs, and drive real performance gains.
Comprehensive model evaluation frameworks are crucial for unlocking gen AI’s business value
The early wave of generative AI adoption ran on excitement more than discipline. Many leaders moved quickly without asking a central question: how do we evaluate these models before scaling them? That gap in due diligence directly led to inflated expectations and inconsistent outcomes.
Without a framework, something that tests models for accuracy, bias, stability, and business relevance, you don’t know what you’re deploying. And when things go wrong later, the fix becomes more expensive and harder to scale.
Richard Sonnenblick, Chief Data Scientist at Planview, explained it this way: “We overestimated AI’s potential in the near term because we didn’t have a rubric for model evaluation.” He’s right. The conversational interfaces were new, accessible, and they dazzled in demos. But very quickly, teams realized that performance under pressure didn’t match isolated test cases.
Now the shift is happening. Enterprises that stay competitive will develop, or adopt, standardized model evaluation protocols. That includes benchmarks for use-case accuracy, hallucination rate checks, stability under different workflows, and alignment with regulatory frameworks.
For C-suite leaders, the message is direct: don’t wait for failure to assess quality. Embed evaluation into early prototyping. Use internal auditors and domain experts to define threshold metrics that models must meet before going live. Be detailed about where and how you measure gaps.
This is about making that progress sustainable. Systems that can’t be measured can’t be trusted. Systems that can’t be trusted don’t scale. To capture real value, apply the same engineering discipline here that you would in any business-critical system. That’s how you move from charm to impact.
Long-term optimism for gen AI remains if strategic improvements are made
Generative AI isn’t done. It’s in a correction cycle, not a collapse. What we’re seeing now is a calibration phase, models are getting reviewed, deployment methods are getting smarter, and leadership teams are asking better questions. That’s progress.
The long-term potential of gen AI continues to be significant. With better model reasoning, curated training datasets, strong orchestration systems, and clearer validation layers, productivity gains and new capabilities remain absolutely achievable. The impact won’t come from hype. It’ll come from well-structured systems and execution grounded in metrics, not marketing.
Mike Sinoway, CEO of Lucidworks, stated this directly: “The next breakthroughs won’t come from individual agents working alone. They’ll come from orchestration systems that route tasks to the most cost-effective models.” That’s where things are headed. Smart coordination between components, not isolated performance from one.
Richard Sonnenblick of Planview added another key point. Even if only a small number of generative AI projects produce tangible value, say one in a hundred, the cumulative return could be significant enough to justify the overall investment. Most innovation economies work like that. You don’t need every attempt to hit. You need the infrastructure and risk mindset to capture the ones that do.
Birgi Tamersoy of Gartner agrees that the value is still there, just not automatic. CIOs and strategy teams need to assess these tools carefully. Run structured evaluations. Test for actual performance. Use multi-layered monitoring. Then deploy.
For executives, the path forward is practical. Avoid all-or-nothing approaches. Invest in frameworks that encourage controlled testing and fail-fast models. Push for orchestration that brings multiple systems together with reliable monitoring. And above all, keep your internal culture focused on learning and iteration. Confidence in the system comes from exposure, not optimism.
Gen AI will deliver results. But those results will belong to the companies that master the system architecture, not those that rushed to say they adopted it first.
Concluding thoughts
Generative AI isn’t fading. It’s maturing, through friction, failure, and refinement. Most of the noise has died down, which clears space for more serious work. The technology still holds enormous potential, but value now depends on execution, not expectation.
If you’re leading teams through this transition, shift your focus to structure. Stop looking for ease-of-use wins and start building reliable systems. Prioritize model evaluation, enforce reliability standards, and integrate multiple AI methods to control for risk. Move away from standalone tools and lean into orchestration that scales across functions.
Don’t throw out the tech just because it didn’t deliver overnight. The long-term value will go to the organizations that moved past demos and into disciplined deployment. Keep experimenting, but don’t subsidize poor architecture. Demand clarity, transparency, and measurable returns.
