Many AI projects are likely to be canceled due to overhype, costs, and risks
The future of AI depends on innovation as well as execution. Right now, a lot of AI projects, especially agentic AI, are hitting roadblocks. Gartner projects that more than 40% of agentic AI initiatives will be abandoned by 2027.
Most of these failures come down to vague goals, inflated expectations, and weak risk management. Companies rush to invest without thinking about implementation strategy or asking: What’s the actual value here?
What we’re seeing is decision-making driven by FOMO instead of grounded ROI. Deployments are often set up without any integration into workflows or clear stakeholding. That’s a mistake. AI is a tool, it doesn’t do the thinking for you. It automates what you already understand. If you don’t know the problem you’re solving, your product won’t solve anything.
For CEOs, CTOs, and boards, the real task is cutting through the hype. Be specific. What functions in your business can be optimized? What workflows are bottlenecked? What outcomes actually move the needle? Focus on building those capabilities with the right guardrails in place to manage cost overruns and mitigate operational risk.
Current agentic AI technology struggles to consistently execute real-world tasks
Agentic AI, AI with decision-making abilities and some autonomy, sounds great. Until you put it into practice. Research from Carnegie Mellon University and Salesforce found that today’s top AI agents fail more than 70% of the time when tested on basic business tasks. These are simple, everyday jobs like handling file formats, closing popups, or routing information correctly.
Even the most advanced models, GPT-4o, Claude 3.5 Sonnet, Gemini 2.0 Flash, only completed about a quarter of their assigned tasks reliably. That’s not enough to support uptime-critical systems in the enterprise.
This isn’t a reason to pull back. It’s a reality check. These tools are early-stage. They need guided deployment, not unrealistic expectations. If you’re pushing AI as an autonomous operator inside real production environments, you’re going to be disappointed, for now.
C-suite leaders should see agentic AI the same way they view any emerging tech: clearly assess what’s working and what’s not. Put it in place for low-risk tasks. Experiment internally first. Understand its boundaries, then scale. With the right approach, early AI tools can provide value today while positioning your company to ride the wave as the tech evolves.
Hype-driven adoption without proper workflow integration
There’s a pattern in agentic AI adoption: companies dive in too fast, set up sandbox experiments, and then walk away when nothing scales. These failures aren’t about the technology, they’re about disconnection from real business workflows. You don’t drive enterprise impact with isolated proof-of-concepts.
Neeraj Abhyankar, VP of Data & AI at R Systems, pointed out the core issue. Many companies adopt agentic AI with no alignment to actual operations. The work gets done in a lab-like setting under ideal conditions. It doesn’t reflect what teams actually deal with daily. When the pilot ends, the tech can’t handle the edge cases, interruptions, or system complexity that real business demands, and everyone moves on.
For C-level leaders, the takeaway is simple: architecture matters. Don’t build these deployments in silos. Design for integration from day one. That means mapping AI capabilities directly into daily processes, aligning your AI goals with business KPIs, and embedding the tools in a way that complements human execution.
Successful deployment doesn’t require perfection. It demands relevance. Tools must operate in the real world with its chaos, gaps, and constraints. Start where the friction is highest. That’s where agentic AI has something to offer.
Organizations can enhance ROI by aligning applications
If you’re only using AI as support for individual users, you’re leaving value on the table. The ROI from agentic AI comes when it’s tied to broader enterprise goals, when it automates repetitive tasks and drives high-leverage decisions.
Anushree Verma, Senior Director Analyst at Gartner, makes this very clear: augmenting users is good, but insufficient. If you want AI to create measurable business value, it has to be embedded into the systems that already run your organization, enterprise resource planning, customer service pipelines, supply chain operations.
This alignment must be purposeful. It means defining the business objective first, then building the AI intervention around it. Processes that deal with repetitive, high-volume work are ideal infrastructure for AI integration, not just because of throughput, but because they pave the way for strategic reinvestment.
C-suite leaders shouldn’t confuse novelty for impact. The goal is sustainable value at scale. Set objectives. Quantify output. Reinvest time and insight where your business wins long-term. AI can do the work, but only if it’s aligned properly.
The absence of internal leadership and clear project scopes
A lot of AI projects hit walls not because of the tech, but because no one’s actually leading. There’s energy, but no direction. Teams stretch their capacity across inconsistent priorities, and when tasks begin to collide or drift, timelines slip. Eventually, the project loses momentum, and gets shelved.
John Callery-Coyne, Chief Product and Technology Officer at ReflexAI, raised a good point here: too many agentic AI efforts lack clear ownership and defined objectives. When you don’t know who’s driving the initiative or what success even looks like, you shouldn’t expect results. The tech can’t fix that kind of leadership gap.
Leadership starts by setting boundaries. Set a defined scope. Assign champions who are accountable throughout the lifecycle of the AI deployment. Clarify what a win looks like. And be honest about internal capacity, under pressure, overloaded teams won’t deliver innovation, they’ll deliver compromises. That’s not what you want with frontier technology.
C-suite executives need to understand that agentic AI isn’t a hands-off project. It needs visible leadership, frequent check-ins, and close alignment with strategic intent. Turn these projects into long-term assets, not short-term distractions. Be present and intentional, or don’t move forward at all.
Market confusion due to “agent washing”
Most of what’s labeled as agentic AI today isn’t actually agentic. There’s a lot of noise. Vendors are repackaging basic automation tools or chatbots and calling them AI agents. That behavior, what Gartner calls “agent washing”—makes it harder for executives to know what they’re actually buying.
Gartner estimates there are only about 130 vendors offering true agentic functionality out of thousands in the AI space. And even among those, the level of maturity is uneven. This creates a trust issue in the ecosystem. You’re seeing inflated promises about autonomy, problem-solving, and decision-making, core features agentic AI should deliver, but many tools can’t yet deliver reliably.
For executives, vetting AI products now requires more in-depth evaluation. Demand technical transparency. Ask where autonomy lives in the workflow, how decisions are made, and how the system recovers from errors. Don’t accept vague marketing language as evidence of capability.
This is a filtering moment. Strong vendors will stand up to detailed scrutiny. Weak ones will default to buzzwords and demo tricks. The companies that take the time to vet based on performance and integration will extract more value and less risk from their adoption. Act accordingly.
Agentic AI heralds a long-term transformation in enterprise automation
Yes, the current tools have limits. But agentic AI is a structural shift. It goes beyond generative AI by introducing systems that act, decide, and adapt. When built and integrated correctly, these agents move workflows forward without waiting for constant inputs or commands.
Gartner is clear on where things are going. By 2028, 15% of daily work decisions will be carried out autonomously through agentic AI. That’s up from virtually zero today. In the same timeframe, one-third of enterprise software will embed these capabilities. The direction of travel is not speculative, it’s already happening, just not evenly distributed yet.
This is where executive foresight matters. Early-stage friction doesn’t cancel long-term momentum. Companies that start adopting agentic AI thoughtfully, in high-impact areas with clear oversight, will be positioned to scale as the tools mature. The learning curve is real, but it’s also strategic advantage.
You want to evolve with the technology, not chase it after disruption becomes visible. Identify where autonomy can complement human expertise. Use what works now, but prepare teams and systems to adopt next-generation versions as interface complexity improves. Long-term transformation begins with structured experimentation, not passive observation.
Achieving success with agentic AI
You can’t bolt agentic AI onto processes that were built for static software. Legacy systems constrain the potential of AI agents, not just technically, but operationally. If your workflows are rigid, fragmented, or built around manual decision points, AI applications will either fail fast or get watered down to basic support tools.
Gartner recommends a rethink, from the ground up if necessary. Integrating agentic AI means reexamining how decisions are made, how information flows, and where automation can unlock measurable outcomes. Often, that means redesigning entire segments of a workflow, so the agent doesn’t just observe the activity but drives part of it.
Don’t confuse integration with relevance. Just because AI is connected to a dashboard doesn’t mean it’s generating value. Value comes from changing outcomes, faster execution, lower operational drag, smarter and more consistent decisions.
For C-suite leaders, this means having the discipline to modernize infrastructure before scaling AI deployments. Focus investment where the returns are repeatable: cost reduction, improved quality, and process speed. Avoid cosmetic adoption. Build from fundamentals. This is how you get durable returns from agentic AI, not just experimental wins.
In conclusion
Agentic AI has momentum, but direction matters more. Right now, too many projects are driven by buzzwords, incomplete strategies, and unrealistic expectations. That’s not innovation, it’s waste. For executive leaders, the path forward is measured, not reactive.
The opportunity is real. But to capture it, you need structure. Clear business goals, decision ownership, disciplined workflows, and technology that earns its place, not demands it. Agentic AI can drive automation, scale decisions, and free up human capacity. But that only happens when it’s embedded with purpose.
Skip the hype. Filter the noise. Build with intention. The companies that do this now won’t just experiment with agentic AI, they’ll operationalize it at scale. And that’s where the real edge lives.
