Enterprise AI vaporware now includes products that launch but fail to deliver practical value at scale.
Evaluate AI vendors on production readiness, governance and measurable ROI -- not polished demos.
Reduce costly AI failures by validating use cases, architecture and deployment plans before investing.
Salesforce announced its Agentforce Platform at Dreamforce 2024 as an AI-powered platform that uses bots to automatically handle business tasks and customer interactions. The original Salesforce expectations for the platform were high, with the stated goal of deploying one billion AI agents by the end of 2025.
Reality, however, turned out to be different. By early 2026, only 6% of Salesforce customers were on paid Agentforce plans, according to Salesforce Ben. The company's shares fell 43% in the first half of 2026 while the S&P 500 gained roughly 7% over the same period, according to a June report from CNBC.
That gap between promise and deployment reality is the current face of enterprise vaporware. It is a situation where AI platforms are technically available but fail to deliver practical value in production. Vaporware originally described software announced and never shipped. The more common problem in 2026 is products that ship but cannot be deployed at scale, change too rapidly for stable implementation or carry pricing that makes production use economically unsustainable.
What is vaporware?
The original basic definition of vaporware is a product or service that doesn't exist; it's just vapor -- or not real. There is now nuance to the term from an enterprise IT perspective, with multiple types of vaporware, including:
Never-released products. Companies announced and marketed these products but never shipped them. Samsung's Ballie home robot, first shown at CES in 2020, was set for a summer 2025 launch in the U.S. and Korean markets, but missed that window and remains unreleased.
Perpetually delayed products. These products have repeated launch commitments, and each of those commitments is pushed back with shifting explanations. Ballie fits this category too, having appeared at CES in 2020, 2024 and 2025 with successive promises and no delivery.
Underdelivered products. These products are shipped but fail on core promises. The Humane AI Pin shut down by February 2025, within a year of its launch, due to product overheating, low sales and lack of interest.
Strategic vaporware. Announced primarily to shape market behavior or discourage competitors, but there is no firm release commitment behind the announcement. Google's Project Astra fits this pattern. Unveiled at Google I/O on May 14, 2024, it was presented as a universal AI assistant capable of real time multimodal interaction across devices. As of June 2026, Google's own page describes Project Astra as "a research prototype, being used and refined by a limited number of trusted testers." Fast Company named it one of the AI products that failed to materialize in 2024.
Enterprise vaporware. Products are purchasable, but adoption stalls because deployment complexity exceeds organizational readiness, production costs are unclear or value propositions do not map to actual workflows. Agentforce fits this category.
Common warning signs of vaporware include:
Vague delivery timelines with no firm commitments.
Demo-only access with no sandbox or beta program available.
Repeated delays paired with changing explanations.
Missing technical specifications or pricing.
Marketing activity that exceeds evidence of customer adoption.
Partner confusion about implementation guidance.
Enterprise vaporware in practice
There are numerous examples of enterprise vaporware in practice that organizations can learn from.
Major enterprise software case: Salesforce Agentforce.
At Dreamforce in September 2024, Salesforce positioned Agentforce as a platform that would autonomously transform customer service, sales and business processes at scale, backing the ambition with major marketing investment aimed at enterprises under pressure to show AI progress.
The market reality diverged quickly. Salesforce Ben reported in July 2025 that customers and partners had begun describing the platform as "expensive, unnecessarily confusing, or just not something many companies feel ready to align with." Forrester analysts at Dreamforce 2025 found sparse adoption in direct customer conversations and concluded the platform still had "a long way to go for a meaningful ROI."
A Bloomberg investigation in May 2026 brought the disconnect into focus. Flagship customer demonstrations were "largely aspirational."Williams-Sonoma's phone line remained disconnected from Agentforce six months after being shown live on a Salesforce conference stage. Finnair's website labeled related features as "future planning only," and patients calling the University of Chicago Medicine were still routed to human schedulers, despite their appearance in Salesforce promotional videos.
The picture is more nuanced for organizations with direct experience with Agentforce deployment.
"Our experience with Salesforce Agentforce has been a positive one," said Chris Bennett, vice president of global AI practice at Unisys, an IT services provider. But Bennett said enterprises are rushing into AI implementations driven by fear of missing out, and in that rush are forgetting basic enterprise architecture, security and governance principles.
"Just because you can do something a certain way doesn't mean you should,' Bennett said. "The two biggest blockers we see across almost every enterprise are process and data."
Other high-profile cases
The same patterns appear across AI hardware and robotics.
Humane AI Pin. Raised $230 million from investors, including OpenAI CEO Sam Altman, launched at $700 in April 2024 and shut down by February 2025 after HP acquired its assets for $116 million. Significant investor backing and pre-launch marketing proved no substitute for a production-ready product.
Samsung Ballie. First shown at CES in 2020 and still unreleased as of January 2026, despite CES appearances in 2020, 2024 and 2025, each promising imminent availability. Compelling demonstrations across five years have not produced a commercial product.
Agentic AI overpromises (industry-wide). The Agentforce pattern is not unique. According to Yugal Joshi, partner at research firm Everest Group, the industry appears to be in a perpetual prototyping mode.
"There have been instances of poor performance from OpenAI in recent times, Anthropic models running token limits," he said. "In addition, a variety of enterprise platform vendors, such as SAP, ServiceNow, Salesforce and Workday, have brought their AI agents to mixed reviews."
Physical AI and robotics. CES 2026 produced significant demonstrations of autonomous robots and physical AI systems. Production deployments remain rare, and enterprises evaluating the category face unresolved questions on maintenance costs, integration, liability and ROI.
Why this matters to IT executives
Most AI product failures share a common origin.
"The fundamental disconnect is that organizations are investing in AI tools without having a clear understanding of the relevant use cases or problems they're trying to solve," said Derrick Pledger, CIO at Maximus, a government technology provider.
AI platforms and automation tools that stall in production carry real financial and operational consequences.
Financial risks
IT budget waste on stalled implementations, opportunity costs from delayed investment in proven resolutions and hidden costs from competitive disadvantage accumulate quickly.
"When an organization commits budget, time, and executive attention to an AI initiative that does not scale, it slows other modernization priorities and can create internal skepticism around future AI investments," said Greg Sarich, CIO at Quest Software.
A separate trap emerges when organizations cut head count in anticipation of AI productivity gains that have not materialized, leaving them with higher costs and a loss of institutional knowledge that AI cannot replace, Pledger said.
Operational consequences
Failed implementations do not leave organizations where they started. Roadmaps built around products that do not deliver are disrupted, and implementation teams must manage rapidly changing products with outdated documentation.
"Teams may spend months trying to force a tool into workflows it was not ready for, while the real foundational issues, like data quality, integration, identity security, governance and platform readiness, remain unresolved," Sarich said.
The "available but unusable" problem
Enterprise vaporware is difficult to manage contractually because the product exists. Pilots obscure this.
"A pilot lets you avoid the hard problems. You're working with curated data, a narrow use case, and very controlled conditions. You don't have to think too much about governance, policy, data protection, monitoring, or explainability," Eric Helmer, chief technology officer at Rimini Street, said. "These issues don't appear in a demo, but they appear immediately in production."
Governance risks
When a product exists but does not deliver on its promises, holding vendors accountable is difficult. Security and compliance gaps arising from underdeveloped features expose the organization and erode stakeholder trust in IT leadership. Pledger said the hardest AI failures in 2026 are no longer about whether the demo is impressive, but whether the system is governable, secure, measurable, contractable, and economically sustainable in production.
What IT executives should do
Knowing how to avoid vaporware starts with treating AI vendor evaluation differently from standard software selection.
Demand production testing, not demos
Demos alone are not enough.
"You cannot just deploy something in your call center, your manufacturing line, or your patient care environment and hope for the best," Bennett said.
The clearest procurement red flag, he said, is a vendor that says, "trust us and just deploy it, we don't have time for a pilot," a stance he added is increasing as fear of missing out drives rushed decisions.
Helmer noted that the mistake he sees most often is that teams fall in love with the experience.
"The demo looks great, the outputs are impressive, and the roadmap sounds exciting," Helmer said. "But a good evaluation process forces you to step back and ask a much harder question: Will this work with our data, in our workflows, under our governance model, at our scale?"
At the end of the day, AI vendors are selling what's technically possible. But enterprises are dealing with what's operationally realistic.
Eric Helmer, chief technology officer, Rimini Street
Anchor evaluation to measurable use cases
Technology ROI must be defined before procurement, not discovered after deployment.
"Instead of bringing AI tools into a legacy or broken system, real value must start by reimagining processes and workflows first and then making deliberate choices on where integrating AI will deliver measurable ROI," Pledger said. "Until organizations identify their process bottlenecks or behavioral inefficiencies, the gap between the promise and reality will continue to grow. AI tools do not fix this baseline problem."
Evaluate vendor credibility and architecture
Understanding the proposed architecture is an essential step. In Pledger's view, a credible enterprise AI vendor evaluation process looks much more like a mix of third-party risk management, model risk management and product due diligence than a traditional technology procurement exercise.
"If vendors can't provide clear documentation and architecture blueprints or pitch end-to-end automation for critical decisions without a human feedback loop for escalation or decision auditability, those should be major warning signs," Pledger said.
Build contracts for the available but unusable scenario
Standard software contracts assume the risk of non-delivery. Enterprise vaporware requires terms that explicitly define successful deployment, not just product availability.
Executive takeaway
The Salesforce Agentforce situation has highlighted what many CIOs describe as enterprise vaporware -- products that technically exist but fail to deliver practical value at scale.
In 2026, the problem is not just products that never ship. The harder challenge for IT leaders is products that ship but cannot be successfully deployed, prove too complex for organizational readiness or change too rapidly for stable implementation.
"At the end of the day, AI vendors are selling what's technically possible. But enterprises are dealing with what's operationally realistic," Helmer said. "And until those two things align, we're going to keep seeing this pattern -- the promise looks big in the early stages, but the path to realizing that value at scale is much harder than most organizations expect."
Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.
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