How to Avoid Regret in Your Next AI Investment

The Risk of Regret Is Real

You committed budget. Rolled it out. Trained the team. But nothing really changed.

The AI agent you bought sounded promising — it demoed well, seemed intuitive, and promised autonomy. But in practice? It rerouted requests, fetched data, and surfaced suggestions that never turned into decisions or action. It didn’t move the needle. And now, you’re left explaining sunk costs to stakeholders wondering what happened.

This is AI investment regret, and it's on the rise.

“With the success of foundational models and growing interest in autonomous systems, vendors are racing to label their products as ‘AI agents’ — often without delivering anything remotely autonomous or agentic.” — Gartner®

You can slap “AI-powered” on it, but buyer’s remorse is still buyer’s remorse. Here’s our advice on how to avoid it, with takeaways from a new report from Gartner®.

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What Triggers AI Investment Regret?

Regret rarely comes from one bad decision. It builds when teams:

• Trust vendor claims without scrutiny
• Choose automation over true autonomy
• Overlook architecture and integration details
• Fail to tie investments to real business outcomes

Without asking vendors the right questions, it’s easy to mistake a flashy demo and shiny promises for long-term value.

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Regret Checklist: Are You Headed Toward AI Disappointment?

“AI agents can differ significantly in sophistication, reliability and operational focus.” — Gartner®

You're at risk if:

• The agent can’t explain its decisions or reasoning
• You don’t know how the architecture works
• The agent only works inside one vendor’s ecosystem, thus adding to tool overload
• You can’t connect agent actions to real outcomes
• Governance and escalation paths are unclear
• There’s no proof of success with customers like you
• Pricing feels unpredictable, opaque, or usage-bound

Identifying Red Flags in AI Agents

The market is full of AI-washed solutions. According to Gartner®: “Most vendors are not offering what Gartner would consider truly autonomous AI agents.”

So how can you tell, when the marketing claims sound identical?

Red flags include:

• Agents that only retrieve information
• Workflows that require constant human handoff
• Decision logic built entirely on static prompts
• No visibility into model governance

Ask vendors how their agents decide, act, and learn — not just how they automate.

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Questions to Ask Before You Sign

Before you commit to any AI platform, dig deeper. Don’t just ask what it does — ask how and why.

How autonomous is it, really?

What kinds of decisions can the agent make without intervention? Can it act at an expert level based on context? Does it do more than one job?

What happens when the agent fails?

Is there a supervision model? Escalation path? Human-in-the-loop fallback?

What powers the decision-making?

Is it prompt chaining? A knowledge graph? Fine-tuned models? Can logic be audited? This will affect the quality of the AI Agent’s outputs.

How is usage metered and priced?

Understand whether pricing is based on actions, tokens, volume, or users — and whether that scales sustainably.

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How to Tell if an Agent Will Deliver

The most important question isn’t “What can it do?” — it’s “What outcomes can it drive?”

 Push for real-world evidence:

• Has this agent delivered value for companies like mine?
• Can I see testimonials or case studies?
• How fast is time to value in sales-specific use cases?

 If vendors won’t entertain this discussion, they might not be ready for production at scale.

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Align to Sales Outcomes That Matter

Avoid vague transformation promises. Focus on whether the agent can:

• Accelerate deal qualification
• Surface account intelligence before meetings
• Recommend next-best actions
• Flag risk or escalation needs
• Improve rep productivity and cycle speed

There’s a lot of nuance in successful B2B selling, so pure automation won’t move the needle as much as genuine intelligence and expertise.

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From Checklist to Action Plan

To avoid regret:

1. Challenge assumptions about what an AI agent is.
2. Interrogate architecture, pricing, and decision logic
3. Align to your highest-value, lowest-risk use cases
4. Demand real-world validation and transparent outcomes

 “It is critical to move beyond surface-level capabilities and probe deeply into architecture, decision-making models, alignment safeguards, governance mechanisms and pricing models.” — Gartner®

Buy Autonomy and Expertise, Not Just Automation

The AI agent market is crowded, fast-moving, and full of noise. But with the right evaluation framework, you can avoid costly mistakes.

Sales leaders don’t need to master every technical nuance — they just need to ask smarter questions. We believe the [PC8] Gartner® report lays them out, clearly and credibly.

Download the Gartner® report.

Source: Gartner, Selecting an AI Agent Solution: Questions to Challenge Vendor Claims, Jim Hare, Gene Alvarez, Tom Coshow, & Deepak Seth, 12 June 2025.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

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