Why Over 40% of AI Agent Projects Will Fail and How to Beat the Odds

Agentic AI, or systems that can make autonomous decisions and execute complex tasks, is one of the most talked-about emerging technologies today.

Yet the reality is sobering: over 40% of agentic AI projects are predicted to fail by 2027 due to rising costs, unclear business value, and insufficient risk controls, according to Gartner.

A January 2025 Gartner poll of 3,412 webinar attendees highlights the mixed adoption landscape:

• 19% of organizations have made significant investments in agentic AI
• 42% have made conservative investments
• 8% have made no investments
• 31% are taking a wait-and-see approach or are unsure

“Agent washing” is muddying the market, with vendors relabeling chatbots and RPA tools as agentic AI, even when they lack real autonomy or decision-making power.

In reality, only about 130 vendors offer genuine agentic AI solutions.

Companies such as Kanda Software, a full-service software development and AI consulting firm, are helping organizations explore how to apply these agents effectively to real-world workflows.

They provide guidance on use cases, pilot implementations, and how to measure tangible outcomes.

Editor's Note: This is a sponsored article created in partnership with Kanda Software.

How to Get Real Value from Agentic AI

Agentic AI can transform operations, automate complex decisions, and unlock efficiencies that traditional automation cannot achieve.

By 2028, key predictions include:

• 15% of daily work decisions will be made autonomously
• 33% of enterprise software applications will include agentic AI

Realizing this potential requires careful planning. Organizations should focus on initiatives where agents can deliver measurable improvements in efficiency, cost reduction, or risk management.

Workflows should be redesigned from the ground up to fully leverage AI capabilities.

When implemented strategically, AI agents can:

• Handle complex decision-making
• Automate repetitive tasks
• Support information retrieval

"The key to successful agentic AI adoption is starting with clear, measurable objectives and redesigning processes from the ground up rather than retrofitting," said Goran Skorput,
CTO of AI, Big data, and Analytics at Kanda Software.

This structured, knowledge-driven approach enables organizations to enhance productivity, speed, scale, and maintain strong compliance and security standards.

5 Ways Organizations Can Beat the Failure Rate

Implementing agentic AI takes more than just adopting new technology.

Success comes from strategies that prioritize business value, minimize risk, and drive measurable impact.

1. Evaluate Use Cases Carefully

Prioritize initiatives where AI agents can provide measurable improvements in efficiency, decision quality, or risk management.

Choosing the right use cases ensures projects deliver tangible business value.

2. Pilot Before Scaling

Run small, controlled pilots to validate assumptions, identify hidden challenges, and measure ROI before committing to a full rollout.

"Small-scale pilots allow organizations to test assumptions safely, uncover hidden challenges, and measure ROI before large-scale deployment," said Skorput.

This approach reduces risk, surfaces operational gaps early, and provides data to inform smarter scaling decisions.

3. Leverage Expert Insights

Drawing on knowledge from experienced teams can help organizations assess workflows, select suitable AI agents, and implement pilots in ways that mitigate risk and build organizational knowledge.

4. Rethink Workflows

Redesign processes with agentic AI in mind rather than retrofitting existing systems.

This approach allows agents to operate autonomously, reduces friction, and ensures human and machine work together efficiently.

5. Focus on Enterprise Productivity

Align AI deployments with strategic objectives to improve overall business outcomes, not just individual task automation.

This ensures measurable gains in cost, speed, quality, and scale.

Maximizing the Potential of Agentic AI

Agentic AI is set to play an increasingly central role in enterprise operations, but success requires careful planning and strategic application.Organizations that focus on the right use cases and redesign workflows while piloting solutions before scaling can unlock significant value.

Careful implementation and continuous measurement of outcomes ensure deployments drive meaningful results.

When aligned with business goals, agentic AI can deliver productivity gains, improved efficiency, and scalable growth while avoiding common pitfalls.