A recent MIT report found that nearly 95% of corporate AI pilot programs fail to deliver meaningful results. While executives often cite regulatory hurdles or model limitations, the research points to a more fundamental challenge: flawed enterprise integration.
In my role, I’m seeing similar trends with organizations who are moving to adopt agentic AI. The transformative potential of agentic AI is immense, but realizing its full potential takes more than experimentation.
Over the past 24 months, I’ve been working closely with companies to explore and adopt agentic AI. Along the way, I’ve seen firsthand what works, where there’s a learning curve, and how to move beyond the hype to deliver results.
As companies move from generative AI to agentic AI, shifting from tools that create content to systems that can reason and act, the pressure to get it right the first time is even greater.
Today, I’m sharing three lessons I’ve learned from these implementations and provide practical takeaways leaders can use to get ahead of agentic AI and create lasting value.
Design AI Agents for Decisioning
Decisioning is a new priority business leaders face as AI agents move from experimental demos to real-world deployments.
While generative AI (GenAI) or LLMs excel at creating content, language or recommendations, agentic AI is designed to go one step further by making decisions that can impact people, industries and society. This new capability raises the stakes. Decisions must be effective, ethical, and explainable.
To ensure agentic AI makes the right decisions, leaders must prioritize decision intelligence. By designing, modeling, and optimizing decision-making processes, organizations can enable AI agents to act autonomously and efficiently while ensuring consistency, traceability, explainability, and compliance. A robust decisioning layer integrates analytics and rules into agent workflows so AI agents can operate reliably in regulated environments, whether approving loans, flagging fraud, or recommending next-best actions.
With agentic AI, doing things right matters more than doing them fast. A well-designed decisioning layer ensures leaders can trust both the process and the outcomes.
Build a Strong Data and Talent Infrastructure
Organizations are eager to create new efficiencies through agentic AI. But unlike GenAI, agentic AI will take longer to implement because it requires an enterprise-wide redesign, starting with a strong data and talent infrastructure.
Without clean, centralized data, robust governance and the right skills in place, even the most well-designed AI agents will struggle to scale and deliver strong results.
Data centralization is the No. 1 challenge for AI adoption, up from seventh just a year ago. AgenticAI depends on accurate, consistent, high-quality data to make autonomous decisions. Even minor errors or fragmented datasets can create biases, inaccuracies or compliance risks. Centralized data enables faster analytics, better collaboration across teams and smoother integration with AI workflows.
Investing in data quality, governance and talent is the bedrock for decisions you can rely on and the foundation for future innovation.
Establishing Trust Through Responsible Governance
As agentic AI gains autonomy, organizations face a pressing question: Who governs its actions? Concerns about data privacy, explainability, and ethical use are top of mind for companies implementing agentic AI. In industries spanning financial services to health care, this technology brings both huge opportunities and serious risks.
That’s why establishing a strong AI agent governance framework is essential. This framework must include human oversight, explainability and transparency, ethical and bias auditing, and regulatory and business safeguards. Humans should remain in the loop for oversight, with governance models defining when AI can act independently and when it requires human approval.
Responsible governance isn’t just ethical; it’s strategic. Organizations that embed it from the start can scale safely while earning the trust of employees, customers and regulators.
Getting Ahead of Agentic AI
The innovation I’ve seen with agentic AI is inspiring, and we’re only at the beginning.
While many organizations are still learning about this new technology, it’s been exciting to learn what has moved them from preparation to production.
Organizations that invest in the right foundations – decisioning, data and talent, and responsible AI – will be the ones able to unlock the full transformative potential of agentic AI and turn experimentation into enterprise-wide efficiencies and lasting results.
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