There’s no shortage of Agentic AI hype despite the summer break. Leaders must navigate the complexities of AI agents from technology to business impact while understanding their deployment, user adoption, and the cultural shifts required for effective integration. But how can leaders move actionable pilots into production, get their company’s data in order to shape their AI strategy, and build a company culture that embraces AI responsibly?
Wrapping up the first half of this year’s conference season, Jon Reed, Industry Analyst and Co-Founder of diginomica, and I recently discussed the current state of Agentic AI, and what leaders need to do right now.
Moving Beyond Proof of Concepts
Many organizations still cling to the idea of running proof of concepts for their AI initiatives. But POCs often lead to inadequate exploration and suboptimal results. Instead, leaders should prioritize live pilot projects that feel real and actionable. By engaging with real data, teams can understand the nuances of AI technology in the context of their specific operations.
Running a pilot allows teams to observe and adjust in real-time, showcasing what tools can achieve within a controlled environment. You might even experiment internally before rolling things out externally, observing how these agents perform in practice. This iterative approach not only provides insights into their capabilities but also fosters collaboration and accountability within your teams.
A successful pilot can build enthusiasm, creating a ripple effect where employees seek to expand and integrate AI into their workflows, rather than feeling daunted by it.
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Understanding the Foundation of Successful AI Deployment
Funny enough, AI initiatives aren’t about the technology itself. Many discussions overlook how impactful quality data is to success, no matter the flavor of AI. Companies need to focus on creating robust data sets that allow agentic systems to flourish.
» Are there areas where we have high quality data and a real obvious use case or need where we can start getting started? «
— Jon Reed
By ensuring that AI models are trained on high-quality data, organizations can increase the potential for accurate results. Employees working closely with AI tools should also feel empowered with this data, understanding how their specific information plays a part in making AI genuinely effective. As a leader, facilitate access to this data while establishing clear goals around how it will be used. The synergy between AI tools and high-quality data will enable a deeper understanding of business operations and help manifest tangible results.
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Cultivating a Culture of Experimentation and Engagement
Perhaps the most pressing issue facing organizations is fostering a workplace culture where AI use is embraced rather than feared. Employees often worry about job security when they hear about AI initiatives. Leaders need to set clear expectations that AI is a partner in their work, not a replacement. This mindset shift can help mitigate feelings of apprehension toward AI tools.
Create opportunities for employees to explore these new tools in a safe and controlled environment, allowing them to experiment without the weight of criticism. Encourage individuals to share successes and learning experiences. By integrating rewards for innovative use cases and creating forums for discussion, you’ll help bolster confidence and reduce the ‘shadow AI’ phenomenon, where employees use unauthorized AI tools out of fear of judgment or repercussion.
As a result, this kind of openness can lead to greater employee satisfaction and a more engaged workforce, driving not just productivity but a greater connection to the company's goals.
Summary
Leaders need to transition from traditional POCs to actionable pilots, emphasize the central role of quality data, and cultivate a culture that encourages experimentation and engagement. Integrate these strategies into your organization. Identify where you can transition from theory to practice and encourage a supportive environment for the adoption of AI tools.
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