Do you have the processes you need to implement agentic AI for data?
Agentic AI is continuing to dominate news headlines about the artificial intelligence industry, and this week financial news outlet Barron’s posted an editorial titled, “AI Agents Are Coming to Take Away Your Busy Work,” calling out the future of work over the next two years in particular. When it comes to data analytics, there is an industry wide problem every company faces, in which data professionals are busy with analyses, but not necessarily effective with meaningful insights–making many investments in data strategies inefficient even with the best teams on board.
In our recent blog post about how AI agents can power your data strategy, we shared how Seek AI is applying the next generation of AI to increase data effectiveness and speed to insights for the data analytics industry. However, to bridge the gap between adopting and implementing agentic AI for data, it will be more important than ever to have a strategic change management plan for your business.
What is change management?
If you’re not familiar, change management is defined as a structured approach to transitioning individuals, teams, and organizations from a current state to a desired future state. It encompasses the processes, tools, and techniques used to manage the people side of change to successfully achieve business outcomes.
The purpose of implementing change management is to help organizations adapt to new goals, processes, and technologies–allowing for improved efficiency and effectiveness–while minimizing disruption to business. Change management can be broken into three key phases of implementation.
Three Phases of Change Management
Planning - The planning phase involves assessing the need for change, defining clear objectives, and developing a comprehensive strategy for implementation.
Implementation - The implementation phase includes executing the change plan. From coordinating resources and communicating with stakeholders, to ensuring that all team members understand their roles in the transition.
Sustaining Change - After implementation, the final phase of sustaining change is crucial to solidify the new practices into the organizational culture, ensuring that changes are maintained over time.
Types of Change Management
Change management can be applied across various types of organizational changes.
Developmental Change - Enhancements to existing processes or systems.
Transitional Change - Moves from one state to another, such as mergers or new technology adoption.
Transformational Change - Fundamental shifts in organizational culture or operations.
Why is change management critical for AI adoption?
Effective change management is vital to AI adoption for several reasons. At a high level, it reduces resistance by involving employees in the change process and addressing their concerns, so organizations can foster a supportive environment. Next, it enhances communication, offering clear reasons for change and its benefits to help build trust among employees. Finally it increases success rates. Structured change management increases the likelihood of achieving desired outcomes by providing a roadmap for implementation.
To unpack this a bit more, the introduction of AI tools often brings uncertainty and potential resistance from employees. Naturally, no data analyst wants to feel like they could be replaced by agentic AI, making it critical to educate data teams about the need for upskilling to the next generation of data analytics. Effective change management helps address concerns about job security and role changes, communicate the benefits and purpose of AI adoption clearly, and provide support and training to build confidence in using new AI systems.
Change management facilitates the seamless integration of AI tools by analyzing organizational readiness and potential obstacles, developing tailored strategies for different departments and roles, and monitoring adoption rates and addressing issues proactively. Proper change management enhances the return on investment in AI technologies by streamlining processes and workflows to leverage AI capabilities, identifying opportunities for AI to augment human work rather than replace it, and measuring and optimizing the impact of AI adoption on productivity and outcomes.
Creating an organizational culture that embraces AI and innovation is another important part of change management. Encouraging experimentation and learning with new AI tools, developing change-ready mindsets among employees, and aligning AI adoption with broader organizational goals and values. Effective change management helps organizations navigate potential pitfalls addressing ethical concerns and ensuring responsible AI use, managing data privacy and security risks associated with AI implementation, and balancing the pace of change with employee adaptation capabilities.
By employing robust change management strategies, organizations can ensure that the adoption of AI tools like Seek AI is not just a technological upgrade to agentic AI for data, but a transformative process that enhances overall organizational effectiveness and employee engagement.
Learn more about how to work with Seek AI or set up a demonstration of our agentic AI technology here.
