- The Need for an Analytics Center
- Best Practices for Creating
To leverage AI effectively, organizations must equip their management with an in-house analytics center. Hiring contractual talents and training the current workforce with online courses are not feasible long-term solutions. In this McKinsey article, Solly Brown and his team explore how today’s businesses can get ready for AI intervention.
The Need for an Analytics Center
New hires can fill the ever-expanding skills gap that organizations are suffering from. External educational providers can train the existing workforce with new tools, processes, and policies. However, these two factors fail to cater to the overall AI requirements of an organization. Having an analytics center can help in the following ways:
- A known setup, common goals, and shared perspectives allow the stakeholders to work in unison. It leads to faster MVPs, resource allocation, better retention rates, and job satisfaction.
- The training content is aligned with organizational strategies and industry requirements. The analytics center members know about the organization inside out. So, they can create workarounds for in-house challenges and identify a competitive edge.
- Employees can readily apply their newly acquired understanding of AI to day-to-day work. Since they use their learnings in real work, they do not take much time to gain expertise.
Best Practices to Create an Analytics Center:
- Train for the Roadmap: Align analytics center training with the AI transformation goals of the organization. Trained teams are already working on AI initiatives and expecting revenues by leveraging AI tools across several business units.
- Include All Roles: Irrespective of hierarchy, team, functions, and roles, everyone should be actively involved. Though some companies prefer training those that need immediate training, engaging all can lead to an all-round transformation.
- Do Not Be Too Technical: Though AI training is technical, you must include the technology’s impact on organizational and cultural aspects. Focus on how to drive value, follow best practices, build soft skills, embrace Agile, and react to changes.
- Make Classroom Training Real: Apply AI solutions to existing issues in the organizations in the training rooms. While the employees are on the floor, help them understand how to apply them in real work.
- Incentivize Engagement: Organizations must find ways to improve attendance for any analytics center training. For example, employees that qualify in the test rounds will be eligible for the training courses. Reward those that complete the courses or lead successful projects after training. Remove the fear of failure.
- Evolve Continually: Since new AI tools and technologies will come up every other day, encourage people to evolve with time. Create internal coaches and a leadership team. Ensure they have enough support to lead the organization in the AI journey.
To view the original article in full, visit the following link: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-analytics-academy-bridging-the-gap-between-human-and-artificial-intelligence