Three Authors from McKinsey have written good article:
about challenges for wide adoption of AI in companies. Based on McKinsey’s survey they estimated that only 8 % of companies engage in core practices that support widespread adoption. They see companies struggle to move from the pilots to companywide programs – and from a focus on discrete business problems, such as improved customer segmentation, to big business challenges, like optimizing the entire customer journey.
In order to move companies into proper directions, three shifts are needed according to authors:
- From siloed work to interdisciplinary collaboration.
- From experience-based, leader-driven decision making to data-driven decision making at the front line.
- From rigid and risk-averse to agile, experimental, and adaptable.
Those shifts can’t be achieved quickly and easy. Leaders need to work on preparing organization for them. Some steps are needed:
- Setting up for success
- Explaining why – and define proper understanding of employees role in new environment.
- Anticipating unique barriers to change – based on company structure and culture.
- Budgeting as much for integration and adoption as for technology (if not more).
- Balancing feasibility, time investment and value.
- Organizing for scale – setting organizational structure with proper central AI and analytics hub and strong executional business units. This hybrid hub-and-spoke model and clear responsibilities split can be very efficient.
- Educate everyone – AI is bringing demand for new type of knowledge and business practices, it requires upgrade on all levels from leaders to end users and new functions like analytics and translators should bring them both closer together.
- Reinforcing the change – leaders, business units responsible for implementation and whole organizations need to be align with AI initiative inside companies and work efficiently and responsibly to move organizations into AI run business models.