The biggest barrier to success is leadership, said findings of the research done by McKinsey & Company, adding at least 48% of employees rank training as the important factor for Gen AI adoption; yet nearly half feel they are receiving moderate or less support.   

Companies are increasingly recalibrating their productivity by integrating artificial intelligence (AI) into their workflows, with 92% of companies plan to increase their AI investments over the next three years, according to research published by McKinsey & Company. 

While the findings of the research titled ‘Superagency in the workplace: Empowering people to unlock AI’s full potential’, indicate a widespread adoption of AI technology across sectors, it also pointed out that only 1% of leaders call their companies “mature” on the deployment spectrum, meaning that AI is fully integrated into workflows and drives substantial business outcomes.

The McKinsey research estimated the long-term AI opportunity at US$4.4 Tn in added productivity growth potential from corporate use. 

The report explored companies’ technology and business readiness for AI adoption. It concluded that employees are ready for AI as they are currently using Gen AI three times more than the leaders expect.

Yet, C-suite is 2.4 times more likely to cite employees’ readiness as a barrier to adoption versus their own issues with leadership alignment. 

What's wrong with leadership?

The biggest barrier to success is leadership, the report said, adding “at least 48% of employees rank training as the important factor for Gen AI adoption; yet nearly half feel they are receiving moderate or less support”.   

The question arises how business leaders can deploy more capital and steer their companies closer to AI maturity, asked the report. The answer is they need to automate repetitive tasks, enhance decision-making with data insights, improve collaboration, and streamline supply chain and operations, among other things. Here’s how they can achieve it:

Automating repetitive and mundane works to get time for high value-tasks: Works that don’t require creativity and are often repetitive in nature, such as data entry, customer support, e-mails follow up, scheduling and rescheduling appointments or interviews, can be automated using AI. Such mundane tasks often clutter employees’ mind and overload their schedules, diverting their attention from more important, creative, and strategic work.

Here, AI tools should be used to keep track on everyone, sending reminders and updating task statuses automatically, so that nothing is left. 

Improving team collaboration: Any successful project is the result of efficient collaboration and teamwork, however setting a common goal, maintaining a clear dialogue and aligning everything smoothly across teams can be difficult and challenging, especially in the time when remote or hybrid working is becoming a norm. 

Here, AI can play a significant role by enhancing collaboration through real-time updates, streamlining communication and dialogues, and ensuring that everyone has access to the most updated details and status of the project. So that every team member remains at the same page, and no one feel left out.   

Streamlining operations and supply chain: AI tools can optimise inventory management, predicting demand, and supply. Therefore, it can help in streamlining the distribution, reducing costs, and improving efficiency. AI can automate key project management tasks, such as allocation of resources, task delegation, and reporting. 

When businesses grow, it become difficult to manage a rising number of projects as well as team members. Here, AI tools can offer scalable solutions to handle increased workloads without affecting the quality and supply chain. 

Better decision-making through data driven insights: In today’s complex work scenario, it is not easy to make correct decisions without delay as information are generally scattered across verticals and teams. Here, AI tools can offer data-driven insights, empowering managers to take fast and accurate decisions through data analytics and predictive modelling. They can help businesses identify potential risks, forecast outcomes, and optimise workflows accordingly. 

Optimising workflow: Companies can use AI to prioritise works based on deadlines, workload, and urgency, maintaining visibility over every task done and ensuring that employees focus on the most critical part of the work. 

Ensuring accountability can be difficult in large or complex teams, here AI can help managers keep a check on the project progress, ensuring everyone fulfils his responsibility. This is how AI can help managers make proactive adjustments before productivity is impacted.