By Tiffany Poeppelman
LinkedIn

As AI unlocks new ways of working, it’s only natural that talent development must transform too. 

After leading two AI-focused sessions — and having many timely conversations — at this year’s Association for Talent Development conference (ATD24), I’m more convinced that every talent leader has an opportunity to help their organizations foster the agility and resilience needed for the future of work. 

To do this, I see four areas of transformation that will build strong, innovative, AI-ready organizations:

1. Career development — empowering impactful and continuous employee growth.
2. Skills agility — encouraging more dynamic and personalized learning to keep pace with change.
3. Internal mobility — developing more ways for people to move around their organizations, delivering skills when and where they’re needed.
4. Business impact — aligning with business strategy and measuring success according to clear business outcomes.

By Chhavi Arora
McKinsey

A cloud transformation has a much greater chance of success if companies know what to track—and how to track it.

The promise and peril of cloud is a common refrain in many C-suites: huge economic potential and regularly underperforming reality. What’s much less clear, however, is what to do about it.

Out of the more than 80 enterprises McKinsey profiled for its CloudSights database, 40 percent have found limited value in their cloud programs.1 In addition, half of companies five or more years into their cloud journey still have not achieved 20 percent cloud adoption. The underlying causes are often hard to pinpoint and articulate, or are simply caught too late to stop the damage. Even for companies that are well on their way to achieving value from cloud, it is often difficult to communicate progress to stakeholders and make a credible case, based on clear ROI data, for substantial new investments.

Given the significant resources of time, money, and people that companies invest in cloud transformations, it’s surprising how poor the metrics and objectives and key results (OKRs) used to track progress often are. For example, some companies will exclusively use point-in-time metrics (for example, number of applications migrated by a given date) to understand where their cloud program is. These metrics, however, frequently fail to accurately capture the full picture of progress over time. Other cloud programs will set their OKRs without involvement from the business side and, as a result, struggle to actually measure and articulate the enterprise value cloud is creating. In many cases, cloud teams will also lack clarity over who is accountable for which metrics and how they are being reported and used. The difficulty often boils down to lack of clarity about what is important to measure and lack of rigor in implementing a tracking program.