What do sports general managers, training managers and information technology (IT) teams have in common?

The need to track productivity and performance.

This couldn’t be more evident as global organizations look to new future of work talent models and measurement tools.

Athletic performance analytics read like an alphabet soup (VORP, PECOTA, PER, Win Shares, CARMELO, Plus-Minus, OPS+, WAR, ERA, HR/9, etc.). If there’s something that is performed on the field, court, track or ice, there’s probably a metric driven by artificial intelligence (AI) to measure it. In the health and wellness field, nutrition, dietary habits, calories, exercise, heart rate, body mass index (BMI), sleep time, etc., are not only tracked, but they can be monitored from any desktop, mobile device or smartwatch. It’s a strategy that transcends industries for good reason: The real-time collection of inputs and outputs provides essential information.

The approach of having information at your fingertips doesn’t seem to cause much controversy when sports or health data is being tracked, as cultivation is essential to measuring current standings and future progress. However, when applying that same thinking to the workplace, the who, what, where, when, why and how of data collection and analysis is perceived to be very “big brother” or “corporate watchdog.” It tends to raise concerns, but it shouldn’t.

In more than 25 years of experience managing high-performing, virtual, software development teams from all over of the world, I have found that the most successful employer-employee relationships embrace the value of data collection and analysis. Empowering both sides to improve skills, efficiency, products and culture is mutually beneficial, and it can produce returns that can be measured in time and human and financial resource savings.

Knowledge Is Power: AI Raises the Stakes

The future of work, and new recruitment and retention models such as the talent marketplace, along with maintaining a cohesive culture across geographically distributed teams makes this era, arguably, the most progressive workplace environment training leaders have seen this side of the millennium. Whether companies are leveraging full time, part time and/or freelance resources, the ability to determine value and build successful teams, while staying mindful of an individual’s career growth, is essential.

Expanding the lens to consider how AI-enabled tools affect performance and productivity measurement means digging deeper into the idea of IT project statistics. A revolution is being driven by those like “The Phoenix Project” and Google’s 2021 State of DevOps Report by DORA (DevOps Research and Assessment, acquired by Google in 2018). Just like in baseball, when few teams valued reaching base with a walk before Bill James and the “Moneyball” model went mainstream in 2003 — bringing sports analysis to the forefront — DORA metrics did the same for DevOps. By focusing on four key metrics — change lead time, deployment frequency, mean time to restore (MTTR) and change fail percentage — more companies began following and tracking those statistics than ever before.

It wasn’t that these metrics were new, but never had the context of what was happening in the workplace been as critically applied to increase their significance and power. For example, rather blaming who was responsible when a system went offline, merging the context of that moment with analysis (tracking MTTR) made it a teaching moment — with an ability to identify ways to improve and reward the success of improvements. This example not only betters the stability and quality of corporate software but raises the profile and importance of the teams working hard on production operations.

Innovation Spotlight: How AI Can Measure Talent Today, Tomorrow

Every company’s needs are unique. For software development, Github has some impressive AI-enabled tools, such as the automated dependency updates from Dependabot and the coding generation features of Github Copilot. Mundane maintenance tasks like updating and monitoring for dependency changes, or typing out templated repeated code, are handled automatically, which frees time for developers to be more productive in other areas and increase skills with time for advanced learning opportunities.

Another helpful AI-based coding tool that augments a user’s skills is Snyk, which proactively identifies dependencies and security flaws. Similarly, AWS CodeGuru is an option to find costly lines of code and pinpoint security vulnerabilities. The key benefit that AI brings to their functionality is speeding up the hunt for security issues and quickly fixing them, rather than wasting time trying to find them at all.

A next-gen approach for productivity and performance management of technology talent is a good way to combine all these tools and more into a decentralized coding platform to allow teams to dynamically add or remove vetted resources on demand. That vision is possible, only if organizations prioritize collaboration and performance management for maximum productivity as the ultimate goal. The implementation of AI-enabled tools could make it happen.

How to Integrate AI-enabled Tools

Training and IT departments have a unique opportunity to work together and redefine the talent ecosystem in terms of how companies and software professionals around the world connect to get work done. Performance and productivity measurement are a part of that. Here are three things to remember when infusing AI-enabled tools into the talent management process:

1. Empower teams to select the best AI-tools to support performance and productivity. Recruiters, trainers, developers, technologists, etc., are closest to projects and how tasks are accomplished. Encourage everyone to provide recommendations on tools and processes.

2. Create an implementation team made up of training and IT professionals. A cross-department effort to gather ideas and information — and then analyze workflow, current tools, missing elements, metrics to track, interpretation, etc. — will cover all bases and fuel participation when it’s time for adoption.

3. Value automation, but honor human resources. With AI, any engineer can potentially become a senior engineer through skills development and effort, but never forget it is human ingenuity combined with fantastic software that yields the best results. The ability to know when and how to leverage people skills with AI-enabled technologies is the key to finding the solution that works best for an organization.

Leveraging digital technologies to advance core business practices and enable workers and organizations to achieve their full potential continues to drive significant change. With a mission of transforming work for a billion people by 2025, the Center for the Transformation of Work (CTW) — a non-profit launched in 2020 by research and advisory consultancy Open Assembly — collaborates globally to make the use of digital talent platforms a more valid and sustainable way of working. AI-enabled performance and productivity measurement tools are a key part of managing talent worldwide.

“Increasing adoption of open work processes by creating a shared narrative around transparency and metrics benefits workers and organizations alike,” says John Healy, managing partner and expert in residence for the CTW. “Something like ‘speed to productivity’ has been a struggle for enterprises to measure. Using AI-enabled tools to quantify meaningful data beyond ‘time to fill’ brings value to the market. The accurate measurement of workforce productivity and performance are essential as we look to best manage talent in the future of work.’”

In Conclusion

For the first time (in a long time), training leaders can truly drive cultural change, morale, performance, productivity and efficiency with the use of AI-enabled tools. In partnership with IT, training leaders can continue to support staffing models that find, cultivate, keep and measure talent in high-value ways that support organizations and workers.