Over the past three years the pandemic has changed our workplaces. According to a McKinsey report The Future of Work After COVID (link), the pandemic accelerated existing trends in remote work, e-commerce, automation and people switching occupations. Employees deemed essential workers, remained on the job working under new protocols and conditions.
One of the primary concerns of senior leaders during this time are the negative impacts of not physically getting together. Managers fear a loss of creativity, collaboration, and connection by working remote. Many early-in-career workers and recently hired colleagues were interviewed, on-boarded, and now work remotely, never having visited a corporate office. This has led many CEOs to push employees to come back to the office and other leaders to significantly increase the use of teams.
Teams are an effective way to build camaraderie, skills, solve problems, and foster innovation. But speaking with several colleagues I have a simple message to senior leaders – stop with all the teams! People are overwhelmed with work assignments, balancing work with needs at home, and days filled with meetings.
In this environment I thought it would be a good time to remind managers to adopt the concept of complexity matching to know when and how to effectively use teams. I first learned of the concept from a Clayton M. Christensen, Richard Bohmer, and John Kenagy article that described how clinicians diagnose and treat medical problems.
This is the simplest case where data and information result in an unambiguous diagnosis and a proven therapeutic strategy. In this case it is clear what the problem and solution are. I’ve seen managers form teams and hold weekly or bi-weekly meetings to address these simple problems. The meetings devolve into status update meetings that simply review completed work and waste the time of your most skilled colleagues.
In this situation replace your regular meetings with a dashboard or report that chronicles the identified issues, actions taken, and a person to contact if they have further questions. There are two scenarios where teams are an efficient use to rule-based problems:
- Teams of early-in-career colleagues get together with a mentor to learn how to recognize rule-based problems and develop the skills necessary to effectively address them; and
- A team of experienced colleague meets occasionally (once or twice a year) to review, validate, or update standard operating procedures for addressing these types of problems.
Pattern Recognition Problems
In this case no single piece of data or information yields an answer, but multiple data points lead to a definitive diagnosis. When a diagnosis is confirmed, a relatively standardized treatment is followed.
Infectious mononucleosis is diagnosed when a pattern is observed – whole body fatigue, fever, chills, malaise, and swollen lymph nodes. Once a diagnosis is confirmed, treatment protocols of medications, self-care and supportive care exist.
Cross-functional teams and meetings are an effective way to review the data, identify patterns quicker and determine the right solutions. Over time an organization wants to find ways to evolve pattern recognition problems into rule-based problems. Form experienced, cross-functional teams to develop better standardized solutions. They help you answer the following questions:
- What environmental trends should we monitor to identify problems and opportunities faster (political, economic, social, technology)?
- What internal weaknesses are preventing us from achieving our objectives?
- What improvements can we make to our existing solutions?
The most difficult disorders, diagnosis and treatment occur in a problem-solving mode. The collective experience and judgement of a team is needed, and involves cycles of testing, hypotheses, and experimentation. The discovery, diagnosis and treatment of COVID-19 is a perfect example. Teams of researchers, clinicians, politicians, regulators, manufacturers, etc. were required to diagnose and treat the disease. The complexity of the virus’ mutations requires ongoing study, experimentation and problem-solving.
This is the highest and best use of teams.
If you want a general guide, target 50% of team time to work on complex problems, 40% on pattern recognition and 10% on rule-based problems.
Use teams effectively by having them work on the right types of problems and managing the amount of time they spend on each. Doing it right will lead to the outcome your organization wants – high-performing teams that solve complex problems, increase capabilities, and drive innovation.