If you have a team of software engineers and want to move them to Mac, you will need to consider a number of things before you do so.
Team Intelligence and AI for Improving Software Delivery
We won’t say that Team Intelligence (TI) is new – but it is an important concept for understanding how our teams are evolving. While relevant to teams of any type, we’re focused on how TI applies to and works in software delivery. Just as technology is evolving faster, AI is opening the door for software development teams to evolve faster and improve software delivery, too.
What is Team Intelligence?
Someday, deep cross-functionality in psychology may be necessary to fully explore the potential of software development teams. We might look at emotional intelligence in greater depth, along with distributed cognition. For now, though, it’s sufficient that most developers (at least from Google and Microsoft) prefer for their managers to have stronger social skills than technical skills.
Few writers are as prestigious and prolific as Suzanne Gordon when it comes to Nursing and Healthcare, where teams work together to save lives. We’ll start with her elegant definition of Team Intelligence (TI):
“TI is the active capacity of individual members of a team to learn, teach, communicate, reason, and think together, irrespective of position in any hierarchy, in the service of realizing shared goals and a shared mission.”
Team Intelligence is nothing new per se, we’re just getting a much better understanding of what can facilitate its development and how it works, at least in a civilized environment. The distinction is appropriate as the military has been quite active with the ins and outs of Team Intelligence for ages. The military makes it a habit of throwing 30 strangers together and getting them to work reasonably well as a team in ~90 days (and beyond).
Most of us “mostly” don’t work in such highly regimented environments. No, no, no… An awful lot of us, at one time or another, “volunteer” to work in the maelstrom of Chaos with tech startups and scaleups. Anywhere people have to work together, Team Intelligence improves performance and results.
Four Components of Team Intelligence
As she points out, there are four requirements to cultivating team intelligence. Interestingly enough, they reinforce the interests of Agile and DevOps when it comes to developing cross-functionality:
- Development of Team Identity, a complex matter in its own right. It’s reflected by team members thinking more in terms of the team’s effort and goals than just their own individual roles.
- Regardless of position, each team member must be willing to be mentor and mentee, to share and seek insights.
- Everyone is involved in the feedback loop (i.e. more than 1-on-1’s or Retrospectives) and should be mindful of helping each member of the team be the best they can be.
- Each team member must cultivate an understanding of each other’s roles, how what they do influences the efforts of others, upstream and downstream.
How Do You Continuously Cultivate Team Intelligence?
Again, Team Intelligence is not new – odds are you’ve been developing it with your teams for years.
- Peer Code Reviews and PR Code Reviews are obvious ways for devs to collaborate, ask questions, and share what they know.
- Retrospectives are a safe zone for everyone to share with all team members what has worked well and what can be done better.
- One-on-One Meetings provide managers and developers a feedback loop to help each other make it easier to do their jobs better – reduce friction, keep things fresh, learn new things.
Add formalized coding standards or style guides, mentoring, walkthroughs, training programs, conventions, and continuing education programs. It all cycles into cultivating team intelligence.
Seeing Team Intelligence in Action
It’s not hard to see Team Intelligence in Action everywhere, daily. In a basic sense, Team Intelligence reflects in team members thinking about how their work will impact not just the product, but the efforts of other team members.
Often enough, it’s the little things that when automatically factored into our work add up to big improvements for other team members, and the team, overall:
- “Hmmm, I suspect Danni will smell something’s off in this section of code, and he’s usually right… let me see how I can improve it before submitting a PR.”
- “I’m still pretty new with Python and this task looks more complex than I’ve tackled before, let’s see if I can hook up with JC for a Peer Code Review before I get started as he’s our best Python dev.”
- “Jackie was looking to go to a conference for Swift devs but wasn’t able to make it, I’ll send her the link to this upcoming virtual conference in case she’s still interested.”
- “The junior devs are having challenges with tasks on our new project, I’ll take the time to find some good examples on the logic and structure they can consider and arrange a quick walkthrough.”
- “As we’re rotating our Scrum Masters bi-weekly, some of our tasks are ambiguously defined and can be broken down further – let’s spend a few extra minutes in our next Spring Planning session to provide some guidance and update standards documents.”
- “Our senior devs are going to be out the next few days, I should pick up some of the slack on the PR Requests.”
How Do AI Assistants Expand Team Intelligence?
Team Intelligence dynamics are on the verge of a radical transformation with the help of AI-powered Digital Assistants offering “Insights On-Demand.” The one issue that we know well is that it can be time-consuming to manually dig through analytics to find actionable insights that can help anyone. It’s useful, but it can be hit or miss, and to some extent – you really need to know what you need to look for.
The BIG DEAL behind AI boils down to two main points:
- 10x Faster Cycle Time for Data-Driven, Actionable Insights for Improvement.
- Anyone, non-devs, and non-managers, can act upon the insights.
Anyone, from Individual Contributor to C-Levels need only ask their AI Assistant a question to receive:
- A summary of the case/situation
- Relevant data in an easy-to-understand chart or graph
- Metrics explained in terms anyone can understand
- Next Best Actions they can take to improve or fix the situation
How Different Stakeholders Can Use AI Assistants
C-Levels and BI specialists have a host of angles for insights on how they can best help their managers and ways to optimize project, team, as well as vendor budgets and performance. AI also provides executives with a strategic view into how their company’s existing skillsets tie into future plans and projects, and helps to keep everyone aligned on critical company KPIs.
For Engineering Managers and Team Leads, AI makes it faster and easier to help their teams and team members. AI serves as a force multiplier on a manager’s team improvement efforts. The hour spent digging through analytics to help one team member instead supplies you with personalized insights for each member of your team that can be used in standups or your next 1-on-1 meeting.
Individual Contributors could be tackling a challenging task, seek to improve their own performance, or have spare time to see which other developers they could help most. With a quick question, they could be on their way to resolving each matter without delay. Transparent access to benchmarking data also provides an objective view into where they are vs. what they can realistically achieve – along with the insights on how to achieve their goals.
Measuring the Impact of AI on Team Intelligence to Improve Software Delivery?
Endless Modernization: How Infinite Flow Keeps Software Fresh by Jim Johnson and Hans Mulder of the Standish Group asserts, “The root cause of software project failures and challenges is slow decision latency.” It makes for some simple equations, even if adding them all up gets complicated.
Software developer wages are the single most expensive line item in software development. Every hour of delay, waste, and rework, counts against the definition of successful software delivery – it’s delivered on time, on budget, and works as intended. We could add more qualifications, but most projects are challenged enough to score on these.
AI provides real-time awareness on what can be improved and how it can be improved. It’s this awareness that is of extreme importance when it comes to successful software delivery. This is often data that would require hours or days reviewing analytics manually, and only after damage has already been done.
The impact for each team could vary quite substantially, suffice that AI’s ROI is likely to scale in direct proportion to the size of the organization. This owes to the inherent complexity and administrative overhead of larger organizations, but also greater knowledge to be shared amongst all teams and team members.
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