
Making the Switch: The Reality of Moving from Windows to Mac for Your Software Engineering Team
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.
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.
Here’s what’s new in our January 2023 Release Notes:
* Tables Columns Sorting Improved
* Reconcile Commits Count Between KPI Card and the Table
* Efficiency Tab Improvements and Efficiency KPI Cards Align
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.
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.
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:
Again, Team Intelligence is not new – odds are you’ve been developing it with your teams for years.
Add formalized coding standards or style guides, mentoring, walkthroughs, training programs, conventions, and continuing education programs. It all cycles into cultivating team intelligence.
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:
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:
Anyone, from Individual Contributor to C-Levels need only ask their AI Assistant a question to receive:
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.
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.