AI-Powered Executive Summaries to Maximize Your Insights

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How do you notice and prevent software developer burnout as a team lead or engineering manager? Burnout affects each person a bit differently. It often starts subtly and progresses incrementally. Developer burnout feeds into a vicious loop that can make your developers quit or even change career path. If not corrected, it can also lead to health consequences. The good news is that its onset can be identified, prevented, and rapidly alleviated. Let’s examine how.

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By GITENTIAL TEAM

How can AI-powered executive summaries help with software delivery? A good question and it has a fairly simple answer. Endless Modernization by the Standish Group sums it up succinctly, “The root cause of software project failures and challenges is slow decision latency.” This is very important because most sources agree that ~70% of software projects are challenged on time, budget, and/or specifications. However, AI reporting has several mechanisms to enable near real-time decisions. Plus, this trick isn’t just for kids er… um… executives, AI reporting is useful for all software development stakeholders to squish decision latency, but also dramatically improve organizational alignment

What is the Purpose of an Executive Dashboard?

Executive dashboards provide decision-makers with KPI reports for their most important business objectives and organizational health. They work like executive summaries with nice color-coded charts and graphs while also making it easy to dig deeper for more granular information. They are customizable as each CxO and stakeholder needs different data.

Gitential’s dynamic dashboard for software development performance analytics.

Most executive dashboards capture data but they leave the analysis to you. That analysis can take time to dig through the data to determine the root causes of problems, what can be optimized, and by how much. The time spent analyzing delays decisions. In software development, time is very expensive. Moreover, the longer a problem is allowed to persist, the more likely it is to compound in cost, whether it is excessive code complexity or the cost to fix bugs

We reference this simple diagram frequently as it directly illustrates the “root cause of software project failures.” If you can solve the decision latency issue you solve a lot of problems faster – and this brings us to the role of AI.

Analysis and Decision Latency

How Does AI Help with Executive Summaries?

Gitential’s in the process of transforming its analytics dashboard into an AI Assistant because AI reporting squishes analysis latency into an almost instant, real-time process – about as fast as you can formulate questions. For starters, this gets you into the decision-making process faster. AI also provides a number of mechanisms to radically accelerate the decision-making process, too:

 

  • Insights On-Demand – Ask questions, get data-driven responses immediately.
  • 10x your 1-on-1’s and OKRs – More actionable insights exponentially faster.
  • Next Best Actions – Recommended actions to take to solve and optimize issues.
  • Advanced analytics – predictive capabilities to proactively mitigate and prevent risks.
  • Real-Time Alerts – notify you if and when critical KPIs deviate from expectations.
  • Single Source of Truth – Everyone works from the same cost and performance metrics making it easier to align your entire organization on improving specific KPIs.
  • Faster Cross-Functionality – Shared data and recommendations make it easier for everyone to better understand and assist other stakeholders while serving as a training tool for newly promoted team leaders and managers.
Beta version of Gitential’s Next Best Actions

Again, AI reporting is useful for all stakeholders, not just CxOs. In software development, this extends to BI analysts, HR managers, Engineering Managers (encompassing DevOps and DataOps), and individual developers, too. 

So, it’s hard to say that AI makes it possible for everyone to make decisions instantly. In some cases, it can, like in convincing HR or Engineering Managers to start a training program to skill up existing developers vs. hiring more. In a lot of cases, decisions require approval from other stakeholders. A BI Analyst might find a project or feature to have a low value or ROI and that resources allocated to it would more optimally be applied to a different project or even pushed to a different team or vendor. This could require C-level approval.

Implementing Actionable Data

When a stakeholder does require approval or agreement from other stakeholders, it’ll be necessary to share the data and discuss it like normal. AI helps here with Next Best Actions making it easier and faster to prepare proposals, action plans, risk assessments, and cost performance analysis. Then, it’s up to your regular organizational processes – whether to send reports on the fly via email for the approving authority to review or discuss them in your next meeting.

For the engineering side of the implementation equation, Agile and other best practices provide ample opportunities to rapidly initiate action:

  • Standup Meeting “Tips of the Day”
  • Sprint Planning and Retrospectives
  • Mentoring
  • Walkthroughs and Reverse Walkthroughs
  • One-on-One Meetings
  • Objective and Key Results Meetings

Organizational Alignment and OKRs

Having addressed how AI helps to squish decision latency to resolve challenges faster, let’s turn to how it helps calibrate organizational alignment. Executives have the task of navigating their organization to continuous profitability, growth, customer satisfaction, and inspiring objectives like organizing the world’s data, colonizing Mars, or mining asteroids. 

Say what? Well, Google and the world’s two richest people didn’t get to where they are by doing what they knew they could do…

So, let’s interrupt this post with Dr. Benjamin Hardy’s talk on 10x’ing. He’s speaking in personal terms in this video, but the same applies organizationally. As an executive or startup founder, you might set a goal of improving revenue from $1 million to $2 million this year. In doing so, everyone will be targeting what’s needed to accomplish that. Conversely, the 10x concept pushes you to align and motivate everyone to make a Moon Shot – and generate $10 million. 

With inflation these days, that’ll be a lot like making $1 million, so some aim for a Mars Shot to make $100 million… Being facetious. Sorta.

Aligned vs. Unaligned Organizations

Without organizational alignment, a BI Analyst seeking to cut costs could suggest downsizing the development team to make this quarter’s balance look better – only for the HR Manager to rehire or recruit new developers in the next quarter or two. Similarly, the HR Manager might be hard-pressed to properly vet them in the interest of getting some warm bodies. This leaves the engineering manager in a pickle or ten while individual developers pick up on the chaos and leave for greener pastures with fewer earthquakes.

Aligning everyone to the same objective and metrics corresponding to their role helps assure no one’s working at cross-purposes. The BI Analyst can determine optimal team sizes and commitment to each project to improve value and reduce waste for the long term. Less recruiting lets the HR Manager focus on issues of training and retention. Engineering Managers can better optimize efforts for stable teams than ones in constant flux. All of these efforts apply to an environment that’ll keep developers around longer and steadily improve their personal performance.

So, How Does AI Help Alignment on KPIs and OKRs?

Once an executive or manager defines clear and specific objectives, the entire organization can bring laser-like focus on two things – what they need to do to achieve them, and how they can help others achieve them, too. This cycles back to executives and managers with what they can do to help their team accomplish their goals. 

At this point, it may help to take a look at how this works organizationally for several stakeholders. This objective is pervasive, suffice that it could be how to reduce defects, how to increase customer engagement, or any number of other objectives.

Objective Increase profitability organizationally or by project
Core KPIs Cycle Time and Cost Performance – doing things faster, better, and cheaper.
Executive Ideally, executives should:
  1. Set data-driven objectives defining what needs to be accomplished, but not how.
  2. Communicate the objectives and why they are important to their managers and specialists.
  3. Let them examine the data and come up with an action plan.
  4. Regularly engage in one-on-ones to track progress, discover what you can do to clear blockers and best allocate organizational resources to help their plans succeed.
We’ll discuss more of what this means for executives below. With objectives in hand, stakeholders can proceed to ask a lot of questions. AI provides the data, an analysis of the data, and the next best steps to solve or optimize issues. But where a manual effort could take weeks and months, your entire organization could do this in a few days and individual managers could do their part in perhaps a couple of hours – or even minutes for issues of a smaller scope.
BI Analyst
  • Are our teams the right size?
  • Do we have the right mix of junior vs senior devs?
  • Why is Team/Vendor A performing better than Team/Vendor B?
  • What work can we safely shift to Team/Vendor A?
  • Which projects/features are generating the most value?
  • How is work being divided across the projects?
  • What low-value efforts can be streamlined?
  • What is the expected $ impact for any given change?
HR Manager
  • What skills are our teams weak in or lacking?
  • How do our existing skills line up with the org’s strategic plans?
  • Is it best to implement training, hire, or outsource to fill our skill gap?
  • How are we faring on turnover and retention?
  • Do we have any developers at risk of leaving that I should talk with about incentives and opportunities to stay?
  • Is there a noticeable difference in the performance of developers hired via specific recruiters, employee referrals, trade fairs, or other job searching venues?
Engineering Manager
  • What’s my team’s weakest KPI and how can it be improved?
  • Where are our bottlenecks and what can we do to fix them?
  • How are tasks aligning with dev expertise?
  • Can we break down tasks to better match our mix of dev experience?
  • How is our code complexity and what can be done to improve it?
  • How can we optimize code review pairing and mentoring?
  • Who is over/under-utilized and what can be adjusted to fix that?
  • How fast are PRs being picked up and can that be improved?
  • How are devs/vendors responding to performance reviews?
Individual Developer
  • What metric am I most challenged on and how can I improve it?
  • How does my performance compare with others in my team, project, company, or industry?
  • Who is the best developer in our org that I should talk with when it comes to ‘x’ language, technology, or metric?
  • What skills can I proactively boost to help fill my team or company’s skill gaps?
  • Which other developer is challenged in an area where I’m strong so I can reach out to them for peer code reviews?

What AI Reporting Means for Executives

Decision-makers have essentially 3-4 options – approve, decline, or ask for more data. Delaying a decision is a decision, suffice that some actions may have dependencies or are of a secondary priority. With everyone in your organization having access to “Insights On-Demand” – they’re able to get you the data and their action plans on a vastly accelerated basis. 

While developers can only ever become 100% efficient, there is (as yet) no ceiling when it comes to productivity. According to Stripe’s Developer Coefficient, the average team loses about 32% of productivity due to inefficiency. In effect, for every three developers, you’re getting the throughput of two. 

Implementing Continuous Integration / Delivery / Deployment can radically enhance productivity – each progressively adds automation to the SDLC. Puppet indicates teams practicing Continuous Deployment have lead times 2,555x faster than manual teams – that a CD team can do in an hour what would take a manual team up to 6 months! They also deploy 200x more frequently and have a 24x faster MTTR. 

Your decisions and assistance are invaluable and often of strategic importance. Faster data-driven decisions save time, help optimize developer hours to better meet project requirements. AI helps you address all three challenges (cost, schedule, and specs) in one fell swoop. If today’s tech giants got to where they are mostly without the benefit of AI, it’s worth looking at what you could do with it. 

Alpha Centauri is only 4.4 light-years away, and while that may stretch the whole 10x thing a bit… what kind of goals will we be setting 10 years from now?

About Gitential

Gitential is an Analytics and Engineering Intelligence service provider bringing visibility and optimization highlights on teams’ productivity. Our mission is to enable faster, data-driven decisions to continuously improve software delivery team cost performance and proactive risk management.

Ready to explore different ways to improve your software projects’ efficiency? Schedule a meeting and we will be happy to listen to and discuss your needs.

Have a project but are not quite ready to contact us? See if Gitential is a fit for you!

FREE TRIAL FOR LIFE
for up to 5 repos, 10 devs and 3 months of data

Post updated: February 08, 2022

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How to Notice and Prevent Software Developer Burnout

How do you notice and prevent software developer burnout as a team lead or engineering manager? Burnout affects each person a bit differently. It often starts subtly and progresses incrementally. Developer burnout feeds into a vicious loop that can make your developers quit or even change career path. If not corrected, it can also lead to health consequences. The good news is that its onset can be identified, prevented, and rapidly alleviated. Let’s examine how.

Read More »
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More than half (53%) of the software development teams do code reviews on a daily basis, according to SmartBear’s 2019 State of Code Review. They find, as one would expect, that more frequent code reviews have a direct correlation to higher-quality code. As their report goes on to say, “There is a compounding effect when you introduce all the benefits of code review into daily behavior. Communication improves, knowledge about the codebase is shared, and fewer bugs make it through development to QA.”

Read More »
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