AI-Enhanced Teams - Are AI Assistants Worth It?


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AI-Enhanced Teams are a relatively new phenomenon in our On-Demand World where “Access is better than ownership.” Devising your own AI is a very expensive and time-consuming task. But, with companies like Gitential, AI services are following the Software as a Service (SaaS) subscription model. This makes the Power of AI affordable for any company seeking to boost its ROI. The advantages of an AI Assistant for software development are extensive but coalesce around one thing that’s even more valuable than Access… Awareness.

Real-Time Awareness to be precise.

(Please note that we’re still in the process of implementing AI, but t’s worth getting a taste of what AI will mean for software engineers. All accounts are provided with Early Access to all of our new features as they are rolled out. )

Real-Time Awareness is Everything

Those with the interest might check out Endless Modernization: How Infinite Flow Keeps Software Fresh by Jim Johnson and Hans Mulder of the Standish Group.

Their assertion is as profound, as it is simple, “The root cause of software project failures and challenges is slow decision latency.” Time to pull out that chart again…

The solution is obvious enough, “…to improve project performance, organizations need to find ways to make decisions faster.”

AI Assistants fit the bill for reducing decision latency at ALL levels of an organization. As we’re conceiving for Gitential, decision latency is reduced by three active components:

  1. Modern Analytics – The ability to ask “Google-like” questions for real-time, automated, data-driven insights.
  2. Real-time Alerts – Receive an alert (mobile, email, sms) whenever a specific metric exceeds danger thresholds that you define.
  3. Next Best Actions – Data-driven actionable insights based on behavioral analytics and Agile best practices.

AI dramatically reduces the latency to capture and analyze data, so that what used to take hours or days can be done in seconds or minutes. Moreover, manually digging through a ton of metrics is not always guaranteed to result in actionable insight.

Time to Implement Insights

Now, it’s true – acting upon insights, implementing decisions still takes time. It’s still necessary to share the insights with others and to reach an agreement on the next steps. Fortunately, Agile software development and other best management practices are rich with both personal and team-wide opportunities to engage with developers:

  • Standup Meeting “Tips of the Day”
  • Sprint Planning and Retrospectives
  • Story-Splitting Efforts
  • Resource Library Examples
  • Optimizing Code/PR Reviews and Mentors
  • Walkthroughs and Reverse Walkthroughs
  • One-on-One Meetings
  • Objective and Key Results Meetings

Time for 10x’ing

We recently presented an example of a software engineering manager might spend 1 hour sorting through a developer’s performance analytics to find a way to reduce their defect rate by just 1 per month and that it took just 1 hour to fix each defect. The manager invested 1 hour and gets 12 developer hours over a year in return. With wage differential, let’s round that to a nice, simple 1,000% annualized ROI.

Either way, that’s a lot of variables:

  • Cycle Time for Insights,
  • Defect Rate Reduction,
  • Mean Time to Repair
  • Difference of Manager/Developer Wages.

In our Guide to the Cost to Fix Bugs, for some large companies, the MTTR can be 10 hours or more. Suddenly, we’re at a 10,000% ROI.

With an AI Assistant, we add another variable. The engineering manager need only spend 5 minutes asking questions in a ponderous sort of way for each developer on their team. This could be anywhere from 3 to 20 or more. But let’s give the manager a 10-minute break, to keep it at that nice x10 multiplier for the hour, for a potential 100,000% ROI.

Feel free to do more precise math if you like, but Holy Smokes – that’s even better than the 10,000% ROI on Design. If hiring a good designer is like printing your own money; adding an AI Assistant practically turns you into the Federal Reserve, because there are more variables yet to account for…

Organization-Wide Alignment

Once you have a Digital Assistant, you’re free to ask it any questions you want. But, the greatest benefit is likely to come from aligning the efforts at every level of your company or org to achieve maximum performance.


Organizational level

Monitor the health of all your software engineering related projects and understand which ones are at risk.

Program/Project Level

Track overall project progress, identify bottlenecks, and deep-dive into your project team’s performance.


Identify social and isolated developers, increase retention by early identification of burnout or low motivation.


Pamper your star developers, praise them based on their performance and provide support for personal growth.

For software development companies, a very big part of that is enabling your developers to be the best developers they can be – as relates to your project and mission:

Role What an AI Assistant Can Help Answer


In what areas are the startups I’m invested in most challenged? Which resource are they in need of the most? How do my companies compare with each other? Which companies should focus on optimizing existing resources, which companies are ready for growth? Has their performance improved, declined, or stagnated over time… and why?


What are my engineering managers’ greatest challenges? What’s the #1 thing I can do now to make it easier for them to improve their teams’ performance? Are our company’s strategic initiatives realistically aligned with our current team experience and skill base? What technologies do we need to strengthen to better fulfill our goals?

BI Manager

Which of our projects/teams/vendors are performing the best and why? What work can we shift to our best performers? How much would we save? What can other teams do to improve their performance? Is there performance improving, declining, stagnating over time?

HR Manager

Where are we finding our best new hires? What’s the overall health of our developers? Who’s at risk of burning out or leaving for another job? What can we do to reduce turnover? How can we improve our training programs to better support engineering managers and their teams?

Engineering Manager

Who are the best developers for this project? What are my team members’ greatest challenges and what can I do to ease them? Why has performance on this project dipped and what are my options to get things back on track?

Individual Contributor

How do I stack up against the rest of my team? What’s my greatest coding challenge and what can I do to improve my performance on that metric? Is my performance improving over time, and by how much? Who would be a good developer to talk with about a specific coding issue (code churn, code complexity, or specific programming language)?

Full organizational alignment sees your VC firm, CEO, BI and HR Managers all asking questions about how to best help the Engineering Manager to best help their developers. It could be as simple as hiring a single specialist or pushing more work to your best-performing vendors.

AI Assistants provide continuous awareness enabling you to continuously and endlessly improve project and team performance.

Project Management Failure Rates

The standard for a successful software project entails being completed on time, on budget, it works as intended, and people actually use it.

The Standish Group’s 2020 CHAOS: Beyond Infinity Report presents that Agile software projects led by experienced project managers are just as likely to fail as they are to succeed, 18% vs 20%. You can see the charts without forking out $600 at Vitality Chicago, which gets to the heart of the matter on project success rates by project manager experience.

Their project failure and challenged rates are generally reflected by Wellingtone, a UK-based certified training provider for the Association for Project Management in their 2020 State of Project Management Report:

  • 29% of projects are mostly or always completed on time
  • 43% of projects are mostly or always completed on budget.

Wellingtone also goes on to note that 54% of the companies that they surveyed do not have access to real-time KPIs.

PMI’s Reasons for Project Failure

Tracking back to the Project Management Institute’s 2018 Pulse of the Profession, we’re provided with 16 primary causes for project failures. Half of these causes can be seen in real-time, while the other half can be seen with trends and when making comparisons. Inaccurate project vision or requirements gathering may not be seen immediately, for example, but they will reflect across several performance metrics (productivity/waste) in time, not to mention Sprint planning.
Causes you can see in real-time: Causes evident in trends and benchmarks
  • Inadequate/poor communication (29%)
  • Inaccurate cost estimates (28%)
  • Resource dependency (26%)
  • Inaccurate task time estimate (25%)
  • Limited/taxed resources (21%)
  • Inadequate resource forecasting (18%)
  • Team member procrastination (13%)
  • Task dependency (12%)
  • Changes in organizational priorities (39%)
  • Changes in project objectives (37%)
  • Inaccurate requirements gathering (35%)
  • Inadequate project vision or goal (29%)
  • Opportunities and risks not defined (29%)
  • Poor change management (28%)
  • Inadequate sponsor support (26%)
  • Inexperienced project manager (22%)
We can naturally suspect a degree of variance between the Standish Group and PMI’s positions regarding the value of experienced Project Managers. Project Management Professional (PMP) Certification is one of the hardest credentials to acquire. The Standish Group’s report is based on roughly 10k projects while PMI has 600k members. But guess what? The Talent Gap strikes again. It’s almost hard to believe, but according to the PMI, the global economy will need 25 million new project professionals by 2030. Obviously, these are not all related to software development projects, but we can plop that onto the 55 million more skilled workers China already needs now.

Can AI Assistants Be Good Teammates?

AI Assistants can provide people with a phenomenal boost to productivity and efficiency. It’s always there, 24-7-365. But at the end of the day, it’s a software system – not a real person. It may never steal your lunch, but hanging out with your digital assistant after work has limited appeal. Presently, at least, they don’t drink, smoke, dance, bowl, hike, engage in sports, or anything other than specific work functions.

These days though, it’s not too unusual to hear about someone marrying a hologram for instance. No judgment. Imagine the social adaptation that we’ll see as AI, Robotics, and other technologies evolve and converge over the next decade.

Article updated: July 20, 2022

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