This interview analysis is sponsored by Pieces and was written, edited and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
As with so many developments in AI already revolutionizing entire industrial sectors, the day-to-day of professional disciplines and workflows in the modern era are being molded directly by software developers. In turn, software development as a discipline is hardly immune to the same changes.
Developers can harness the power of AI and machine learning in the context of DevOps to facilitate accelerated software development by automating code reviews and analysis. A recent McKinsey study found that generative AI tools can increase developer speeds in code generation and documentation alone by 35-50%. Automating these tasks allows for earlier detection of code flaws, security issues and defects, enhancing end-user performance in the process.
Emerj CEO and Head of Research Daniel Faggella recently sat down with Jason Wells, Chief Technology Officer at Pattern, and Tsavo Knott, Co-founder & CEO of Pieces, to discuss how AI can help solve the challenges in developer teams.
Pattern is a tech company that builds an AI-driven platform to help businesses grow faster and sell more on eCommerce marketplaces. In contrast, Pieces is an AI-driven software company whose platform enables software developers to work more efficiently.
Hearing from both sides of the table helps enterprise leaders better understand what management strategies – for human expertise as well as leveraging the right technology – are making the biggest differences in expediting developer workflows.
The following analysis examines five critical insights from their conversation:
- Role of leadership in encouraging creative problem-solving: Fostering innovation involves the crucial role of leadership expressing belief in complex objectives to encourage creative thinking within teams.
- Moving talent across teams based on strengths found in data: Placing team members in roles where data-driven insights indicate their skills most align – rather than where they’ve built seniority or where they started – to help optimize the team’s performance and output.
- Strategies and criteria for recognizing team and employee accomplishments: Rewarding team members’ contributions through practices such as feature release acknowledgment, various rewards systems and integrating positive customer feedback to highlight the direct impact of the team’s work on end users.
- Identifying opportunities for copilots in developer workflows: Integrating data and AI in software development to automate routine tasks through code suggestions, using personal AI-driven assistants to provide guidance and code corrections.
- Importance of managers with high emotional intelligence: Managers with higher EQ better understand how individuals work and collaborate. This skill of managers plays a vital role as companies scale up.
Expertise: Software development, Analytics and Agile Methodologies
Brief Recognition: Before joining Pattern in 2014 as Chief Technology Officer, Jason spent the previous four years in various technical and software engineering roles for the LDS Church. Previously, he was a Software Developer for Quomation Insurance Services. He graduated from Brigham Young University with a Bachelor’s in Information Technology in 2012.
Expertise: Coding, Software Development, Entrepreneurship, Interactive Media, Computer Science
Brief Recognition: Tsavo graduated from Miami University in 2018 with a Bachelor’s Degrees in Game and Interactive Media Design as well as Computer Science. Before co-founding Pieces in 2020, he was a vice president and co-founder of Accent.ai, a language learning platform.
Role of Leadership in Inspiring Teams and Encouraging Creative Problem Solving
Both Jason and Tsavo highlight the importance of top-down inspiration and leadership in a specific context. They both stress that when leaders express belief in the feasibility of complex objectives, they can inspire their teams to tackle these challenges with renewed commitment and creativity.
Their shared viewpoint emphasizes the role of leadership in driving innovation and fostering a culture of ambition. Specifically, they emphasize challenging their teams to think creatively when a new task is first considered impossible and not immediately dismiss seemingly any daunting suggestions.
They recognize that, at times, the initial response of a team might be skepticism or doubt, especially when faced with a problem they consider “impossible.” However, starting from a vantage point of possibility and encouraging their teams to explore those possibilities while not hastily dismissing the idea can motivate teams to engage in more profound and more innovative problem-solving:
“Sometimes you want people coming from the top down, saying, ‘I think this is possible,’ and the team replying, ‘I don’t – that’s really hard.’ And then you go and say, ‘Well, I want you to go figure it out.’ And I think some of our most interesting products at Pattern have come out of challenging a team with something like that. Then they go back and seriously think about it. They don’t just dismiss it out of the hand. They’re hearing someone say, ‘I think it’s possible. Go tell me how.'”
– Jason Wells, Chief Technology Officer at Pattern
Moving Talent Across Teams Based on Strengths Found in Data
Tsavo also touches on the dynamic between different teams involved in the development and production process. He emphasizes the need for collaboration between teams and the breakdown of silos.
Jason points out many of these challenges are symptomatic of growth in tech companies. “Some of it is, when you really get big, it’s about knowing what teams do. It’s also about having enough of the same people on each team,” he explains. “So it’s not just looking at a huge DevOps team, ‘Who do I talk to over there?'”
While the machine learning team might have phases of intense focus during which they explore their work’s viability, seamless communication and cooperation are also needed. Tsavo highlights the importance of finding the right individuals or experts to carry out specific tasks efficiently.
Pieces’ platform addresses these challenges by using an ML model to connect people based on their workflow, responsibilities and tasks – accounting for their personal preferences and aspirations but in the context of how data is tracking their results:
“I’m a technical founder,” Tsavo prefaces his contribution, “and Sam [Jones] sees that as our Chief AI Officer: this person is actually really good at that, but they may be interested in something else. We’re finding data that can help us thread that needle on that topic of ‘What’s their happy place?’ and where they want to go versus what’s the highest ROI for the company?”
He references Pieces’ own ‘copilot’ software as a tool that can help teams identify who to reach out to for specific tasks and how to facilitate smoother communication between team members.
Agreeing with Tsavo, Jason offers additional insights and shares with Emerj the importance of team cohesion and having the right people with the right skills involved. He suggests doing so requires more than having a large DevOps team where it becomes challenging to determine who to contact; it is rather about having the right people with the right expertise in each unit.
However, Jason offers a slightly different perspective on fearlessly moving talent based on their strengths and inclinations. He advocates placing team members in roles where their skills align most effectively.
The concept is a pragmatic way of optimizing the team’s performance and output. While Tsavo mentioned the importance of identifying the right individuals, Jason takes it further by suggesting that team members should be assigned roles where they can excel the most, even if it means moving them to different areas of the business.
Strategies and Criteria for Recognizing Team and Employee Accomplishments
Jason discusses the importance of recognizing and rewarding the contributions of team members in the context of feature releases and feedback. He explains how Pattern approaches rewards systems based on employee and team outputs:
- Feature release acknowledgment: He mentions the significance of acknowledging the individuals working on specific organizational features. He highlights the practice of internally attributing the work to those who contributed and publicly calling out the team members’ names when announcing new features.
- Awards and rewards: Jason also shares that Pattern has implemented various mechanisms for recognizing outstanding work. It includes restricted stock unit (RSU) awards as well as an employee-to-employee recognition system. In the latter, any team member can recognize their peers for exceptional work, and this recognition can lead to rewards or other forms of acknowledgment.
- Customer feedback and motivation for elevating end-user experiences: Jason underlines that input from the business side is essential in this recognition process. When team members receive positive feedback from customers or clients, such as hearing that a product is transformational or time-saving, the feedback is then shared with the whole team. Such positive, customer-derived feedback not only motivates the team but also underscores the impact of their work on the end-users.
Identifying Opportunities in Inter-Team Communications for Greater AI Adoption
In turn, Tsavo describes Pieces’ product and the underlying philosophy behind it as a platform for helping developers manage their work in progress more efficiently.
The critical point Tsavo emphasizes is their commitment to first making the product valuable at an individual level. Once developers experience its benefits personally, the tool naturally fosters collaborative experiences and can be scaled across entire enterprises.
In response, Jason underscores the challenge of dealing with redundancies and inconsistencies in data and processes:
“That’s probably the biggest thing that you’re always trying to surface,” Jason vents about another recurring scenario for Pattern’s growing eCommerce business. “Who works on this data? I’m trying to get this metric that isn’t even available; people like fighting things – like I just wrote two APIs that serve up the exact same data.”
His examples reinforce Tsavo’s point that there is room for improvement in optimizing data and developer processes across the organization.
Moreover, Jason highlights an organization’s communication and information gaps as a significant challenge. He shares an instance where a developer coordinated with the wrong product manager due to miscommunication, resulting in inconsistencies in deployment. The example emphasizes the critical role of effective communication within organizations and hints at AI’s potential to address these communication gaps.
However, Tsavo brings unique insights to the discussion by exploring the potential of AI to make these processes more efficient. He suggests that AI can prompt developers to reconsider their actions and verify the existence of reusable APIs or other documentation:
“It’s at that moment where you’re looking at a dock or doing a code review, or looking at a web page, where we think, ‘Can you have a feed that is smart enough to know what you’ve been doing recently, what others have been doing recently? And then surface that document you’ve been talking about – or say, ‘Hey, Sam worked on something similar two days ago.'”
– Tsavo Knott, Co-founder and CEO of Pieces
Identifying Opportunities for Copilots in Developer Workflows
Tsavo outlines his viewpoint on the role of data and AI in software development. He highlights several critical elements for this:
- Code suggestions and automation: The traditional roles that require developers to perform highly repetitive tasks are gradually being automated, allowing for more efficiency and scalability in the development process.
- Personal assistants for developer workflows: Tsavo envisions a future where developers have personal AI-driven assistants. These virtual assistants will interact with developers, offering guidance, recommendations and code corrections. These digital mentors will assist developers in code quality, design, adherence to business requirements and eliminating redundancy.
- Focus on Complex Problem Solving: Tsavo asserts that developers’ core responsibilities have always revolved around addressing challenging issues and making critical decisions. With the integration of AI, routine tasks are streamlined and automated, giving developers more time and mental capacity to handle complex, high-level questions.
Jason emphasizes the necessity of reducing ‘context switching’ for developers. He highlights the emerging trend of developers shifting from primarily code writing to more code review and quality assurance. “We’re trying to allow developers to connect more dots as opposed to focusing on a single dot,” as he describes the strategy.
Echoing Tsavo’s copilot vision, Jason envisions the concept of a virtual assistant that resides in a developer’s pocket, providing context, insights and connections between relevant information. This assistant aids developers in understanding the broader context of their work and facilitates smarter, more informed decision-making.
Both Tsavo and Jason are aligned in their anticipation of how AI and advanced tools enhance individuals’ abilities, regardless of their stage or experience in the field of software development. They acknowledge the empowerment these tools provide to individual developers, making them more effective in their work.
Importance of Managers With High Emotional Intelligence
Towards the end of their conversation, Jason espouses the critical role that managers with high emotional intelligence play as companies scale. He highlights the importance of managers in understanding how individuals work and collaborate effectively.
“As you scale out, the effectiveness of the company really comes down to who can assemble those Navy SEALs and Navy SEAL teams on a mission basis,” Tsavo contributes. Throughout, he emphasizes the need for managers to have deep insights into team dynamics and individual work in progress.
Tsavo further builds upon the idea of AI-facilitated dynamic teams by acknowledging that emerging tools and AI systems have the potential to enable the formation of active, specialized teams for specific projects. This idea reinforces the notion that AI can be used not only to assist individual developers but also to allocate teams behind projects as the need arises.