Ecosystem Dynamics in the GenAI Era: Transforming Digital Product Development

In digital product development, generative AI (GenAI) is not just another technological trend—it’s reshaping how teams design, build, and scale digital products. While the potential for AI to automate tasks and redefine roles has been widely discussed, GenAI’s broader impact on the dynamics of cross-functional product teams often flies under the radar.

But in this new era, GenAI tools are not simply enhancing individual productivity—they are fundamentally altering how teams collaborate, innovate, and deliver value.

This article delves into how GenAI is lowering barriers, enabling greater participation across the product development lifecycle and blurring the lines between traditional roles. We’ll explore why product leaders must approach the integration of GenAI not just as a tool adoption, but as a holistic transformation—impacting everything from use case selection and team upskilling to strategic adjustments in roadmaps and hiring.

By understanding the ecosystem dynamics at play, product leaders can use GenAI to drive innovation—without compromising the integrity of their teams, processes, or products.

Product Teams Are Highly Interconnected

As GenAI continues to advance and see widespread adoption across industries, discussions often center around its impact on individual jobs and the automation of selected tasks: What jobs will be replaced by AI? What tasks will soon be done by computers? How do we upskill ourselves for the new hyper-digital age? 

Less frequently, we discuss the impact of GenAI on closely connected, cross-functional teams—exemplified by digital product teams. These teams—consisting of product, design, and engineering (at minimum)—are fundamentally interdisciplinary.

Designers (UX/UI, product design, and user research) are often the most creative members, collaborating with product managers to identify latent user needs, bringing concept visions to life, and handing over visual wireframes and prototypes to engineering. Product managers (PMs) then work alongside engineers to ensure feature requirements are well-defined, minimizing wasted effort in the coding process. Although there is a brief period when PMs work independently to hand over requirements from design to engineering, agile software development practices often necessitate iteration of requirements and priorities mid-flight.

In such a fast-moving, interconnected environment, an excellent PM communicates effectively with their team, enabling design and engineering to operate efficiently despite shifting feature needs. Product managers are also responsible for driving overall team processes and establishing best practices, including tool selection for software delivery.

When considering GenAI's impact on digital product teams, product managers are uniquely positioned to lead change management. Their dual role—managing team dynamics and overseeing functional processes—makes them well-suited to guide their teams through this transformation.

GenAI Tools Support Every Step of the Product Lifecycle

A new wave of GenAI tools is already emerging with rapid industry adoption. They span design, delivery, and product along the entire 10-step product development lifecycle—from ideation all the way through evolution or sunsetting.

However, these tools do not equally support all stages of the product development process. The quality of the outcomes and maturity of the GenAI solutions vary significantly depending on the task at hand.

As proven in a hallmark study by the BCG Henderson Institute, GPT-4 is highly effective in tasks requiring creative ideation but ineffective in tasks requiring business analysis. As an extension of this theory, it stands to reason that the GenAI solution landscape is:

• more mature in stages involving text generation (e.g., ideation, market research, development, testing)
• somewhat mature in stages involving multimedia generation (e.g., design, growth, concept definition)
• less mature in stages requiring strategic analysis (e.g., MVP definition, GTM, evolution and sunsetting)

For now, GenAI is not replacing any roles or functions outright. Still, it has “lowered the floor” for product teams and stakeholders to participate in more creative parts of the product development process (where the technology is more mature).

Before GenAI adoption, product managers sometimes played a “support role” to creative designers involved in concept ideation and divergent thinking. That is no longer the case—in the same BCG Henderson Institute study, a staggering 90% of participants improved their performance when using GPT for creative ideation. Participants actually did best when they did not attempt to edit or tweak GPT-4’s output, suggesting that creative, text-based GenAI use cases require less supervision than analytical use cases. For tasks requiring text-based creativity (such as copy generation), using GenAI tools inside the product development lifecycle further blurs the overlapping responsibilities within product teams.

The benefits of GenAI for product teams are clear. As such, the real question isn't whether or not to introduce GenAI into product organizations—it’s how to do so effectively while maximizing the benefits and avoiding potential drawbacks.

New GenAI Tools Trigger Cascading Effects

Introducing new GenAI tools into a team can create cascading effects throughout the product development team ecosystem. These effects begin with the tool’s integration, which can significantly alter how work is done, the roles involved, and even the overall product development lifecycle. Here’s how the process unfolds:

Step 1: Tool Introduction

When introducing a new tool into a team ecosystem, there are typically two approaches:

• Adopting an off-the-shelf SaaS solution: This option involves buying pre-built software that supports a range of steps and use cases, providing a quick and relatively straightforward way to integrate new technology.
• Building a custom solution: This approach entails developing a bespoke tool based on an enterprise large language model (LLM) tailored to specific needs and use cases.

Although adopting an off-the-shelf SaaS tool might seem like the safer bet, the “Non Adoption Paradox” looms large. This paradox describes the quandary where “despite high optimism around the [AI] tools, key factors hinder scaled adoption, including technical limitations, learning curve challenges, and cultural resistance.” For instance, only 30% of software firms have deployed GenAI copilots, and among these, 75% report that fewer than half of their developers actively use the tools.

Given these challenges, a custom-built solution can sometimes present a lower-risk alternative for a product team within a larger organization. Custom solutions offer the advantage of being specifically designed to address the team's unique needs, potentially overcoming some of the adoption hurdles faced by off-the-shelf tools.

Regardless of the chosen path, achieving widespread GenAI adoption requires significant resources and strategic planning—and product managers play a critical role in this process. Their formal and informal influence within the team—along with their deep involvement in team processes—enable them to effectively prioritize use cases and manage the implementation effort.

Step 2: People Impact

Once a GenAI tool is deployed and achieves a certain level of adoption within a product team, it affects team members in several ways. For instance, individual productivity often increases, with BCG analysis indicating that team members can gain up to 5 additional hours of productive time per week.

The introduction of GenAI tools can also blur traditional role boundaries. For example, product managers may take on more creative design tasks, and team members shift from being “creators” who produce solution artifacts to “curators” who oversee AI-generated outputs. This shift highlights the growing need for new skills to fully leverage these tools. However, only 44% of leaders and 14% of juniors have received AI training within their organizations, suggesting that not enough is being done to upskill entry-level digital product team members compared to CPOs and CTOs.

It’s important to recognize that this blurring of boundaries isn’t always seen positively. In the upcoming UIzard case study, for example, you’ll see how product managers taking on design tasks using GenAI was met with resistance from team members with design expertise.

Step 3: Process Evolution

As designers, product managers, and engineers fully integrate GenAI tools into their roles, these new tools drive changes both within individual phases of product development and across the entire process.

Within each phase of the 10-step product development lifecycle, GenAI tools help remove barriers to creativity. With more individuals feeling empowered to contribute beyond their traditional scope of work, there is an influx of new ideas and inputs. Additionally, GenAI automates repetitive tasks, such as drafting product requirements and bug reports, which previously consumed significant time. This automation allows product managers to focus more on strategic tasks and leverage data-driven insights, thanks to GenAI's ability to process and analyze semi-structured data.

Even more fascinating is the prospect of the 10-step lifecycle evolving into a shorter but “fatter” process, with multiple steps happening at the same time. For example, development and testing could occur simultaneously, with a product manager overseeing both activities. Advancements in fully autonomous agents, facilitated by frameworks such as BCG X’s Agent Kit, suggest a future where product managers might even be able to provide one nuanced initial prompt and then let AI handle multiple stages of the workflow simultaneously, reducing the need for step-by-step AI supervision.

Real-World Case Study: Proof-of-Concepts with UIzard

To explore the potential of GenAI tools for product managers, a team at BCG X conducted an experiment using UIzard, a GenAI-powered wireframing tool. The hypothesis was that GenAI could help product managers and their teams quickly create proof-of-concept (PoC) wireframes, enhancing efficiency in early product strategy phases and delivering faster results.

The experiment involved six individuals—3 product managers and 3 strategic designers (not UI designers)—tasked with creating 3 to 5 screens for early-stage PoC case studies across B2B and B2C products. The goal was to complete this work in 2-3 hours.

While the hypothesis was ultimately proven (with all prototypes passing the acceptance criteria), the experiment generated numerous insights into barriers and enablers for adoption.

Key findings from the experiment were as follows:

• Speed at the expense of quality: UIzard’s rapid output came at the cost of limited control over design decisions and depth of thought—a trade-off acceptable only in certain phases of product design and development.
• Manual work remains significant: Even when using GenAI tools, the majority of time is still spent on manual tasks. In the experiment, participants said they spent 20% of their time using GenAI tools and 80% on manual effort.
• Complementary tool use is essential: UIzard provided the most value when individuals used it with other complementary, mature tools. For example, testers were happiest with UIzard when they combined it with ChatGPT or Unsplash to complete their workflows.
• Greater benefit for non-designers: Product managers experienced significant time savings with UIzard—about three times more efficient than Figma—and expressed a strong likelihood of continued use (8.5/10). In contrast, strategic designers reported no improvement in efficiency and a lower likelihood of future use (4/10).
• Low barriers, mixed reactions: Low barriers to entry (to start effectively using the tool) are not always seen positively. Curiously, while UIzard’s ease of use was noted, it was not universally appreciated. For highly trained designers accustomed to advanced tools, UIzard’s relative lack of professional design features reinforced the tool’s limitations and the sunk costs of developing their existing skills.

Five Steps to Integrate GenAI in Product Teams

Corporate leadership is 1.5 times more optimistic about GenAI’s potential impact than junior team members. This places the responsibility on product leaders—chief product and digital officers, directors of transformation, and heads of product—to channel this optimism into action.

Here are five recommendations to help product leaders effectively integrate GenAI into their organizations:

1. Assess holistically
   
   1. Identify use cases where GenAI can deliver the most value and prioritize those that align with your product and organizational strategy.
   2. Evaluate how GenAI integrates with your current tools and technology stack, considering the ripple effects on roles, responsibilities, and processes.
2. Drive adoption 
   
   1. Invest in training to ensure all team members are proficient with GenAI tools, enabling them to fully leverage the technology.
   2. Foster a culture of continuous learning, encouraging a shift in mindset from being mere “creators” to “curators” who can effectively harness AI outputs.
3. Adjust team structure and roadmaps
   
   1. Focus on developing skills within your team that complement GenAI capabilities, such as analytical thinking, creativity, and collaboration.
   2. Update your product development roadmaps to incorporate GenAI tools, reflecting the new efficiencies and capabilities in your delivery timelines.
4. Promote a culture of innovation
   
   1. Clearly articulate a GenAI-inclusive strategy that aligns with your product organization's goals.
   2. Create a safe space for your product teams to experiment with GenAI tools, share use cases, and learn from each other.
5. Monitor and iterate 
   
   1. Measure the impact of GenAI on team productivity, output quality, and collaboration dynamics.
   2. Use these insights to continuously refine your practices, processes, and tools, ensuring you maintain a competitive edge.

Navigating the AI Shift in Product Teams

In technology, as in sports, it is wise to “skate to where the puck is going.” Rather than merely accepting that GenAI will continue to evolve, forward-thinking product managers should anticipate how this technology will impact the product development lifecycle and identify the phases that will be most affected. This proactive approach will allow them to guide their organizations most effectively.

A notable example of technology-driven transformation occurred in 2021 when Facebook rebranded as META and shifted its focus to building the metaverse as the next digital frontier. Although the move sparked controversy, it undeniably aligned internal and external stakeholders with the company's new mission and clarified Mark Zuckerberg's vision. Product leaders can draw inspiration from such bold transformations to develop a GenAI-inclusive strategy for their own organizations.

While product leaders may not need to go as far as rebranding their companies as GenAI-driven entities, understanding the cascading effects of GenAI on tools, people, and processes can help them create the right ecosystem for success and optimize product development.