Guest blog by Joel Semeniuk, Chief Strategy Officer, Communitech

We're going to need to rethink just about everything about innovation, aren't we?

Once upon a time, in the not-so-distant past of the 1990s, a wave of digital transformation washed over the world, forever changing how we work, think and innovate. It was a period marked by the buzz of dial-up internet, clunky websites and the promise of a future where technology was seamlessly woven into the fabric of our lives.

As a university student, I developed a strong interest in the process of innovation. As I progressed into my career, I realized that the only way to unlock true innovation was by bringing together people, processes and technology. Fundamentally, I felt that innovation was a human process – people creating solutions to other people's problems.

Thus, there has been a human at the heart of every innovation.

Until now, perhaps.

“Hello AI World”

What happens when AI is introduced into the innovation process?

It's like having a new team member who is exceptionally talented in… well, everything. Building AI into the innovation process isn’t simply additive; it will be profoundly transformative.

Throughout the digital transformation era, there was a constant effort to enhance the efficiency of producing new digital solutions. The primary focus of most innovation practices was achieving high value, speed and efficiency. Over time, a large number of innovation practices have emerged and some of them have turned into full-blown religions.

AI will change all of this.

We must invent entirely new practices to create AI-enabled solutions by people whom AI enables (or replaces).

How might that play out? Here are a few ideas (not predictions; please don’t hold me to any of this).

AI as a collaborator

Let’s assume that AI will not wholly replace innovation development teams in the short term.

So far, innovation relies on human expertise and skills to create products that meet market needs and solve customer problems. However, with the advent of AI, innovation developers can now leverage clever technology to automate and optimize various aspects of the innovation process.

AI, as a co-collaborator in innovation, is a game-changing idea that promises to transform how we create technology-based solutions. This shift is not just about using AI to handle monotonous or repetitive tasks; it is about fostering a harmonious relationship where AI becomes an essential part of the creative and engineering processes.

AI as a team coach

Imagine an environment where AI is a personalized productivity coach for members of an innovation team, adapting to each member’s unique style, expertise and preference. This AI coach could go beyond offering generic advice and provide tailored guidance, learning resources and real-time feedback specific to the project just in time. 

It could analyze each team member’s contribution patterns, suggest optimizations and recommend new technologies or methodologies suited to their goals. For example, an AI coach could simulate pair programming sessions, offering insights or nudging software developers towards better practices, almost as if a seasoned developer is looking over their shoulder, ready to share their wisdom.

The future of coding careers may not look so bright. 

“Over the course of the last 10 years, 15 years, almost everybody who sits on a stage like this would tell you that it is vital that your children learn computer science. [That] everybody should learn how to program. And in fact, it’s almost exactly the opposite,” Jensen Huang, Nvidia’s founder and CEO says. “It is our job to create computing technology such that nobody has to program and that the programming language is human. Everybody in the world is now a programmer. This is the miracle of artificial intelligence.”

AI as a member of the team

The role of AI extends into collaboration, where it acts not just as a coaching tool but as a team member capable of contributing ideas, generating code (or other assets) and providing insights based on vast datasets complex for humans to consume. This AI collaborator could participate in brainstorming sessions, offering suggestions that combine best practices with creative, out-of-the-box thinking. It could help identify potential pitfalls or opportunities in the project's design phase, drawing on knowledge from various sources and precedents.

In more advanced scenarios, AI could automatically generate prototypes, based on high-level descriptions provided by the innovation team or intended customers. This capability would allow teams to focus on the projects' creative and strategic aspects, leaving AI to handle the heavy lifting of boilerplate code and basic solution goo.

Integrating AI into software development teams could significantly enhance dynamics and productivity. By offloading specific tasks to AI, teams can allocate more time to innovation, problem-solving and high-impact work. AI could also serve as a mediator, synthesizing different viewpoints into cohesive and optimized solutions, thereby enhancing team performance.

Elimination of the innovation team?

As we move into the future, we can expect a shift in how AI-enabled innovation is produced. The traditional approach to innovation has always involved a team of tech experts who develop new ideas and products collaboratively with the customer. It is becoming increasingly likely that tech teams will not be the only ones capable of innovating. Instead, the customers (users, etc) will be empowered by AI to create their solutions.

One could argue that this is already possible with customGPT.

Just like a declarative app allows the user to define what they want to achieve, a customGPT is trained on specific datasets and fine-tuned to address the specific needs of a particular type of task. This makes them highly specialized and efficient in generating text or answering queries related to a particular domain.

As of this post, I have created around 15 customGPTs declaratively without using any code and all of them were done in less than 10 minutes, each serving me in a different way - from helping me brainstorm and work through ideas to helping me make decisions.

The Future of AI in Software Development

As AI technology continues to evolve, its role as coach and collaborator will likely expand, offering even more sophisticated interaction and assistance. We might see AI systems capable of managing projects, coordinating teams and negotiating project scopes with clients. The potential for AI to understand and simulate user feedback could revolutionize testing and quality assurance, making software more user-centric from the outset. These changes won’t be immediate and will likely take time to evolve.

Here is my best guess of what that could look like over the next five years.

What’s happening now (zero to three years):

  • Increased sophistication: AI code generators will likely become more sophisticated, with a better understanding of context and the ability to handle more complex coding tasks.
  • Wider adoption: As these tools become more reliable, their adoption is expected to grow, especially among startups and individual developers.
  • Ethical and quality challenges: There will be a focus on addressing ethical concerns and improving code quality as the technology matures.
  • Integration with development environments: AI code generators will likely be more deeply integrated into development environments, providing more seamless assistance to developers.
  • Advancements in personalization: Tools may offer more personalized code generation based on individual developer styles and project requirements.
  • Addressing bias and misuse: Efforts to mitigate bias in AI-generated code and prevent misuse will become more prominent.

Within five years

  • End-to-end development: AI will be capable of managing entire development processes, from setting up databases to designing interfaces, based on high-level instructions.
  • More significant impact on employment and job roles: While there may be concerns about AI replacing jobs, the demand for AI-literate workers is expected to grow. AI will start to add significant value to the global economy.

Challenges and considerations

While the promise of AI as a coach and collaborator is vast, it also presents challenges. Ethics, privacy and the potential for over-reliance on AI guidance must be addressed. Balancing the benefits of AI assistance with the need to maintain human oversight and creativity will be crucial. For me, solving some of these challenges still seems quite far off.

AI-driven future

As we approach the onset of this AI-driven era, it's evident that the innovation landscape is about to undergo a significant change. Integrating AI into our innovation teams will force us to completely reconsider just about everything involved in the innovation process.

Everything about innovation is about to change.

So much for my 30-year career mastering existing models!