Five Ways AI Elevates Product Innovation

For decades software companies have struggled to build and launch profitable new products. From personal experience, I have seen one reason for bad outcomes to be a communication gap between engineering-led product development and marketing-led go-to-market (GTM) launch teams.

When different functions operate in silos, they may not have a shared understanding of the customer, the market, or the product goals. This can lead to miscommunication, missed opportunities, and ultimately, a product that fails to meet critical unmet customer needs.

Today, businesses can leverage generative AI in a strategic manner to create better products and differentiate themselves in the market. Using AI technology, successful teams will engage all new product constituents in an iterative, cross-functional business strategy dialog.

While AI can provide universal knowledge, the human element—specifically the marketing team—is crucial for integrating this knowledge into a unique and effective strategy. From my own investigation, here are five ways AI enables dialog and elevates innovation.

In a world where everyone is super, no one is super. With generative AI widely available, every company has access to the best practices of every other. Sure, you can deploy AI, but will your company stand apart?  And how will you build a growing business with sustainable profits?

As I wrote in Amplify Human Wisdom recently, businesses can use generative AI to make better products that create new value in the marketplace. Great teams will use AI to sweat every detail and test every aspect of product strategy to carve out a new category.

David Packard said, “Marketing is too important to leave to the marketing department.” Fair enough. Everyone’s in marketing now. Give everyone access to the AI tools, and train them on use. Then put the marketing team in charge of the process that integrates what they discover.

Here’s how it works. Generative AI models collect universal knowledge. They know what you don’t know. Your team must ask the precise questions that expose this knowledge, and those with the best understanding of a particular subject should be the one’s to ask those questions.

Engineers best know how to tease out available technologies. Product managers recognize product-market fit. Sales managers know the segments with optimal revenue potential. Marketing brings all their insight together into a shared strategy that sets your product apart.

That's why I expect the new set of new product winners to come out of teams with an integrated strategic process that leverages generative AI. Over the past few months, I have gone deep into five areas where I expect that use of AI will make a team better and elevate its new product innovation.

1. Iterative, cross-functional product strategy
2. Enhanced data collection and analysis
3. Deep dive topic-specific research
4. Day-to-day team administration
5. Continuous launch planning and content creation

In each of these areas, the addition of AI adds value by either enhancing efficiency, improving decision-making, or both, ultimately leading to more successful product development and launch outcomes.  In each of the sections below, I go into greater detail on this value creation.

Iterative, cross-functional product strategy

An iterative, cross-functional product strategy process improves collaboration on new product initiatives and leads to a shared strategy. Generative AI improves this process by summarizing constituent inputs and objectively identifying weakness relative to industry standards.

Less time spent managing the process frees time to iterate on the strategy and make it stronger. Now, alert team leaders have time to find weakness versus industry competitors. They can also quickly see potential conflicts through the contrast of team member’s perception of the strategy.

Here’s my experience with strategy.  Ultimately, the goal of the new product is to satisfy critical unmet need. For a successful launch, you must have a product that uniquely satisfies the need, an unfair advantage to hold your position, a business model that pays, and an ability to execute.

In my practice at TruNorth Consulting, I introduced the GAUGE Services Platform to implement such an iterative process for emerging companies. Elicitation with fifteen prompts collects team knowledge of strategy from 10-20 constituents. A five factor framework captures the input.

Before generative AI, I spent considerable time sorting through the results to get the shared strategy for the team to discuss. Now, I can summarize, compare and contrast the inputs with a single prompt. There is even a Python library to access the OpenAI APIs, and run it batch.

What’s the takeaway?  Less time spent on the grunt work of natural language comparisons means more time available for team leaders like me to add their wisdom. That leads to much faster iteration on the shared strategy, and much faster testing for potential success or failure.

Enhanced data collection and analysis

Generative AI can enable efficient and effective data collection and analysis for data-driven decisions. Within the iterative strategy process discussed in this article, it shows up as an idea generator for the strategy framework. It’s like an automated intern who brings the top-line news.

Overall, better data leads to better choices in new product strategy, which leads to greater likelihood of launch success.  Better data is more accurate, timely, comprehensive, relevant, accessible actionable and reliable. Does generative AI replace traditional methods here?

Traditional methods of data analysis involve the collection and transformation of data, which may then be stored in a data warehouse that can combine both public and private information. These methods enable greater system complexity, security, scalability and customization.

By contrast, generative AI excels at in tasks related to natural language understanding and generation, such as answering questions, generating text, and summarizing content. It has access to a global knowledge base, so it knows what you don’t know. Here are two use cases.

Automated Intern - Standard prompts search the global knowledge base for industry-specific strategies, competitive analysis, market trends, segment definitions and sizes. Let the AI tools summarize the big picture as expressed by thought leaders and industry participants.

Idea Generation - During strategy audits and deep dive research, the team uncovers many disparate data points. As strategy becomes less well understood, use generative AI as a creative catalyst that brings in knowledge from the outside to spark new ways of thinking.

Additionally, AI tools can help build the data warehouse. Not explicitly in service of this area, I found Chat GPT quite helpful as a coding co-pilot. That’s an article for another day though.

Deep dive topic-specific research

Generative AI acts like a guide by your side as you conduct deep-dive research into critical points of strategy like customer needs, market trends and competitor analysis. It fills knowledge gaps and provides actionable insights. You don’t know what you don’t know until you ask the AI.

The integrated strategy process discussed in this article exposes such knowledge gaps. With each iteration, reading summaries of contrasting opinions highlight obvious issues. Big red flags can pop up in all aspects of strategy, but I often see them first in the market opportunity.

Recently, I had to dive deep on a project management software segment. Using the language of the team, I begin to explore the core concepts that they had expressed as their strategy. Using prompts, I went deep into the language of the problem space. Clues appeared. I set them aside.

First, I used a string of prompts to confirm unmet needs.

Where are people talking about <our specific project management gap>?

Now, within those sources, find me the posts with <certain keywords that match the value we intend to deliver>.

Scrape and analyze the contents of those posts.

This was repeated for other key strategy sections that didn’t feel complete. In each unique case, prompts extracted clues and meaningful bits from the content available. It’s not possible to go into complete detail here, but prompting is where the product leaders differentiate themselves.

Repeat this process for every team member, with unique functional experience and knowledge. Empower them with the tools to dive deep into a problem they know best like technical advantage, product cost analysis, outbound sales plans.

Now, the summaries of these data points get plugged back into the overall strategy.  This new knowledge quickly filters back into the consciousness of the team, who make their own steps forward. AI accelerates this process of the team, exposing it’s best opportunity in real time.

Day-to-day team administration

Generative AI will likely change the focus of company administrators to strategy. Day-to-day finance, HR, project management functions will then follow standard industry best practices. One might then expect ad hoc decision making to be left to the product team.

The real word example of HR at IBM makes this point. This huge company was for years significantly better at this necessary buy not differentiating aspect of business. Twenty years ago, IBM was so good at HR that it outsourced its process to P&G in a $400m contract.

But three months ago, IBM CEO Arvind Krishna revealed plans to pause hiring on 7,800 positions that could be replaced by AI (Ars Technica). Looking deeper, we learn that value added tasks like productivity and work composition evaluation will NOT be replaced at this time.

Each company chooses certain activities where it will add value in ways that give it differentiated zcompetitive advantage.  In his book, "Dealing with Darwin: How Great Companies Innovate at Every Phase of Their Evolution," Geoffrey Moore separated these into core and context.

Core activities create competitive advantage. You find them in your “Innovation Zones.”  Contextual activities move the company along, essential but not differentiating. Here you follow industry best practice and spend as little time as possible in the execution.

You can see the problem. As AI gives you greater access to best practices, it gives your competitors the same access. It accelerates the speed at which your hamster wheel of business actvity spins. Before, you might have some slack to be slightly better at your context. Not now.

What does that imply? Innovative teams will have less support staff with time for day-to-day issues. Inside your innovative product team, this could be a bonus. If the team leaders can use the AI to streamline their own functional overhead, the team will likely move faster.

Here’s an example of how that might work. Imagine an older manager in a growth business, dealing with remote workers and early exits. The one person left in HR works on strategy. This manager talks to younger contributors about expectations for internal company advancement.

One day, the manager drops some comment about professionalism at an offsite. Confusion ensues. Perhaps a bit of preparation would have helped create a presentation and a document that set the expectations. Here’s how that might have started in a Chat GPT conversation.

From the point of view of an older manager in a bootstrapped software company, describe professionalism to younger Gen-Z professionals with the potential to rise into leadership in the company. Write as a single page document that sets expectations.

Though highly visible, HR isn’t the only place where a team can benefit from best practices. I found multiple examples in travel, documentation, planning and more.  Remember this, teams will be more and more on their own. Figure out your admin tasks with AI and save time.

Continuous launch planning and content creation

As stated earlier, new products can often fail at the market launch. The product might not match the market. The marketing team might not understand the product. Generative AI enables an outbound marketing process for continuous engagement through product-market discovery.

Building on the iterative strategy, prompts generate messages that communicate new product value clearly, accurately and quickly. At any given point in the product lifecycle, AI tools can generate a best practice campaign that reaches its intended audience.

Before the product is ready for a full launch, you might pitch new value to people who have expressed need in public forums. AI can write direct messages and email content to invite prospects to test the product, answer questions on documentation and collect feedback.

Once the product clearly fits the market, you need a full launch campaign. There is no time to waste as the first company that fully satisfies the need is going to take the market. AI delivers an outline for this campaign, vetted by the team through the integrated strategy process.

Working with templated tools, AI takes the raw strategy statements and combine them into message for various intended audiences. Rather than face the grind of cranking out dozens of different campaign elements, the marketing team focuses on optimization of the messages.

Messages that come straight from the strategy cannot be misinterpreted. Because they have been developed iteratively, they have a higher likelihood of hitting the mark on a larger scale. Please though, allow the marketing team to use the extra time to rewrite and revise. Let it sing.

Conclusion

Supporting product innovation, generative AI adds value in at least five processes of a new product development and market launch initiative. It helps to integrate teams, aligning them in a differentiated strategy that creates new value in the market. This leads to a profitable business.

Look, I know you can save time and money by replacing team members with AI. I simply advocate for a different approach that is more sustainable. Most products fail. Yours will simply fail faster with less money wasted.

Instead, I encourage you to do something amazing.  Use generative Ai to develop a strategy that creates extraordinary new value for the world. Serve the unserved. Satisfy critical unmet needs. With each product you build like this, more money will come to you for the next project.