Commercializing AI Products

Now that we’re in the period of artificial intelligence (AI) integration, we’re seeing many use cases of AI proliferating across industries. In our work managing AI products, we’ve certainly relied on AI consultants and PhD-level advisors to help us with modeling and orchestrating our data strategy to support a full-scale AI operation. However, as the rising tidal wave of AI continues to penetrate various companies and use cases, we’re seeing less of a reliance on breakthroughs achieved through advanced degrees. What’s most important now is familiarity with the use of even simple, reliable models. There’s a time and place for specialization. Data science and AI are massive umbrella terms but, based on my experiences as a product manager, I see a real need for data and AI generalists that can understand the use cases themselves and how they relate to a business perspective.

In the age of AI breakthroughs, we didn...

The professionals – examples of B2B products done right

The professionals are a good place to start with grouping products. AI will gravitate first toward use cases that can be profitable and allow for research and optimization. Because B2B products are products that are made for and used by other businesses, their use case is oriented completely toward the business world. This impacts everything from how they’re marketed to how they’re bought, sold, used, and negotiated. So many B2B products speak to the business impact that a product can satisfy for a customer across multiple levels. It’s a great way to learn potentially helpful applications of AI.

Part of the challenge with the rising supply of AI companies is that they need data to train on. Specifically, one of the ethical challenges with this expansion in data and AI products is being able to offer large amounts of data without leaking information that can identify you as a person, also known as...

The artists – examples of B2C products done right

The artists are here to show us how AI can be leveraged in a way that allows expression for potentially billions of unmet customers. B2C refers to products that are built with the expectation that the product will be bought and used by individual consumers rather than other businesses. With B2C products, there’s a feeling that you’re looking for a way to satisfy the needs and tastes of many individuals. Upon further reflection, B2C companies are looking to satisfy the few common collective needs of millions. The more due diligence that goes into imagining solutions and understanding unmet needs that would help so many people, the more prepared these companies will be to satisfy unmet needs.

Hands down our favorite AI-powered consumer app would be TikTok. The Chinese giant uses three types of AI (https://dev.to/mage_ai/how-does-tiktok-use-machine-learning-5b7i) to optimize its experience for its users:

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The pioneers – examples of blue ocean products

With pioneers, we wanted to focus on companies that were seeking new categories or use cases for their products. These are companies that are finding new paths to create demand for what they offer and these companies create demand for themselves in multiple ways. This is what’s referred to as a blue ocean: a competitive landscape that’s still being formed, that doesn’t see a lot of competition (yet), and that has to advocate for its own demand. It’s relatively straightforward to explore the market in a red ocean because so many pathways have been created. There is already a thriving ecosystem to learn and navigate. But pioneers have to work for this knowledge.

Partially, the companies themselves have to do enough research and development to warrant the pursuit of this industry they’re helping build. Blue oceans also put a lot of onus on the early players in their industries to create thought...

The rebels – examples of red ocean products

Red oceans are markets that have become inhospitable environments with a full, mature, and developed competitive landscape. In this environment, many paths have already been formed and you have to choose one to live in and serve. You still have the option to create something new and you can employ many strategies to beat out the competition and specialize, but you’re challenged with having enough intuition to see what the next step should be. Part of the challenge with having such a diverse and thriving ecosystem of competitors is that it’s hard to know what direction you want to really go in, or which competitors are truly taking a piece of your business.

Lacework, a California-based cloud cybersecurity company, has shown a lot of promise, receiving a collective $1.9 billion in funding, according to Crunchbase. Cybersecurity is a blood bath when it comes to the competitive landscape. Between companies cannibalizing...

The GOAT – examples of differentiated disruptive and dominant strategy products

Now, we’ll turn our attention to the main market strategies and see some of the greatest of all time, or GOAT, examples for each strategy. A market strategy informs your go-to-market team’s efforts. Will you be going after customers that have too many options or not enough? Will you create a product that effectively works better or worse than your competitors? These seem like obvious questions when putting together a business plan, but once things get going for your company and you start getting some customers, suddenly these decisions might not be as concrete as when the company was first formed.

One of our greatest lessons from the start-up world was about getting comfortable with asking questions that seemed so baked into the company mission and ethos that they seemed obvious. We’ve asked these questions reluctantly in previous experiences but we don’t anymore. Companies...

Summary

In this chapter, we covered some promising examples of AI done right in recent years for the purposes of finding inspiration in the successes and use cases of our peers in the space. As a product manager, having an understanding of your go-to-market strategy, business model, and the market you serve is crucial. We are firmly in the era of AI proliferation now. Many businesses are adopting AI within their products and within their businesses, and we hope you’ve enjoyed these examples as we continue to understand the particulars of managing an AI product.

In the next chapter, we will put our futurist hats on and understand a bit about what the emerging trends on the horizon in AI are. We believe all product managers need to have a streak of futurism in their composition. Having a grasp of where things are in the recent and present moments provides a snapshot with which to evaluate your existing strategy. But in tech, things evolve pretty quickly. In business, projections...

References

• AI Helps Duolingo Personalize Language Learning: https://www.wired.com/brandlab/2018/12/ai-helps-duolingo-personalize-language-learning/#:~:text=The%20learning%20behind%20the%20lingo,data%20and%20make%20intelligent%20predictions
• Hazy Company Info: https://www.cbinsights.com/company/anon-ai
• GGWP Company Info: https://www.crunchbase.com/organization/ggwp-65c2
• Lacework Labs: https://www.lacework.com/labs/
• How does TikTok use machine learning?: https://dev.to/mage_ai/how-does-tiktok-use-machine-learning-5b7i
• Canva: https://www.lifeatcanva.com/en
• Lilt Company Info: https://www.crunchbase.com/organization/lilt