As artificial intelligence begins to overhaul industries and change the way consumers engaged with businesses, companies large and small are looking to leverage AI to develop new products and services for the market.
It’s happening across sectors, and with companies of all sizes:
- Established enterprises are looking to improve their customer experience with new AI applications
- Software companies are looking to layer AI into their products to improve their functionality and compete in the market
- Consulting businesses are looking to “productize” their knowledge assets (or help their clients develop AI products)
- Smaller companies and startups are looking to develop AI products to leapfrog their larger competitors
But how can leaders predict the response of their users, and pick AI initiatives that both satisfy customers and win in the marketplace?
Most have no idea.
Some leaders get lucky, most lose money and time on fruitless AI projects, and only a few have a process the help ensure their success.
The Right AI Product Will Disrupt a Sector – but Most Products Won’t Come Close
There’s no doubt that AI will change the way companies compete in the market, and the way that customers interact with brands.
While there a number of factors that contribute to an AI product’s success, there is one defining commonality among failures:
- They were thinking only of incremental improvement
Most AI products are treated as tools. A new IT offering to build up and present to customers or users.
But AI doesn’t operate like that.
For an AI product to win the hearts of customers and a greater market share – it must have a focus on both (a) near-term value creation for customers, and (b) a long-term competitive advantage, an “expanding moat” of data and value for the company developing it.
The good news is that it generally doesn’t take more data scientists to think long-term, it takes the right strategic perspective from business leadership, and a structured process for finding the most high-ROI AI product ideas.
Business leaders, innovation leaders, and consultants who are deciding on AI product development projects want to:
- Ensure that they focus on only the most promising AI product development ideas (for both long-term and near-term ROI)
- Use their existing core competencies (experiences, process, IP) to help jumpstart their competitive edge with AI
- Quickly assess the viability of AI product ideas to sift through to the few are both accessible and promising
And that’s exactly what the new AI Product Development Roadmap report provides.
Learn from the Companies and Experts Who Understand AI Product Development Best
Most leaders don’t have a structured process to find and develop the most promising AI product ideas because (a) they simply haven’t had the chance to try, and (b) they aren’t familiar with the common factors of success and failure across dozens of AI products and initiatives.
AI is new to many sectors, and most pilot projects are barely getting started – so actual experience with reliably rolling out AI is a skill set held by a limited few categories of experts around the world.
Our AI Product Development Roadmap report draws from over 40 interviews with business leaders from across sectors and geo-regions.
- Established Enterprise AI Products: Whether it’s a bank launching a new chatbot for their retail banking customers, or an eCommerce store creating new recommendation engine – our research leads us to examine AI initiatives that succeed and fail, and determine the pitfalls and best practices.
- B2B AI Vendor Products: From established, multibillion-dollar software companies to venture-backed startups, tech vendor companies are releasing AI products (and “layering” AI onto their existing products) at an ever-increasing speed. Some releases win more customers and help grow the business, others are a waste of time.
- World-Class AI Consultants: The few consultants with robust AI experience in the enterprise will tell you directly: Most AI products fail and never have a chance of generating ROI. Why is this? What do the few successful applications have in common? That’s what we found out.
The objective of our AI product development research was to distill the strategies and frameworks that allow smart companies to give themselves the best chance of getting great results.
If you want to find near-term opportunities for AI to add value to your existing products (or to launch a new AI product altogether) – you’ll find this report immensely valuable.
If you want to ensure that your AI product is aligned to long-term competitive advantage in your market – and you want a set of simple frameworks to use in order to arrive at high-ROI ideas – then this is the right guide for your needs.
A Structured Guide to Developing an AI Product That Wins in the Marketplace
This report provides a set of frameworks to set up an AI product development effort for success – establishing factors for both near-term and long-term success.
Our readers are business leaders, innovation and strategy leaders, and cutting-edge consultants who want maximum business impact with minimum fluff – and that’s how this report is structured. Short, succinct, with actionable insights on every page.
This compact 25-page report is broken down into three key chapters:
- Product Development – Winning in the Near-Term and Long-Term: Discover the “thought experiment” questions that executive teams can use to narrow down high-value AI product ideas, and to find ideas that have near-term promise and maximum long-term strategic value.
- Scoring the AI Product Development Landscape: A simple framework for sorting and organizing potential AI product ideas across five unique criteria – allowing leaders to simply boil down the ideas that leverage existing strengths and take advantage of gaps in the market.
- Building Your Product Development Roadmap: Customized advice for firms of all sizes – giving leaders the tools they need to take the most promising AI product ideas and put them into action.
Purchase your copy of the AI Product Development Roadmap.