How do you go about commercializing AI? What should you be doing to ensure the success of your venture? The key lies in the four interconnected pillars of successful AI commercialization - advanced innovation ideation and R&D development, corporate strategy integration from the start, business development and sales organization involvement, and an outside-in product manager - initially external, and later in-house. In order to successfully commercialize an AI product or service in today’s market, all of these four pillars must be in place from the outset of your R&D efforts. Let’s break down each one briefly.
Take Domino’s Pizza. It partnered with Ford Motor Company to create self-driving pizza delivery vehicles for Domino’s locations in the US. While many organizations could theoretically benefit from autonomous delivery vehicles (for example, taxi services), Domino’s decided already at the start to strategically partner with Ford because it recognized how the industry was changing due to autonomous vehicle technology.
Before you begin on your path to AI commercialization, you need to make sure that your organizational structure and business processes are ready to handle it. There is no one size fits all model for companies to use when it comes to commercializing their technology. Rather, what works well in one company’s case might be ineffective for another. However, there are some commonalities that you can use as a starting point to build your own structure. Organizations must follow a nonlinear interconnected approach towards the interconnected AI commercialization pillars, as opposed to traditional products. This is important to ensure full buy-in and alignment at every level within your organization, including corporate strategy, business development, and sales & marketing organizations.
The four pillars are Advanced Innovation Ideation & R&D Development; Corporate Strategy Integration from the Start; Business Development & Sales Organization Involvement; and an Outside-In Product Manager – Initially External, and Later In-House. Each pillar has multiple touchpoints that must be addressed with innovative methods, not traditional product management approaches. With these pieces in place, you should have a solid foundation on which to grow your business while focusing on valuable customers and new market opportunities. Let’s break down each one briefly.
Advanced Innovation Ideation & R&D Development
The innovation pillar is essentially your brainstorming and research arm and needs to be a key part of an organization’s overall approach to commercializing AI, especially if you’re attempting to enter a space that doesn’t currently exist. Ideation encompasses everything from defining an exciting problem your product will solve to outlining specific high-level AI use cases (including customer experience).
It also entails R&D development; this is where a lot of companies run into trouble because they think that simply implementing AI and releasing one set of features or tools is going to somehow deliver long-term value. In the case of AI products and services, it’s a customer entrenched iterative process and takes time, effort, and adaptive strategic planning to lead somewhere bigger. This requires patience and long-term thinking from management.
Corporate Strategy Integration From the Start
Corporate strategy integration from day one is perhaps where artificial intelligence in business differs most from other more traditional technologies, and it’s a pillar that will become increasingly important to successful deployment in many different industries. Business leaders that ignore this concept tend to end up with only a small handful of niche machine learning applications and AI projects, rather than transforming their core businesses and achieving a true competitive advantage.
The pillars on which all companies with any kind of AI technology should build include Identifying opportunities for business transformation; Setting explicit goals for how you want to change your company and how you plan to achieve them; Developing plans for how to execute against those goals. In short, don’t expect to be able to commercialize AI unless you’re ready and willing to commit resources across several fronts.
When it comes down to it, effective commercialization isn’t about investing in a specific technology or developing clever marketing campaigns—it’s about having a clear strategy from day one and knowing how to put all the pieces together into an effective whole.
Business Development & Sales Organization Involvement
Business development and sales organization involvement is perhaps a less obvious pillar, but it’s a fundamental piece. If a company is looking to commercialize its AI product, then it needs to ensure that every part of its business is on board with this decision. Not only does this involve getting buy-in from top executives, but it also means communicating your business plan and vision to your sales team so they can support AI adoption. Companies can no longer afford to develop AI capabilities in isolation and expect sales professionals to know how best to position them with customers.
While they’re not generally involved with R&D, Sales and Business Development will likely have a hand in understanding and defining your AI operating model. This means having an outside-in salesperson that works closely with customers to understand needs, as well as asking key questions about how new AI systems might integrate into existing products or services.
These are very important relationships for your company, particularly if you’re releasing a beta version of your new AI tools into already established products. If you sell from within an established organization, make sure your inside sales team is aware of what’s going on and what types of leads may come their way once people hear about your new product. An AI operating model will ensure everyone stays focused on driving revenue. It’s also crucial to keep track of ROI, which is where your business development team comes in—if someone isn’t watching metrics and KPIs it can be easy to get lost in research and lose sight of revenue goals.
Additionally, companies should find ways to invest in these individuals by equipping them with data-driven insights and strategic products or services so they’re able to effectively sell to both current and potential customers on how your technology will benefit them.
An Outside-In Product Manager – Initially External, Later In-House
An outside-in product manager is responsible for AI ideation and development. The role allows teams to benefit from world-class technological advances from applications of AI in business, while still receiving organizational guidance. Without an operational and sales structure, even advanced AI can be rendered useless. As more companies begin to incorporate artificial intelligence technology into their business strategy, effective implementation will become crucial. An outside-in product manager ensures that a company’s day-to-day operations stay fluid, and are not affected by new innovation. The product manager will conduct research through data analysis, customer surveys, and interviews with members of an organization; these resources provide information on a company’s operating model and allow them to decide what tasks they need AI technology for most efficiently.
A good outside-in product manager need not be a top expert in deep learning and advanced machine learning algorithms but will have to have experience with Artificial intelligence and automation, as well as knowledge of current industry trends. They should also have experience leading teams, managing budgets, and working within budget constraints. A key component of an outside-in product manager’s job is communicating effectively; they must be able to explain complex topics to a variety of audiences across multiple levels within an organization in order to ensure that everyone understands how AI can improve efficiency within their business model.
Summary
Artificial intelligence has been around for years. The problem is that it hasn’t scaled quickly enough. Why? Because traditional business functions and business processes are not well equipped to manage AI products, and because AI was developed without an overarching strategy or roadmap.
You need a structure that allows your business development team and product management team to work together with your AI R&D teams, beginning with ideation – how do you get started? What’s on a product roadmap? What are you prioritizing now? We call that operating model an AI operating model, which begins with building a dedicated team of external specialists who can create interfaces between internal AI divisions and external stakeholders, as well as help both companies better understand customer needs.
