A Five-Step Enterprise AI Strategy
Tang explained how enterprises can leverage AI and laid out a step-by-step process to integrate AI in your organization. He also offered some handy tips and resources to ensure that your implementation is a success.
1. Get Familiar With AI
Take the time to become familiar with what modern AI can do. The accelerator offers its startups a wide array of resources through its partnerships with organizations such as Stanford University and corporations in the AI space. You should also take advantage of the wealth of online information and resources available to familiarize yourself with the basic concepts of AI. Tang recommends some of the remote workshops and online courses offered by organizations such as Udacity as easy ways to get started with AI and to increase your knowledge of areas such as ML and predictive analytics within your organization.
2. Identify the Problems You Want AI to Solve
Once you’re up to speed on the basics, then the next step for any business is to begin exploring different ideas. Think about how you can add AI capabilities to your existing products and services. More importantly, your company should have goals in mind of specific use cases in which AI could solve business problems or provide demonstrable value.
“When we’re working with a company, we start with an overview of their key technology programs and problems. We want to be able to show them how natural language processing, image recognition, ML, etc. fit into those products, usually with a workshop of some sort with the management of the company,” explained Tang. “The specifics always vary by industry. For example, if the company does video surveillance, they can capture a lot of value by adding ML to that process.”
3. Prioritize Concrete Value
Next, you need to assess the potential business and financial value of the various possible AI implementations you’ve identified. It’s easy to get lost in “pie in the sky” AI discussions but Tang stressed the importance of tying your initiatives directly to business value.
“To prioritize, look at the dimensions of potential and feasibility and put them into a 2×2 matrix,” said Tang. “This should help you prioritize based on near-term visibility and knowing what the financial value is for the company. For this step, you usually need ownership and recognition from managers and top-level executives.
4. Acknowledge the Internal Capability Gap
There’s a stark difference between what you want to accomplish and what you have the organizational ability to actually achieve within a given time frame. Tang said a business should know what it’s capable of and what it’s not from a technological and business process perspective before launching into a full-blown AI implementation.
“Sometimes this can take a long time to do,” said Tang. “There is an opportunity with AI to change the innovation and strategy part of the equation but, if they don’t have a well-established process already, it doesn’t make sense to do that for the company. Addressing your internal capability gap means identifying what you need to acquire and any processes that need to be internally evolved before you get going. Depending on the business, there may be existing projects or teams that can help do this organically for certain business units.”
5. Bring in Experts and Set Up a Pilot Project
Once your business is ready from an organizational and technological standpoint, then it’s time to start building and integrating. Tang said the most important factors here are to start small, have project goals in mind, and, most importantly, be aware of what you know and what you don’t know about AI. This is where bringing in outside experts or AI consultants can be invaluable.
“You don’t need a lot of time for a first project; usually for a pilot project, 2-3 months is a good range,” said Tang. “You want to bring internal and external people together in a small team, maybe 4-5 people, and that tighter time frame will keep the team focused on straightforward goals. After the pilot is completed, you should be able to decide what the longer term, more elaborate project will be and whether the value proposition makes sense for your business. It’s also important that expertise from both sides—the people who know about the business and the people who know about AI—is merged on your pilot project team.”