We're reaching an inflection point with artificial intelligence (AI). This isn't the AI that pop culture has conditioned us to expect; it's not sentient robots or Skynet or even Tony Stark's Jarvis assistant. This AI plateau is happening under the surface, making our existing technology smarter and unlocking the power of all the data that enterprises collect. What that means: Widespread advancement in machine learning (ML), computer vision, deep learning, and natural language processing (NLP) have made it easier than ever to bake an AI algorithm layer into your software or cloud platform.
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For businesses, practical AI applications can manifest in all sorts of ways, depending on your organizational needs and the business intelligence (BI) insights derived from the data you collect. Enterprises can employ AI for everything from mining social data to driving engagement in customer relationship management (CRM) to optimizing logistics and efficiency when it comes to tracking and managing assets.
A recent survey from Wakefield Research and account-based marketing (ABM) provider Demandbase polled 500 business-to-business (B2B) marketers and found that 80 percent of executives predict that AI will revolutionize marketing by 2020. The catch is, only 10 percent of marketing organizations are currently using AI. The survey pointed to integration (60 percent), training employees (54 percent), difficulty interpreting results (46 percent) and cost of implementation (42 percent) as the top challenges to devising and implementing an enterprise AI strategy.
Techcode's Global AI+ Accelerator helps incubate AI startups but also helps startups to incorporate AI on top of their existing products and services, and offers a consulting service to do the same for other businesses. I spoke to Luke Tang, General Manager of TechCode's Global AI+ Accelerator Program, who explained that Techcode covers AI application across enterprise, industry, and consumer apps. The accelerator sees some clear near-term opportunities for AI, as well as longer-term goals that are still three to five years out.
"Right now, AI is being driven by all the recent progress in ML. There's no one single breakthrough you can point to, but the business value we can extract from ML now is off the charts," said Tang. "From the enterprise point of view, what's happening right now could disrupt some core corporate business processes around coordination and control: scheduling, resource allocation, reporting, etc. These are very time-consuming tasks. Other opportunities on the enterprise side require more creativity and social intelligence that's not addressed by current technology. But we'll see this in the continued progression of AI over the next three to five years or so."
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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.
The following are a number of online resources (free and paid) that you can use to get started:
- Udacity's Intro to AI course and Artificial Intelligence Nanodegree Program
- Stanford University's online lectures: Artificial Intelligence: Principles and Techniques
- edX's online AI course, offered through Columbia University
- Microsoft's open-source Cognitive Toolkit (previously known as CNTK) to help developers train deep learning algorithms
- Google's open-source TensorFlow software library for machine intelligence
- AI Access Foundation's open-source code directory at AIResources.org
- The Association for the Advancement of Artificial Intelligence (AAAI)'s Resources Page
- MonkeyLearn's Gentle Guide to Machine Learning
- Stephen Hawking and Elon Musk's Future of Life Institute
- Join the mailing list for Open.ai, an open industry and academia-wide deep learning initiative
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 2x2 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."