Or at least that’s how it feels when it comes to harnessing artificial intelligence for your business.
The reality is that businesses across all industries span the full spectrum of AI strategy, with some fully executing on multiple integrated fronts, while others are virtually figuring out what to do with these new technologies, whether they’re GPT-4 or model-based in machine learning.
There’s good reason for all the action around artificial intelligence. Investments in artificial intelligence have increased steadily and is likely to become part of the DNA of most industries. Soon it will be tables to stay competitive in most areas. So getting the AI strategy right — and soon — is crucial.
But it’s understandably hard to know where to start from. Or even if you’ve started down the right path. To help business leaders think through their nascent or established AI strategy, I’ve broken it down into three key steps, as outlined here. Following these will ensure you’re covering the right bases with AI and setting your business up for success.
Step 1: Know your data (“feed the beast”)
It all starts with data. No data, no effective AI-based strategy or tactics.
As the parenthetical mantra in the section title suggests, you need to feed the beast that is AI, and data is the fodder of choice. This requires a critical first step in knowing what data your company produces or can access and what will be most useful for AI applications.
AI uses a wide range of data: numbers, text, video/images and more. Your internal data sources may include email, IM, Slack, Salesforce, video, audio, sensor data, desktop research/survey, customer calls, and the like. Public data sources, meanwhile, include web, TV, print, consumer, digital and other databases. So start by gaining a thorough understanding of the data you have or can obtain, what would be most useful in addressing key questions related to your business strategy, and how to begin storing, managing, curating and using the data to receive in informative answers.
But here’s the catch: you need data unique to your business; Applying AI to online data gives you the same general answers that all your competitors can access. Internal data is the source of competitive advantage when used alone or in combination with public data.
Financial firms, for example, have access to vast amounts of public data from government reports, specialized databases such as Compustat, and sources such as Bloomberg IM. But everyone can access them depending on their budget. Fortunately, these businesses can also pull business-specific data from themselves to use alone or alongside public data. It might be internal communication data that they can analyze to understand which portfolio managers work best together, or who on the trading floor is the first to notice a pivotal shift in the market.
Likewise, a hedge fund will have a relatively unique portfolio of data that illuminates how embedded they are in different trading strategies, along with specialized knowledge of business lines and historical data on the performance of those portfolios. This data can be fed into a machine to find hidden performance patterns and company-specific capabilities. AI could be trained to identify market conditions related to overholding a stock, buy and sell strategies for specific portfolios, or early warning signs that a trader is not right for the company or may be the the company’s next trading star. Given the power of such analytics, it’s never too early or too late to start storing and caring for your data.
Step 2: Train to lead (“lead with fire”)
Everyone agrees that AI can make jobs faster, cheaper and better, whether it’s marketing, manufacturing or logistics. But the most amazing returns come from innovative thinking.
Consider fire: Fire solved critical problems (warmth, protection, lighting) before humans understood the chemical properties of fire because our ancient ancestors creatively figured out how to use it. AI technicians are trained to apply complex analytics, but not to know which analytics would be most valuable in the first place. Therefore, business leaders must be strategic thinkers who guide AI technology experts, even though the leaders themselves may not understand the inner workings of AI.
What does this mean for you? Commit to learning about how AI works (and doesn’t) and what it can (and can’t) do in your business and industry. There are many ways to gain knowledge: universities, consultants, industry alliances and others. Once you do, it will be simpler to hire and train a team of AI experts to manage the application.
At a large healthcare organization, management instituted an innovative “reverse mentoring” program. Top leaders (including the CEO) were paired with AI technicians (many years their junior) who opened their eyes to the technology’s many applications for classification, prediction, recommendations, sentiment analysis, document discovery, and more. Ultimately, senior management gained the ability to share ideas and language that made AI strategy discussions more effective and efficient, creating a virtuous cycle driven by situationally aware strategic thinking.
Step 3: Create your AI strategy (“be careful and comprehensive”)
With Steps 1 and 2 completed or in progress, you can bring them all together in your AI strategy. This usually happens through a combination of internal discussions, consultants and critics of AI applications.
Central to your AI strategy is this question: How can AI enhance your success and overcome your weaknesses? To answer this, understand that strategic AI works on three main types of problems:
Use these ideas to get your team thinking about how to use AI and the right data to enable creative and innovative directions for your business, while recognizing the limitations, shortcomings and expected trends of technologies related to regulations and ethics. The more you can learn about AI and its applications, the better you will be able to use these breakthrough technologies to drive profitable growth or reduce risk.
*
This article originally appeared on Forbes.