Artificial intelligence is hot, but also daunting. The latest advances — known variously as cognitive computing, machine learning, and deep learning — sound complicated and expensive. And they are, despite the enormous potential they bring to the marketplace. For many companies, the price tag and the commitment of resources are too high a hurdle. And for other reasons, even a giant like Apple is facing challenges.

But the good news is that the early dividends from AI are already within reach of most midsize companies as they look for ways to expand their digital boundaries. In fact, the building blocks of AI can produce great results with fewer technical requirements and less time and money than many companies realize. What’s more, those that take this initial step are getting a leg up on AI’s future, since that step is going to be a prerequisite for everything that follows.

First, let’s get our bearings.

At the high end of artificial intelligence are systems like cognitive computing that are allowing driverless cars and other machines to develop the capacity to learn from their experiences in the world — by incorporating nuances, remembering outcomes, and adapting to mistakes. (A recent accident involving a Tesla “autonomous car” has raised questions about AI’s current limits.)

At the less-expensive end is a knowledge-based approach that organizes data and language into highly malleable and helpful blocks of information. These “AI Lite” systems don’t learn new tricks — unless their human minders use new code instructions to “teach” them. But they can become very smart indeed about sorting and distributing their information in extremely fast ways.

What follows are some guideposts to help you put AI Lite to work.

Find the right places to use it. Even in this new information age, not everything requires the razzle-dazzle of AI. But companies and government agencies are starting to find plenty of places where knowledge-based tools can make a huge difference. These include improving data-mining operations, helping with training, and making structured, repeatable tasks and processes far more efficient and less costly. And they are finding the tools increasingly useful, of course, in dealing with online customers.

For example, Allstate Business Insurance, a division of Allstate Insurance, used the tools to develop a virtual assistant known as ABIe (pronounced “Abby”) to answer questions from its 12,000 agents. It was a bit like hiring Apple’s Siri at a sliver of the cost. Mike Barton, the division’s president, put it this way: “We think of ABIe as our precursor to cognitive computing on a shoestring.”

When the company upgraded its commercial insurance line for small businesses a few years ago, the agents jammed internal call centers with questions about how the policies worked and how to set sales quotes. The cost of simply expanding the call centers was prohibitive.

Enter ABIe (shorthand for the Allstate Business Insurance Expert), which my firm helped develop. Employing a rigorous approach to the words and phrases at the heart of the company’s products, ABIe’s avatar-driven interface offers accurate answers to policy questions while streamlining the quote process. From just a few thousand queries a month in 2013, ABIe now handles 100,000 — from all of the company’s employees, and not just from agents. A new version of ABIe will soon be taking queries directly from the customers. Best of all, ABIe paid for itself the first year, so almost all of the ongoing savings now drop to the bottom line.

Roll up your sleeves. AI Lite is far less complicated and less expensive than the high end of the spectrum, but that doesn’t mean it is off the shelf. One size does not fit all — and there are no plug-and-play magic bullets.

For example, it took nearly a year to design, build, and implement ABIe. Allstate Business assigned a team of managers and subject-matter experts to the project to figure out the “taxonomy” of the words, phrases, and data that ABIe would require to answer all of those questions. ABIe pulls its answers from a data warehouse, where all the knowledge relating to the company’s products and processes has been organized. In AI speak, that meant all of the company’s numbers, charts, words, and phrases had to be chopped, chunked, tagged, and HR-optimized to give ABIe the ingredients for those answers.

In short, companies that want to get into this game will have to roll up their sleeves and do some old-fashioned blocking and tackling. But the prize is worth pursuing: If the right data is married to the right vocabulary and terminology, a company’s information capabilities will soar.

Don’t expect everything to be perfect. Given all the moving parts, however, mistakes, and the need for midcourse corrections, are inevitable, so prepare for them. In ABIe’s case, the team started out by offering some answers that were far too thorough, essentially telling the questioner how to build a clock when all that was sought was the correct time. It took trial and error — including detailed debriefs of those using ABIe — to arrive at very specific and actionable answers and to put in place the governance, metrics, and change-management processes to make controlled, methodical updates.

Don’t make your AI too lite. When using AI to interact with online customers, keep in mind that piecemeal approaches won’t work. Most organizations are deploying department-level solutions and standalone tools without sufficient funding. The results are typically inconsistent and haphazard, forcing time-consuming and costly fixes.

One department at a company, for example, may see the customer through a transactional lens, while another puts the emphasis on promotions. The differences in their data models will slow, and perhaps hobble, the entire program.

Getting your money’s worth from AI — whether at the knowledge-based end of the spectrum or later on, with more extensive, and expensive, applications — requires three things. The effort must involve careful analysis and preparation, which takes into account each department but keeps the focus on the full enterprise. It must have a formal (and nuanced) governance structure. And someone at the most senior level of the company must sponsor it.

Without those foundational elements in place, you will fall short in deploying the incremental power of AI Lite and be ill prepared for the revolutionary changes that AI promises to bring.