Generative AI for Small Businesses    

My father worked 40+ years in West Monroe as an electrician for the old Brown Paper Mill, which later became Olinkraft. I also worked briefly there as a plant engineer upon my graduation from Louisiana Tech. My involvement with the timber and forest products industry was very limited, though, during the decades that I taught university business management courses. I’ve spent a little time over the past two years studying the industry and the forces affecting it. Below are some of my basic impressions and one particular concern.

The major external forces affecting the overall industry now seem to be global competition, macroeconomic conditions, international trade policies including tariffs, supply chain considerations, the health of related industries such as construction, climate change and sustainability issues, and continuing technological advances. One significant internal industry trend has been increasing consolidation (or larger companies taking over or dominating smaller ones).

This consolidation seems most apparent in the pulp and paper manufacturing sector where larger companies have market power due to economies of scale and capital-intensive infrastructures. The lumber, plywood, and other solid wood products sector as well as the wood products distribution/retail sector have also experienced at least moderate levels of consolidation. The developing engineered wood products market has a more balanced mix of both established larger companies and new and smaller ones.

The Deep South region of Louisiana, east Texas, Arkansas, and Mississippi has many small, often family-run, businesses that play a vital role in supporting the health of the timber and forest products industry. Some of these smaller businesses have struggled to survive, though, for a variety of reasons. 

  

A company’s access to valuable strategic information resources can provide a huge competitive business advantage. Often small business managers associated with the timber and forest products industry don’t have nearly as much access to some forms of strategic information. These smaller business operators may well have key information advantages in their own specific markets, due to their personal knowledge and long experiences with their suppliers, customers, and communities. Where they can be disadvantaged is their relative ability to access, analyze, and respond adequately to 1) critical market-shaping technological, political, and economic information and 2) key competitive shifts in specific global and national markets impacting them.

Traditionally, small business managers have obtained potentially useful information from government reports and statistics and through involvement with industry/trade associations. This information value can pale by comparison, though, to the relevant information additionally obtained by many larger companies through their employment of specialized consultants and staff experts. 

A year ago in the PWJ, I described some of the reported benefits and risks of generative artificial intelligence (Gen AI). The early successes and promises of Gen AI have received enormous public attention in this past year. So have its failures, its possible consequences, and the heavy energy demands currently to maintain AI’s large language learning models.  

According to Rob Thomas, Senior VP of Software and Chief Commercial Officer for IBM, 2024 should be “the” year of AI adoption as many businesses move from their experimentation to their deployment phase. He claims in a recent Fortune magazine that “With the right vision and approach to responsible AI adoption, we will begin to see widespread economic benefits of this technology in the next three years, with many more years of sustained growth and prosperity to come.”

I believe that Gen AI has the potential to narrow the gap between large corporations and small businesses in accessing and using strategic and competitive information. Gen AI seems particularly useful for 1) market research (identifying emerging economic and industry trends, customer preferences, and competitor moves), 2) content creation (generating engaging product and services descriptions, including improvement of company website content), and 3) information analysis and planning (helping managers make better decisions about operations and marketing options).

Some small business managers are already learning and starting to use Gen AI. There are plenty of online sources offering free introductory training videos. A very basic one is an 18-minute video by Henrik Kniberg on YouTube. More depth and breadth of coverage for Gen AI business applications are free or inexpensively available on YouTube and elsewhere.

  

Responsibly investing in a specific Gen AI approach for a small business manager probably requires some limited technical expertise and assistance. This technical assistance should help a few others within the company, including the top manager, learn about AI options and for them to become an AI resource team.

Fortunately, more user-friendly AI interfaces are being introduced for the practical needs of small business managers. An internet search should help these managers identify companies offering possible AI services for small businesses. Enlighten, MeaningCloud, Crayon, Linkfluence, and IBM Watson Assistant are among these firms. There are differences between these and other AI tools in terms of their costs, focus, and breadth of services provided for small businesses.

No doubt, there will be many more books, articles and videos updating AI business advice, like Krista Neher’s recent Newsweek article “7 Keys to Getting Your Organization AI Ready.” These sources can be generally helpful, but very few will explicitly cover AI strategies for smaller, family-run, and industry-specific firms.

I’ll suggest a few basic recommendations for these small companies. You most likely won’t be an early and extensive AI adopter soon. Yet you should 1) have your eyes and ears on how AI is being applied elsewhere in your industry and among your competitors, 2) consider specific company tasks (for possible example, inventory control) where adopting AI tools initially might improve your effectiveness or efficiency, 3) determine which AI tools available then might be a better fit for your unique characteristics and priorities, and 4) look carefully at ROI (return on investment) afterwards on your initial AI choices to plot a more refined AI strategy.

For more info, check books like the one below: