Tuesday, October 07, 2025

NEW INC. MAGAZINE COLUMN FROM HOWARD TULLMAN - AND A.I. ENGINE DEMOS

 

How to Start Smart With a Plug and Play AI Model

There is a better approach to AI for businesses that don’t want to shell out millions of dollars to Google or OpenAI to build their own custom version of ChatGPT.

EXPERT OPINION BY HOWARD TULLMAN, GENERAL MANAGING PARTNER, G2T3V AND CHICAGO HIGH TECH INVESTORS @HOWARDTULLMAN1

Oct 7, 2025

It’s hard to say whether there are more columns arguing that, if you haven’t already infused your business processes with AI you may be too late and consequently doomed, or that the vast majority of corporate AI projects to date have been miserable and costly failures. The latter parade of horribles is led right now by recent M.I.T pronouncements asserting that something like 95 percent of the serious large organization efforts have failed.  

There are plenty of reasons offered for the failures of these initiatives, but they sound unsurprisingly similar to the explanations offered when the pace of adoption and implementation of any new tool or technology is slower than the hyped expectations—and when the promised payoffs in savings and enhanced productivity among others fail to materialize immediately. The real wonder is why resistance to change comes as any surprise to anyone at this late date.  

In the case of AI, there is another debilitating and discouraging aspect of the process, which is that even after all the time and analysis and reviews aimed at attempting to determine why some given project hasn’t worked, there’s almost nothing to be gathered or learned because the “black box” that you’re attempting to interrogate and explore is just that—a black box which performs its function in somewhat mysterious and unclear ways that no honest auditor would even pretend to fully understand. 

Needless to say, it’s hard to extract lessons or process improvements in order to achieve better future results when the underlying engine driving the outputs (a) has no memory, (b) doesn’t really “learn,” and (c) doesn’t accept or incorporate input, instructions, and corrections in any consistent or measurable fashion. It just is what it is and you’re offered the opportunity to take it or leave it. This isn’t the most appealing context or foundation to try to build the next phase of your business upon.  

To be clear, the opaqueness and obstinance of the primary AI foundational platforms aren’t even the most pressing concerns for most smaller companies and businesses which are trying to incorporate AI into their organizations. These firms — which is to say the vast majority of all the entities in the world except the mega-corporations — can’t afford to spend their scarce and critical resources on broad AI experiments which may or may not ever come to fruition. Even more importantly, they are facing a much more pressing set of risks and concerns within their own companies presented by the actions of their own people.  

Close to 60 percent of employees are using AI tools not approved by their employers, according to a recent Cybernews survey. Worse yet, more than half admit that their direct supervisors or managers know about their use and don’t object, while another sizeable group reports that their managers don’t care. In fact, the survey found that it was executives and senior managers who were the most likely to be using these tools. But the critical finding from the survey is that around 75 percent of the respondents using unauthorized tools admitted to sharing sensitive data with them. And the companies themselves have no control or even information about which team members are doing what. Once the data enters any of these AI platforms, the user has no control over whether the information is stored, reused or exposed to third parties. 

There is a much better and smarter approach for SMBs and, frankly, for just about any business not willing to shell out millions of dollars to Google or tens of millions of dollars to OpenAI to build their own custom version of ChatGPT. That’s what I call the “plug and play” solution. Simply stated, this is a “portable” query engine that any business can install on their own premises and in their own machines and networks.  

Typically, there is an initial fee for installation and implementation of the system—roughly $50,000 or less—and then a monthly or annual recurring cost which may vary with the volume of usage but which isn’t likely to be a major cost and can be cancelled at any time. This puts a known cap and realistic limitation on the entire cost of the experiment. 

The firm designates and assigns an underlying selection of data for the engine (the corpus) to operate on, interrogate, and extract answers from. All employees have access to the system and can simply ask whatever questions about the data which they need further information about, request analytics or compilations of segments or selections of the data, or request the creation of documents, reports, or presentations of parts of the data which are relevant to areas or questions they are dealing with. One key consideration is that by limiting the dataset to material relevant to the specific business and activities of each company, the fit of the engine to the likely inquiry scope is better and the overall costs of operation as well as response time are reduced since there is no need to “search worldwide” or attempt to “boil the ocean” in order to develop and respond with timely and accurate information.  

Note here that the system has two critically important guardrails in place which govern every query. First, it knows what it knows, so it’s designed to report back that it can’t answer certain questions. Second, it knows the scope and limitations of what queries are proper topics for inquiry and rejects irrelevant or immaterial prompts. For example, the system doesn’t know the meaning of life. In addition, the system creates a log file of every query and every answer which can be reviewed by management to track activity and also readily determine how often and how effectively the system is being used.  

Other crucial elements of these systems are that there are evaluation, review and edit functions which in essence let the system “learn” and improve its answers and responses on an ongoing basis as well as allowing for corrections and updates—all within the control of the company itself and without ever allowing any such proprietary information to leave the premises. Security and control as well as demonstrable growth in efficacy are all central to the system. Because the system also permits any user to evaluate and rank the value and accuracy of any answer, there is a real time feedback loop that encourages and rewards employee use and engagement. This prospect makes it far more likely that team members will adopt and regularly employ the system especially as they see their own input and roles in the process incorporated into the system.  

Bottom line: you can put a toe in the water at a reasonable cost and try to expose your people to the upsides of these next-generation tools without incurring substantial expenses, without exposing your proprietary data to the outside world in an uncontrolled and unaccountable manner, and without encouraging or permitting your own team members to go outside the walls and waste time and energy randomly exploring the large LLMs without any particular guidance, support or benefit to your firm.  

I have installed two sample systems on my own website in the lower right-hand corner. These are free to use and to experiment with for all readers. The white link has a corpus which consists solely of my hundreds of Inc. magazine columns published over the last decade. The black link is a much broader corpus of all my books, columns, speeches and presentations, etc. which will attempt to answer a far broader range of questions. Give it a try before you decide to buy. Test before you invest.

 


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