New INC. Magazine column from Howard Tullman 4 Reasons You Need to Right-Size Your AI

 

4 Reasons You Need to Right-Size Your AI

Sometimes it’s smart to be careful—and slow—when you’re dealing with new technologies.

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

Aug 26, 2025

This is a very complex and challenging time for startups and small businesses in terms of how they should be addressing all of the issues and concerns around artificial intelligence and, more specifically, how they can incorporate the new AI tools and technologies into their own businesses. I realize that every startup in the world already professes to have built AI into their current offerings as well as into their future plans but, at best, many of these claims are nothing more than adaptations of machine learning or pattern recognition with a new shiny coat of paint and some text prediction capability. Sometimes it’s smart to be careful and slow when you’re dealing with new technologies. The last thing you want to do is be the latest victim of the fake-it-‘til-you-make-it disease.

It’s not remotely clear that a surface-level solution built on top of a generic large language model system will be of much value or benefit to many midsize businesses with very specific needs and nuanced market dynamics. One size almost never fits all these days. The implementation and operating costs alone of many of these systems would likely exceed any concrete internal improvements that addressed the user’s real needs. On the other hand, a smaller, more targeted, and clearly focused system whose objectives and functions the company’s management understands could be a valuable aid and time-saver if properly deployed.  

A side note that should be obvious but is often overlooked in top-down implementations of new tech is that you must secure buy-in from your key management and other pivotal team members and address in advance their concerns and the typical misunderstandings they may have about the plans, the short- and long-term job consequences, and other implications of the new systems and their roles in the process. 

We’re all rushing to employ these things before we fully understand them and, worse yet, it’s easy to come to depend on these seductive tools even when we know in our hearts that we’re not fully in control of them. You don’t need to cross the chasm in a single bound. Hallucinations and biases are only two of the most obvious risks and concerns when you start looking under the hood of some of these programs and discover that even their makers have only a passing idea of how they really work. 

The big guys in the corporate world can now rush to join the line of lemmings willing to pay OpenAI a consulting fee starting at $10 million to send a team of its eager engineers into their shops to build them custom solutions based on its GPT-4o technology. You would think that—given the havoc that the DOGE monkeys and minions brought about across our entire government—these corporate honchos would take a breath or two and ask themselves whether turning over the keys to their futures to Sammie’s smarties is the wisest course or whether it’s roughly akin to giving expensive whiskey and your car keys to the neighbor’s teenage son and wishing him well on his journey. 

If there’s a single statement that says it all for me right now, it’s the various versions of the observation that no one’s going to lose their business to AI, but most will lose their businesses to competitors who are more effectively using AI to streamline and accelerate their operations, to reduce their headcount without sacrificing customer connections and satisfaction, and to give them a far broader and more accurate overview of their marketplace, their competition, and timely intelligence and data to react to emerging positive and negative trends. 

The best and quickest of the players will rapidly realize that the hours and days they previously spent pouring over voluminous market data, analyzing their often incomplete and delayed compilations, and attempting to extract actionable information from the mess will now be replaced and made available in real-time detailed summaries crafted by young and clever prompt engineers.

The truth is that—with regard to the introduction of any new and disruptive technology—it will take every business a significant amount of time to learn how best to deploy it and how to deal with the displacements, interruptions, and new responsibilities and job descriptions that will accompany it and inevitably cause problems.  

Walking before you run—especially if you’re trying to do this development and implementation basically on your own—is the only rational and cost-effective course. It’s critical to keep in mind that you can always circle back and build better and more robust versions of what you’re initially experimenting with. It’s not likely to be an overnight project or an overnight success, but each iterative step will teach you a great deal, further empower you, and also help you to better understand the capabilities of the tools you are using—even as those abilities continue to grow and expand every day.  

What’s most important is for you to take the time to gather your team and review your operations and outline the areas where some intelligent automation could speed and simplify your own processes and actually produce a better result. In the first instance, none of this needs to be rocket science. Guesty is a legitimately AI-assisted property management system that was designed specifically for short-term rentals handled by Airbnb owners and operators.  

While this sounds about as mom-and-pop as can be, these folks face many of the same issues you do in your businesses—albeit at perhaps a smaller scale. The point is that, if this kind of simple use-case can show dramatic improvements in their metrics and their bottom lines, then shame on you if you haven’t figured out how to replicate these tools and techniques in your own shop.  

Here are four simple examples that a satisfied Airbnb operator told me has increased his yield and profit, dramatically decreased the time he was spending each week on his side business, improved his ratings and rankings with Airbnb, and led to repeat business and referrals from satisfied customers. And to be clear, I think he spends about $30 a month for the app. Eat your heart out.  

1. Hundreds of stored FAQ responses are delivered automatically in context-sensitive and narrative serial fashion 

You would be surprised and possibly shocked to learn how many times a day your team members waste their time repeatedly responding to and answering the same questions over and over again. Often, they do it slowly or inaccurately and eventually they do it impatiently—human nature being what it is—and none of this is good for your business. Automated responses can satisfy a significant number of callers who have simple, redundant inquiries and, more importantly, can deflect the wrong callers by simply and quickly making it clear to them that they are looking in the wrong place. 

Pricing is dynamic 24/7 and throughout each week based on a variety of factors and competitive offerings in the market as well as available capacity 

While in theory you could spend your entire day checking out competitive offerings and prices and adjusting your offers accordingly (and clearly Amazon does its pricing in this fashion every minute) and you could also constantly check your bookings through the week and determine whether price reductions might absorb available and empty units, rooms, or beds (just as American Airlines does all day long), the fact is that neither you nor anyone on your team has the time or interest to do anything like this, but the Guesty system does it automatically for you according to your guidelines and parameters instantly every day.  

Publish and sync your listings in real time across more than 50 major listing services including all the major sites  

You may use programmatic tools (with very little actual accountability) to get your messages out to the masses, but, in truth, you have little idea of who is seeing them and absolutely no real time ability to change or update the content or distribution plan. Intelligent systems using open APIs across multiple platforms give you a one-stop solution to precisely target and deliver your messages to qualified, interested viewers in the proper context with the ability to vary and alter any portion of the listings that you wish at any time.  

Responses to every inquiry are immediately replied to even if the reply is merely a placeholder and conversation starter 

Not surprisingly, response time is a measurable metric that firms like Airbnb use to evaluate the owners and operators on their site who use their services. Automated intelligent systems can respond instantly to every inquiry even if the response isn’t a substantive answer, but only a request for further info, details, or specificity to continue the conversation. In addition to managing the Airbnb metric, this immediate reply improves customer satisfaction and engagement without consuming any incremental resources until the lead is further qualified.  

Bottom line: While these examples may not directly apply to your company’s needs and current operations, each of them is an invitation and a suggestion to explore similar kinds of concerns and friction within your own organization and to see how AI and intelligent automation can help to address and improve things.  

NEW INC. MAGAZINE COLUMN FROM HOWARD TULLMAN

 

Why Now Is the Time to A.I. Audit Your Business

A.I. isn’t an optional add-on. It’s foundational and roughly equivalent to electricity or the internet.

 

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

Jun 17, 2025

 

In most of my conversations over the last year with new business owners and seasoned operators, all of whom are all concerned about ChatGPT’s impact on the economy, I’ve found an interesting contradiction in the way entrepreneurs are approaching the use and incorporation of A.I. I see a whole lot of wait and see.  Is it a genius or a clown?

While the novelty is starting to wear off, and the hiccups and hallucinations are certainly reasons for caution, the critical need to investigate, engage with and integrate these new technologies has yet to be fully appreciated and folded into the planning and operations of millions of businesses that really can’t afford to wait. They’re taking their time when the time to move is now and the timing couldn’t be more critical.

It’s not that hard to see why they’re conflicted. They’ve lived their entire business lives trying to be innovators, first movers and early adopters of new technologies in order to stay ahead of the competition. The “ready, fire, aim” attitude has mostly served them well over the years. But the truth is that smart entrepreneurs are far more careful and conservative than we’ve been led to believe. In fact, many are control freaks.

So, when they’re confronted with a pitch that basically says they should turn over some of their business processes to the “machine” because it will be good for their bottom line, they’re more than a little wary and reluctant to jump right in.  00:0001:49

Add to their basic mindset the fact that they understand almost nothing about how these black boxes really work, that they rarely have anyone presently onboard who can help them learn or who is up-to-speed on AI themselves, and that things seem to be moving ahead and changing at a ridiculously rapid pace. This makes for a perfect formula for angst and analysis paralysis. But, as is always the case, worrying never gets you anywhere and standing still is never the right solution.  A bad decision is often better than no decision at all. 

The good news is that there are simple and cost-effective steps forward –“toes in the water” if you will – that every company can take to get the ball rolling, and none are “bet the ranch” actions or expensive decisions. They’re simply smart ways to get smarter sooner.  

Every business today needs to conduct an AI audit if they don’t want to be left behind. AI isn’t an optional add-on at this point; it’s foundational and roughly equivalent to electricity or the internet. In the call center industry, for example, it’s now estimated that AI agents will handle 70 percent of all contacts by 2028. 

The first order of business doesn’t require technologists or AI experts. It’s simply a comprehensive review by your senior leaders of various areas of the business where AI may be able to help. Not, to be sure, by working immediate miracles (in spite of all the hype about eliminating hundreds of jobs overnight), but by helping you identify improvements, import better practices, and eliminate obstacles in your current operations.  

In my experience, this audit and review exercise also encourages your people to do some wishful thinking, to look forward to what could be, and to even think outside of their day-to-day, nose-to-the-grindstone activities and responsibilities. It’s a literal license to iterate and constantly improve.  

Broadly speaking, I’d break the critical categories down into four major buckets: automation of various internal processes, automation of various external processes, cleaning up and streamlining basic operations, and all your employee issues from augmentation, robotics, and realignment to concerns around recruitment and retention.

Once you’ve built a hit list and a wish list, you can bring in some professional help, a prompt engineer or two, and other AI resources to start building some solutions. Here are four examples.

Internal processes 

The long-term dream of a paperless digital world remains a remote and ambitious fantasy for millions of companies still drowning in reams of paper reports, receipts, requisitions, and records of all kinds. From the accounting department to the shipping center and personnel department, AI tools will create massive improvements in the traditional systems and antiquated procedures used in virtually every business, government agency and regulatory authority. Automation, digital records and AI-enabled identification processes will improve diagnostics in medical facilities, security in all of our transportation hubs and public areas, and in the entire finance world. 

External processes 

As the world becomes increasingly comfortable with ATMs, self-service checkout counters, and other forms of automation, AI systems can speed up, simplify, enhance and scale all of your front-of-house interactions with customers, clients and consumers including sales, service, and support. Millions of bank customers already acknowledge that they would rather not deal with a teller if efficient alternatives were available. AI tools can also streamline, simplify and optimize websites which, in many instances, companies haven’t reviewed or updated in years to improve customer experience and speed up the process.   

Basic operations 

Real-time review, ongoing support and enhancement, and timely intervention to avoid problems, breakdowns and other system interruptions are already being implemented in manufacturing firms around the world. The ability to project needs, demands and resource requirements will build even further upon the economic success of many just-in-time supply and warehousing chains and save huge amounts of time and money. Having AI systems review months or years of prior actions and activities and generate detailed analytics on the fly will provide insights, new directions, and even concrete suggestions for process improvements and better use of personnel and other materials and resources.  

People  

AI and related intelligent agentic devices and robotics can augment and supplement the work done by your employees to improve accuracy, capacity and safety as well as avoiding burnout, repetitive behavior injuries, and human errors. Systems are already being designed to identify, evaluate and categorize job applicants on a variety of criteria, to assist in scaling and speeding their documentation, onboarding and training, and to outline and create multi-year individualized career paths for each team member which serve as great recruiting tools and help to manage education, expectations and attitudes as well as improving retention. MIT and Nvidia Research have already developed a new algorithm that enables a robot to “think ahead” in a planning process and evaluate thousands of alternative paths in seconds. 

The bottom line is, you don’t know what you don’t know about your own business until you ask. Now’s the time to start asking. There’s no better, more cost-effective system than an AI system built for and based upon your own data as well as employing comparable data and other information drawn from the industry, your competitors’ reports and activities, and all manner of other external information and data sources. An AI audit is step number one.  

New INC. Magazine column from Howard Tullman

 

The AI Hype Machine is Running on Empty.

After dumping hundreds of billions of dollars into AI startups, investors are discovering that the payoff to date has been extremely underwhelming. 

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

JUL 23, 2024

 

In a column in January I noted that in the practical world of business, where real results matter rather than hype and bragging rights, the smart players were starting to back away from their substantial commitments and investments in generative A.I. tools and projects. Especially the guys who write the checks and keep score. Yeah, they were all still talking a good game, but fewer and fewer of them were putting their money where their mouths were.  

The main reason seems to be that the near-term prospects for seeing concrete growth and improvement in revenues as opposed to cosmetic reductions in admittedly overbuilt headcounts aren't very encouraging. In many cases, any paths to eventual bottom-line benefits weren't even apparent because the operating costs of these new large language model (LLM) engines are so high that the businesses were spending serious capital dollars to generate digital dimes - if they were lucky. Compelling, substantive use cases for these tools as opposed to novelties, chatbots and toys have been few and far between.

What has really been emerging is the fact that, in addition to AI being ridiculously costly and resource intensive, after all the manipulation of the underlying data is done, you still need to hand off the output to a human being to actually get something done. Instructions aren't the endpoint of virtually any process - whether it's manufacturing, medicine, or movement - it's real-world implementation and execution by people that ultimately gets the tasks done.   

Things might be getting done faster, but it's by no means certain that the outputs are better. And it's absolutely clear that these outputs aren't new or innovative because they're ultimately constrained by the limits of their training data to making what amounts to best guesses at what's next based on what's happened in the past. You still can't Google the future. And if you're not smart and sharp enough to ask exactly the right questions in your prompts, you get garbage for an answer. There's no big prize for even having the best answer to the wrong question.

Bumping the speed and the scope of analysis or review may create some efficiencies, but these "improvements" don't add "intelligence" until the outputs are evaluated and employed by the human end users. No one's willing to turn these systems loose until their results, findings and conclusions have been vetted and fine-tuned by humans. Hallucinations might be a more polite term than lies or fabrications by the machines, but ultimately no one is going to trust them with our lives or our livelihoods any time soon.

There's still talk about the next generation or newest black box that will work some kind of magic that not only scales but shrinks costs as well, but there's no evidence that it's anything more than a pipe dream about a new version of Moore's Law. One of the flaws in this analysis is that the underlying foundation of Moore's Law is that experience is gained in production over time, which enables exponential enhancements in the circuitry. Sadly, to date, it's clear that in the GPT world we're interested bystanders at best and, while it's fascinating to watch, we rarely learn much of anything beneficial that will let us improve the process. Nor is there evidence that simply by adding more data and more computational power, we do anything to improve or expand the outputs so that they become self-effectuating and autonomous.   

Interestingly enough, we are finally starting to see that even the shameless hucksters and promoters on Wall Street are taking a hard second look and changing their tunes from rabid generative A.I. boosterism to a far more tentative endorsement that smells more defensive than aggressive. Research reports, press conferences, and presentations from most of the leading financial firms, led by Goldman Sachs, are beginning to observe and report on the empirical evidence in the field, which suggests that they may have completely misunderstood what's happening with this latest technology. Two key things are becoming obvious and each of them is largely contrary to the speeches and spiels we've been hearing from these guys for the last two or three years.

First, there are entire industries where the ultimate impact of these kinds of tools will be largely immaterial - construction is a good example. Even Goldman Sachs suggests that only around 6% of the fieldwork in construction and extraction businesses will be automated and the productivity improvements would most likely simply be a wash for the additional costs. There may be some augmentation but even those tasks will continue to be directed and executed by onsite workers. Fast food, customer service, and transportation will be other areas where it will be very difficult - without sacrificing the quality of engagement and experience - to dramatically reduce personnel. We've already seen all of the major QSR players, including McDonalds, take steps to back away from some of their initial AI implementations.

Second, the most likely jobs to be eliminated in large numbers through substantial task automation (30%-to-50%) are NOT likely to be the low-paying positions (no collar and blue collar), which require physical labor and direct interaction with customers and co-workers. Instead, white collar and new collar (knowledge workers) positions including administrative jobs, legal work, financial analysts, marketing, and writers and editors will take the hit. Unilever in Europe is already leading the pack in this workforce pruning.)  The two critical defining characteristics of the targeted jobs will be that (1) it is very difficult in many of these cases to directly measure productivity and (2) senior managers looking for easy cuts and economies with only passing concerns for content, originality, innovative analysis and quality will happily trade out these positions for machine-created material that may well be drawn and lifted from other similarly situated creators.

When you look closely, as everyone has finally begun to do, the only conclusion you can make is that unless you're Nvidia and basically producing the picks and shovels for this industry (and largely without competitors), there's unlikely to be very much there there. And what is there will inure (as usual in tech) to the biggest of the big guys. As the saying goes, when the elephants start dancing, the grass takes a beating.

 

NEW INC. MAGAZINE COLUMN FROM HOWARD TULLMAN

 

The Future Belongs to Prompt Engineers

Although the platforms that will run AI will largely belong to big tech. There will still be plenty of opportunities for startups to live in this new world. 

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

MAR 12, 2024

In my next life, I think I'd like to be a prompt engineer. If you don't know what that is, I'm not really surprised; most people don't.  But you should learn because in the next decade these folks are going to be among the most valuable, strategic, and in-demand employees in any company.

Prompt engineers learn how to think like a machine. Your business will need to find and hire these folks if you plan to be competitive for the same reason that most NFL football teams have more data scientists on their rosters than they do quarterbacks. Data is the oil of the digital age, and its use and proper application will drive every kind of company in the future.

If you plan to scale your business, you can be sure that you'll need to augment your team's own actions and decision making with machine-driven technologies to be able to match the speed, recall and reaction times of the competition. In the digital world, speed kills in a good way. Prompt engineers are the humans who will translate our queries and be our primary interfaces to the existing and constantly emerging massive AI knowledge systems, artificial neural networks, machine-learning environments and large language models (LLMs). These AI engines are now being built by the country's largest tech companies and rapidly deployed around the globe. As you might expect, the prime players are the usual suspects and the only ones who can afford the required investments while, of course, our government itself (unlike China's) isn't even in the game. Once again, it's gonna be a winners-take-all world.

There's also a powerful and discouraging reminder here of the early days of the computer gaming industry, where dozens of entrepreneurs thought they'd build their own game machines, interfaces, and programs. Over a relatively short period of time, these dreams were crushed by Sony, Nintendo, and Microsoft, each of whom developed a dominant platform and basically required all the other industry players to develop games that would run on top their platforms.

While stupid and greedy venture investors will invest and lose billions betting on AI nuts-and-bolts startups, it appears entirely likely that the real battle for platform dominance is largely over.  The playing field will be owned and operated by the same half dozen or so tech giants--Apple, Amazon, Meta, Alphabet, et al. -- that already own search, the desktop, our phones, email and messaging, video, and large slices of the Internet itself.

Everyone else will be remitted to running on top of these platforms, and as licensees or "partners," with the gatekeepers of these major systems. As the industry giants continue to build out the underlying infrastructure for these processing environments, the most likely and potentially profitable opportunities for entrepreneurs and new business builders will be in developing industry and market-specific tools and applications that make use of the capabilities of the LLMs and other versions of the machines, rather than trying to create new versions of the systems and machines themselves.

This is actually good news in one respect:  little guys will still have a place in the ecosystem. The successful smaller and more agile players will be more organized around supporting business operations, exploiting their industry and market knowledge, and enhancing business logistics, rather than on building costly and super-technical programs that take years to develop and are likely to be copied and overrun by extensions of the established players' offerings. In addition, the capital costs of entry for specific solution suppliers will be considerably less, as will be, at least initially, the cost of attracting and retaining scarce talent.

Amazon of old was a good example of this business versus bits distinction. Today, they call themselves an A.I.-driven tech biz, but at the beginning, Amazon was all about logistics, location, execution and speed. Their tech was okay, but the competitive edge was their aggressive leadership, industry knowledge base, powerful analytics, and people who were hungry, competitive, and scrappy workhorses rather than kids, academics and computer jocks. They quickly came to know the basic book business better than the big guys in the space and beat them at their own game. Also, in fairness, the development work, and advances that Amazon has made with Alexa in terms of voice recognition, interpretation, and conversation continuity have helped dramatically move the A.I. needle forward. That work remained largely under the radar for many years - whether by design or media inattention.

Knowing what's important to ask and how to frame the right questions of the new machines will be the most critical competitive skill set in the new A.I.-enabled economy. That's because, as often as not, it's harder to shape and design the precise question than it is to eventually find the correct answers. The skills required to do this job well are far more practical and qualitative talents rather than the more quantitative and purely technical ones we ordinarily associate with computer scientists and engineers.

Anyone who has ever asked Google a question and had the unsettling experience of being told in response that there are "about 1,250,000 search results" knows that limiting and narrowly stating your inquiry is the key to achieving any simple and useful answer. In much the same way, the current LLMs are way too much of a good thing and need to be tamed and bounded. In fact, at the moment the best way on the web to get the right answer to a question is to post the wrong answer and wait for the good Samaritans and the trolls to weigh in.

Smart prompt engineers will iteratively fashion and input the "prompts" or plain English questions, which will ask the generative machines for increasingly precise and detailed answers, solutions, directions, evaluations, statistical relationships, and other responses. This will all be based on the machines' compilations, interpretations, and discovered connections, which it will theoretically draw from literally all of the digital data and accumulated knowledge in the world. The latest advances have enabled the machines to retain the content and context of earlier inquiries and incorporate those requests into the continuing series of prompts, which has made the iterative process somewhat easier and more consistent.

What's especially attractive about becoming a prompt engineer is that anyone can learn the job and - as far as I can tell - not only don't you need an extensive technical background or an expensive education, you simply need a lot of common sense, an extensive vocabulary, the ability to constantly iterate and tighten down queries on a wide range of subjects, and a passion for problem solving, crossword puzzles, Scrabble and Wordle. Readers, writers, liberal arts grads, and kids straight out of high school are all welcome. It's about aptitude and knowledge, not college -- what you know, not where you go. It also helps to be hyper-literal and anal as well, but that's not essential. Remember that these people are trying to learn to think like the machine they're interacting with.  

And even if you (or your kids) aren't looking for a new career, and don't have access yet to the latest and greatest tools, you should still spend a few minutes experiencing these back-and-forth conversations. I recommend trying Microsoft's Copilot which is free, readily appended to its suite of Office products, and couldn't be easier to use. It invites you to "ask it anything" and it's an interesting trip down whatever rabbit hole strikes your fancy.  And more than a little addictive. You can start to build your own company-specific questions and also do a little DIY experiment to see how these systems can make sense out of, and better organize, your business's own data, historical information, and customer input about their experiences in order to provide support for and augment the performance and behaviors of your team.

Having instant access to the world's storehouse of accumulated information, literature, and knowledge at your fingertips is a very exciting and empowering feeling.  One which will give you a clear idea of why so many people are thrilled, awed, and scared by the possibilities, opportunities, and challenges of these new tools. AI is all about careful curation and filtering the flood at this point. Prompt engineers will be steering the ship and leading the way.