HOW A.I.-POWERED DATA ANALYSIS CAN HELP YOU MAKE FASTER AND BETTER DECISIONS

Artificial intelligence is about to make your data analysis a whole lot faster.

The process of turning data into an actionable report is crucial for business leaders, with Gartner predicting that the market for business intelligence and data analytics software will grow to $13 billion by 2025. Part of that growth is being driven by businesses that are using A.I. to analyze large sets of data at a speed and scale that would simply be impossible for humans. The leaders behind these companies say that A.I. allows them to deliver high-quality reports in moments, rather than weeks.

Here are three examples of how you can use A.I. to make stronger, better, and faster decisions. 

A.I. for saving your supply chain 

Disruptions in your supply chain can wreak havoc on your ability to do business. When a company wants to identify the biggest risks to its supply chain, it either has workers spend days or weeks compiling the relevant data from disparate sources, or it pays an outside consulting firm tens of thousands of dollars to handle it, according to Francisco Martin-Rayo, co-founder of A.I.-powered agricultural supply chain management tool Helios. The firm, founded in 2022, uses A.I. to exponentially speed up that process, enabling procurement specialists to easily keep track of all their suppliers and analyze each supplier's current risk level. 

Helios compiles billions of data points related to historical weather trends, commodity-based economic indicators, and political signals to create its analytics. Those historical signals are compared with the supplier's current indicators to predict disruptions before they happen. Helios is also working on a generative chatbot called Cersi (named for the Greek god Helios's daughter). By eliminating the time-intensive process of generating reports, Martin-Rayo says procurement and analysis teams can spend less time identifying risks and more time mitigating them. 

A.I. for learning more about your customers

Perfecto Sanchez and Christina Van Houten, co-founders of A.I. stakeholder intelligence program Equity Quotient, say that you can use A.I. to simultaneously reach new customers and deepen relationships with existing ones through "demographic alignment," defined by the pair as the process of tweaking elements of your business so that they algorithmically appeal to the specific customer demographic you're trying to reach.

As an example of how Equity Quotient's platform, which is powered by a massive set of public and private socioeconomic data, can be used to achieve demographic alignment, Van Houten related a recent experience she had working with a national mortgage lender's CMO. The CMO wanted to gain a better understanding of how to secure new loan business from second-generation Hispanic populations across the country. With help from Equity Quotient, the CMO was able to identify specific cities and counties with high Hispanic populations and significant access to affordable housing stock. That data informed a targeted marketing campaign and sparked an initiative to hire more Hispanic loan officers. Van Houten says that studies have shown minority borrowers are more likely to be approved by minority loan officers than White ones. "We see our opportunity for growth in giving leaders a clearer view of the individual pieces that make up their business," says Van Houten.  

A.I. to make sense of your raw data 

Companies are leaving a lot of data on the table. According to a 2023 report from Seagate, only 32 percent of data available to enterprises is ever analyzed, while the remaining 68 percent goes unleveraged. Arina Curtis, co-founder and CEO of DataGPT, an AI-powered platform for analyzing internal business data, believes that by analyzing 100 percent of a company's data, businesses can understand the context and meaning hidden within the numbers, leading to better decisions. Once DataGPT connects to an organization's data warehouses and other data repositories, users can ask natural language questions to a chatbot and get detailed analysis back, according to Curtis.

One of DataGPT's largest clients, the ad-supported streaming service Plex, used DataGPT to understand what was driving a decrease in new users on a specific streaming device. The company found that conversion rates were actually up overall, so the issue had to be related to the Plex app's performance on that specific streaming device. The Plex product team discovered that glitches were hampering the app's performance on the streaming device. Within a week, fixes had been deployed, and the conversion rate immediately began shifting back to normal with a healthy flow of new user registrations. 

8 ATTRIBUTES THAT MAKE YOU A GOOD BUSINESS OWNER

As an old angel investor in new businesses, I quickly learned to look for certain personality traits that constitute a mindset of perseverance and determination to get things done, versus a passionate dreamer and thinker who could talk well but not deliver a return on my investment. I offer my insights here for those of you who really want to build a successful business.

Not all of us are cut out to be business owners and drive solutions to the many challenges facing every business today, even though we may be unhappy with our current business roles and leadership. I challenge you to do your own self-assessment against the following attributes before you strike out on your own, especially with someone else's money:

1.    Move quickly from an idea to a documented plan. 

Business professionals and aspiring entrepreneurs who claim to have the most ideas are generally not so adept at starting and growing businesses. I look for people who show me they have a very specific plan, are proceeding through implementation, and building a support team along the way.

I find that a written business plan really has the most value to the founder since most mere humans can't build and communicate a complete plan in their heads. The costs for items overlooked or forgotten can be huge and will jeopardize your funding. 

2.    Stay focused on a meaningful business objective. 

Trying to make the world a better place may be laudable, but does not always sustain a business. A business must have customers willing and able to spend money. You need to produce a product or service that satisfies a current need, and is competitive with other alternatives in the marketplace.

In addition, it is also important to align your business objectives with your own personal priorities and interests. The reality is that if you build a business you love, you may make big money, but if you start a business you hate, just to get rich, you will probably fail.

3.    Be ready and willing to take the required risks to win. 

Willingness to seek out and take smart risks, rather than any risk or no risk, is the mark of a top business owner. Taking risks requires making decisions and initiating action, rather than ignoring challenges or dodging decisions. In business today, making slow or no decisions leads to early failure.

4.    Be able to communicate your value-add to constituents. 

Your team as well as your customers need to understand your value proposition. Keep it simple enough to fit in a single sentence, without acronyms or technical jargon, or a need for abstract or complicated explanations. Make this a key part of your pitch to investors and customers.

5.    Put faith in your confidence and determination.

Positive and energetic business owners can accomplish the impossible - pulling success from the clutches of defeat. It has to start with faith in yourself, but a business requires a team, so don't forget team building of the same attributes in your team, and providing the leadership they all need.

6.    Really listen to customers, team members, and advisors. 

Asking questions and listening to feedback is much more effective in growing a business than talking and selling yourself. Great business owners have a mindset and develop a team culture of focusing on key issues they can control, rather than being distracted by external events.

7.    See change as an opportunity rather than a problem. 

Change is normal and required in every business, so I look for owners who have overcome their own fear of change, and reward rather than penalize team members who argue for change or fixes. You must look at all change experiments as learning opportunities and competitive advantages.

8.    Attack a challenge based more on data than emotion. 

Business problems cannot be solved by wishful thinking or ego, so you need to demonstrate your ability to find the root cause of problems, think innovatively about alternatives, and use real data and advisors to create and execute a real plan to resolve tough challenges in a timely manner.

Of course, every business professional may choose to improve his fit for business ownership and leadership by adopting a mindset of adapting to the set of skills, rules, and emotional balance principles outlined here. I love to see and mentor people who are looking to learn and change their own approach to get the satisfaction and success they want in their careers. 

BY MARTIN ZWILLING, FOUNDER AND CEO, STARTUP PROFESSIONALS@STARTUPPRO

HOW REPUBLICAN CANDIDATE DOUG BURGUM WOULD USE A.I AS PRESIDENT

North Dakota Gov. Doug Burgum, a Republican, says he will use artificial intelligence to make the federal government more efficient if elected president.

Burgum shared a preview on how he'd approach A.I. in a recent exclusive interview at Inc.'s offices, framing A.I. as a "co-pilot" that can help government workers improve their productivity, instead of a technology that will eliminate jobs. 

He speaks from experience.  North Dakota, Burgum said as an example, used AI when the budget cuts kept the state from hiring additional full-time employees. Earlier this year, the state government further partnered with cybersecurity technology vendor Palo Alto Networks to work on an autonomous software that would handle all of North Dakota's IT cyber protection and response duties, according to cyber security news outlet CSO Online.

"You didn't get every state employee you wanted to hire. But I got [you] a co-pilot," he says, referring to A.I. "This co-pilot speaks 26 languages. It can code. It works seven by 24. It doesn't need a desk, it doesn't need a computer. It doesn't need any state benefits. It's free." 

A.I. is just one part of Burgum's plan, which he noted in a far-reaching conversation that ranged from the importance of antitrust policy to the problem of overregulation in the federal government. Burgum, a longtime entrepreneur, talked about the pivotal role being a founder and investor played in his life, so much so that it would inform his presidential style, he says. 

"From that whole journey of being involved in creating dozens and dozens of startups over my life," he said. "The learning is innovation, not regulation."

Considered one of the wealthiest governors in America, Burgum led the accounting software company, Great Plains Software, until 2001 when it was acquired by Microsoft for more than $1 billion. After that, he was involved with several other startups, some of which also went public. These companies include early growth capital firm Arthur Ventures which he founded, and software company Atlassian, which he led as board chairman. These experiences helped win him two consecutive terms as North Dakota Governor since 2016, despite not having any prior political experience. 

When asked about how he felt about small businesses, Burgum said he thinks the federal government has not given entrepreneurs the support they deserve, especially when big corporations hold outsize sway in Washington. To this end, if elected he would keep the U.S. Small Business Administration intact, but he would rethink how it serves its clients.

"What are we doing here to support these entrepreneurs? That's where job creation happens in our country," he says. "We've got to figure out a way to support them, and I think this is not just as much as the programs you do, but getting the red tape and the anti-competitive practices out of some of these industries."

To suggest that the SBA has been anti-competitive or that it hasn't been streamlining its processes would be inaccurate. In the last year, the SBA had done away with various practices like having to get paper loan approvals before agreeing to back a small business loan. And the agency has worked to add more certified lenders into its community.

Burgum says that he would adopt many of the efficient practices he has been able to implement as a two-term governor of North Dakota to the White House. By way of example, in 2022, Burgum formed a working group to weed out unnecessary and burdensome policies across agencies in the state, according to an executive order he signed involving eliminating red tape. The governor has since signed 50 bills that aimed to either streamline or modernize these policies for a more efficient government. 

"I don't know any other governor alive who's ever cut 27 percent of their budget and kept the trains running on time," said Burgum. "We can keep making those kinds of moves if we keep applying the productivity that makes the jobs more meaningful and makes the services better delivered." 

And he's not convinced the government is currently on track. The federal government spends more than $100 billion on information technology and cyber investments every year, according to the U.S. Government Accountability Office (GAO). But that doesn't mean the use of technology has led to greater efficiencies. In some cases, the use of technology has caused harm. In February this year, the Biden administration ordered federal agencies to "root out bias in the design and use of new technologies, such as artificial intelligence."

Burgum isn't swayed by those negative results. "It doesn't matter what segment of the economy you're in. If you're a small business owner, you're engaged with technology," he says. "Technology's changing every job, every company, every industry." 

Still, he is in favor of limited federal intervention-particularly when it helps to level the playing field between big and small businesses. Case in point: the Federal Trade Commission's recent case against Amazon. "There are places where we need guardrails, and the FTC is looking into some of those right now. But I would support that because we've got to make sure that we are creating an opportunity for small businesses to be able to be competitive." This, he said, is how small businesses can win on their own merit, instead of losing because "big tech slanted the table against them."

Ultimately, smart regulation is about ensuring fairness and then just getting out of the way, Burgum says. "I'm not a regulation guy," he says. "But we have to have some guardrails."

THE A.I. TERMS EVERY BUSINESS OWNER SHOULD KNOW

In just the past year, advancements in artificial intelligence have introduced transformative new ways to automate complicated tasks. Your company won't be able to take full advantage of this new technology, however, if you don't understand how it works. 

To help you make sense of all things A.I., we're building a living document to explain all the hard-to-understand terms around A.I. We'll continue to update this guide with everything you need to know about A.I. These definitions were written with the help of Tiago Cardoso, principal product manager at digital transformation firm Hyland. 

1. A.I.

A field of science dedicated to creating machines and computer programs capable of recreating the cognitive functions of the human brain, such as making decisions through logical reasoning, recognizing and categorizing objects, and learning new things.

Think of it this way: A.I. is an umbrella term used to describe a wide range of technologies, but any program that processes information to perform a task can be considered A.I. 

2. Computer algorithm

A set of instructions that a computer follows to perform tasks and process data. Social media companies like Facebook use algorithms to analyze the type of content you interact with most often, and then use that information to score every post, video, and ad on the platform by how statistically likely you are to click on it. The top-scoring posts get pushed to the top of your feed.

Think of it this way: Any time you use an Excel formula to perform data analysis, like calculating a combined sum from hundreds of data points, you are creating a basic algorithm, complete with a set of instructions for how a computer program should process specific data. 

3. Machine learning

A branch of A.I. in which an algorithm is altered or enhanced by processing a dataset and identifying the underlying patterns and relationships hidden within the data. For example: A machine learning algorithm trained on thousands of images of your company's product would be able to identify how often it appears in social media posts. 

Think of it this way:  Your email's spam filter uses machine learning to identify keywords and patterns that often appear in unwanted messages. When you receive an email, an algorithm calls upon its training data to determine if the text of the email is statistically closer to its database of spam emails or safe emails, and sorts them accordingly. 

4. Model

A computer program that's been trained by a machine learning algorithm to perform a specific task. After being trained, the program is left with a "model" for how to process new input data, like a text prompt or a voice recording, into predictions and insights based on the patterns it has learned from the training data.

Think of it this way: ChatGPT is a language model. Your text prompts serve as the input data, which is processed by the model and converted into the chatbot's response. 

5. Generative A.I.

Artificial intelligence programs are capable of creating and generating "original" content. Recent advancements in A.I. have led to breakthroughs for image-generation models like Dall-E and large language models like ChatGPT, but the tech is also being used to create original music, video, and code. 

Think of it this way:  Generative A.I. is an extremely new technology, and the rules around its use are still being debated. As such, be careful about how you implement it in your business. The US Copyright Review Board recently determined that AI-generated art cannot be copyrighted, for example. 

6. Training Data

Sets of data that are processed by machine learning algorithms to improve their functionality.

Think of it this way: Datasets, which are often extremely large, are fed into machine learning algorithms to teach them how to respond to inputs. Once the data has been processed, it gets converted into a model. There are two main types of training for machine learning algorithms: supervised and unsupervised. 

7. Supervised learning

Training in which each piece of data is paired with a label, which helps the machine learning algorithm understand the meaning of the data. An algorithm being trained to make a diagnosis based on X-ray scans, for example, would be trained on images labeled with the correct diagnosis  

Think of it this way:  An object detection model designed to identify fruits would be trained with many different pictures of those fruits, all paired with the correct labels. Through training, the algorithm would learn to identify the unique characteristics that define each fruit. 

8. Unsupervised Learning

In unsupervised learning, the training data doesn't come paired with any descriptive labels. Rather, machine learning algorithms process large amounts of data, which are then grouped into "clusters" based on their similarities or differences. This style of learning is what allows ChatGPT to do all kinds of tasks, like holding conversations, writing stories, and answering questions. It wasn't trained to do any one thing specifically; it's been loaded up with a massive collection of text. 

Think of it this way:  Alpha-Go, the A.I. model that beat a world champion in the classic game Go, wasn't trained on any labeled information about gaming strategies; it just played the game enough times to master every possible winning pattern.

9. Neural networks/Deep learning

One of the oldest, and for the last decade most dominant, designs for A.I. programs, loosely modeled on the organization of neurons in the brain. A neural network consists of several layers of interconnected nodes, which act as the network's "neurons." Each node processes input data, performs calculations, and outputs the data to be reprocessed by the next layer of nodes. Deep learning is a class of especially large neural networks with hundreds of layers, which allows for even more connections.

Think of it this way:  Most generative A.I. models are built with deep learning, with the largest neutral networks being large language models like ChatGPT, which have billions of "neurons." 

10. Parameters

In a neural network, parameters are the settings and weights that control how each "neuron" or node processes and transforms input data. You can imagine parameters as knobs on an old radio. Just like you'd adjust the knobs to improve the frequency, volume, treble, and bass of the radio, parameters are automatically fine-tuned during training to create an optimal output. 

Think of it this way:  Imagine an A.I. model built to analyze images of license plates taken from a red light camera. Each "neuron"/node has a parameter responsible for turning the image's pixels into a sequence of text and numbers that the model can understand. 

11. Natural language processing (NLP)

A specific type of A.I. designed to understand and interpret everyday language. NLP models are trained to break down a piece of language, either written or spoken, into machine-readable data. 

Think of it this way:  NLP models can be used to analyze documents, turn speech into text, translate between languages, and create advanced chatbots. 

12. Transformer

A highly advanced type of A.I. architecture that has hastened the revolution in generative A.I., and in particular the field of natural language processing, since being introduced by Google in 2017. Transformers use a process called "tokenization" to convert a string of symbols like this sentence into data, and then analyze that data to identify patterns. 

Think of it this way:  Nearly all modern natural language processing models, like OpenAI's GPT (Generative Pre-trained Transformer) family of models, are built using transformers.  

13. Tokens

Grammar elements that have been converted into data by a transformer. When you submit a query to ChatGPT, for example, the transformer takes your sentence and turns it into a series of tokens. The transformer processes each token at the same time and is able to call upon its training to understand the semantic relationships between tokens. According to OpenAI, one token generally corresponds to around 4 characters of text, but they are often slightly shorter or longer, and special characters like punctuation marks are usually counted as their own token.

Think of it this way:  The sentence "Nowadays, I feel goodish." would be tokenized into eight tokens: "Now-adays-,-I-feel-good-ish-."

14. Hallucinations

Instances where an A.I., usually a large language model, produces something that sounds plausible but is untrue. The A.I. isn't technically lying, since it doesn't know that what it is saying is false, thus the term "hallucinations" 

Think of it this way:  New York attorney Steven Schwartz used ChatGPT to find cases for him to cite in a legal briefing. Schwartz didn't realize that the cases generated by ChatGPT were hallucinated until he was asked to provide copies of the cases. 

15. API (Application Program Interface)

A software component that allows you to integrate someone else's program into your own application without needing to understand the underlying code. A.I. models are deployed and released through an API so that companies can monetize their technology by providing outside parties with access to the tech's services and capabilities.

Think of it this way:  OpenAI has released APIs for nearly all its A.I. models, with users being charged depending on how many tokens are used to process and output a query. 

TURNED DOWN FOR A BUSINESS LOAN? A.I. COULD BE TO BLAME

The frosty credit environment that small businesses have been experiencing for the past year could be getting even chillier. That's all thanks to artificial intelligence.

Financial institutions are increasingly employing A.I. and machine learning, including for work like risk assessment and credit scoring. A survey conducted in June by the mortgage industry advisory firm Stratmor Group found that 22 percent of lenders are already using A.I., up from 13 percent in 2018. For lenders, particularly smaller ones strapped for staff and resources, investing in automation has the potential to add up to major cost savings. By 2030, those savings could reach as high as $1 trillion for the entire industry, according to a report from the financial research firm Autonomous, which found that banks could cut costs by 22 percent by using artificial intelligence.

While A.I. could be a major boon for lenders, which now have the ability to gather and process much more consumer information than the standard debt-to-income ratio, the federal government is considering what that means for borrowers, particularly those who find themselves turned down for a loan or credit line. Last month, the Consumer Financial Protection Bureau issued new guidance about legal requirements for lenders using artificial intelligence and other advanced algorithms. The agency said that if a borrower is denied credit, the lender must provide "specific and accurate reasons" why that decision was made. 

"Artificial intelligence is expanding the data used for lending decisions, and also growing the list of potential reasons for why credit is denied," said CFPB director Rohit Chopra in a statement. "Creditors must be able to specifically explain their reasons for denial."

Richard Gusmano, the founder and CEO of Business Credit Consultants, which helps small businesses obtain credit, says that the CFPB is trying to safeguard against machine learning becoming the sole decision maker determining which small businesses are approved for a loan and which ones are denied. Gusmano sees the rule as a positive one for businesses but stresses that the guidance was "such a broad stroke" that questions remain about how it will be implemented. 

"We're not seeing a timeline," says Gusmano. "We need reasons, but how quickly will those reasons come out? ... That's the thing that I'm most interested to see."

The best defense against having your loan application rejected by A.I. is to be even more scrupulous than usual with your documentation. "A lot of the information that banks are given or a lot of information that accountants put on the top form of the tax return is inaccurate," says Gusmano, who adds that this problem will be most acute for businesses working with a lender for the first time.

In his experience, one of the most common mistakes is a company's North American Industry Classification System, or NAICS, code, which the federal government uses to classify businesses by sector. The six-digit number is often chosen by the accountant filing a business's taxes, so owners should check that the category is accurate. Talking with your accountant before applying for credit is also important, he says, because their objectives when preparing tax documents are often in direct confrontation with the determinants that A.I. models are trained to look for in creditworthy businesses. 

"Accountants want to show losses to mitigate tax," says Gusmano. "Let's make sure Mr. or Mrs. Accountant is not being overly aggressive with losses, and let's make sure that the numbers make sense."

The pandemic, Gusmano says, has also created unique problems for businesses trying to circumvent banks' algorithmic models. Some of his clients who have transitioned to a remote model or moved offices in the past few years have been tripped up by inconsistent business addresses.

"It's common-sense stuff, but there are mistakes all over the map," he says.

WHY A.I. IS FORCING STARTUPS TO CHASE A MIRAGE

If you've ever wondered why a new company with a ton of potential would suddenly start making moves that seem awkward and desperate, there's usually a good reason.

Actually, there are four good reasons. But they all boil down to one root cause, and that root cause can change as trends come and go.

I've spent 25 years founding, working at, and advising startups. I've seen company priorities go off the rails more times than you might imagine. Another sad story dropped into my inbox last week, and this time the antagonist of the story was A.I.

The Sad Story

The founding CEO of a generative A.I. company wrote to me via my website. Long story short: 

• They launched their non-A.I. product to some very promising sales numbers. 

• Then they rode a series of A.I.-fueled catalysts to even more success.

• But ultimately, they came crashing down after just a couple of years, looking nothing like the company that the founder initially set out to build. 

Reading through the founder's story, I felt an uneasy sense of deja vu. Because it's the same old story I've seen over and over again, only with a different villain this time.

Here are four ways that a change in a startup's priorities can lead to some unpleasant changes in direction.

1. Chasing the Wrong Kind of Attention

One of the more unconventional lessons to learn at a startup is that attention that doesn't directly lead to revenue is usually more trouble than it's worth. 

The founder who wrote to me had founded his company a little over two years ago, offering a mental health companion app. The company had achieved some initial success, both clinical and financial, and was slowly but surely on its way to establishing a foothold in what had become a hotly competitive market.

Then the A.I. gold rush started percolating. 

While generative A.I. would not necessarily have a material impact on the product's value proposition, the mere mention of A.I. created a lot of buzz in investor meetings and sales pitches. So, against the founder's instincts, the company began referring to itself as an "A.I.-powered mental health companion app."

And while the company's progress had heretofore been slow but steady, it began to pour all its efforts into an A.I.-powered quick and dirty climb.

Attention is nice, and in a lot of cases with early-stage companies, it's absolutely necessary. But that old adage that "any press is good press" is just not true with a startup. The attention received requires attention returned in kind, and that always comes at the cost of time and resources that would be better spent building solutions, chasing leads, and closing deals. 

2. Chasing Misaligned Connections

When you're running a small startup with big plans, it can be a lonely mission. Promise and potential are a startup's currency, and stewardship of that potential is as critical as stewardship of any other kind of investment.

The founder turned that potential into a bevy of calls and meetings with investors and influencers, looking to accelerate the progress of the company by striking while the A.I. iron was hot. But those connections, fueled by the power of A.I., weren't aligned with the mission and vision of the company, which was making mental health more accessible. 

Again, a startup is a lonely proposition, and there is strength in numbers, in terms of both funding dollars and potential customers. How do you say no to those opportunities?

The company, as it was being promoted, was quickly starting to offer a promise it had no idea how to fulfill, let alone the means and the resources to actually fulfill it. 

3. Chasing Mispurposed Funding

I don't have the ratio on this, but for every startup that realizes a huge windfall from outside funding, there are startups for which that funding does more damage than good.

With term sheets on the table and influencers to pay, the startup started shedding more and more of its equity to keep the hype cycle going. Sure, some of that funding, maybe most of it, went to shoving the square peg of A.I. into the round hole of accessible mental health, but now the company goals had changed.

The moment a startup agrees to take money, whether from an angel, an incubator, a VC, or even a single large customer, it immediately seats a new boss at the company table. Furthermore, depending on the size of the infusion of new capital, that boss usually gets what it wants, regardless of -- and here's the dirty secret -- whether or not what it wants is good for the company and its customers.

The founder, now beholden to the desires of several new "bosses," found himself ultimately responsible for generating an outsized return on their investment, and furthermore doing it their way. The runway got shorter, the window started closing, and the mandate became "hit the numbers or else."

Reality chose "or else."

4. Chasing Revenue at All Costs

With or without funding, chasing revenue at all costs is usually a sneaky recipe for failure.

Aside from the ethics, I can tell you that the value of money is always dependent on time, and so revenue force-fed into a business model in the present usually comes at the expense of realizing greater revenue in the future. 

And if that trade-off is built on a house of cards of hype, connections, and funding tranches, the odds are always going to be stacked against that once-promising startup. That's exactly how it ended for the founder, who sunk everything into hitting the numbers now, and when that didn't happen, and after a failed attempt at bridge financing, he was forced to wind his company down.

The founder's story was a sad story, a common story, and a cautionary tale. But keep in mind it doesn't always play out that exact way. A startup's leadership can choose any one of those four false priorities, or any combination, and fall prey to the same result.

The solution is to always keep the startup's mission at the top of the priority list, and never start that slippery slope. It's easy on paper, but it requires real leadership in practice. Shiny objects will come at you constantly. The easy path of high-risk money from investors or customers with their own agendas will be a constant temptation. Journalists and marketing agencies will seemingly love your vibe, but in reality they just need your clicks. 

Stay on mission. Protect your priorities. It's not a guarantee of success, but it's your best chance.

BY JOE PROCOPIO, FOUNDER, TEACHINGSTARTUP.COM@JPROCO