Houstonian Joe Schurman's latest venture PhenomAInon is aimed at tapping into AI and data analytics for for space domain awareness and threat detection. Photo via Getty Images

As artificial intelligence continues to expand its sphere of influence, Spring-based expert Joe Schurman is looking to take this technology to an out-of-this-world space.

With his background includes working with advising defense and aerospace organizations like NASA, Schurman's latest venture PhenomAInon is perfectly aligned with what he’s been working towards since 2019. The company aims to be a multi-tiered subscription service and application that will be the world’s first cloud native data and AI platform for phenomenon-based data analysis that can analyze data from any source for space domain awareness and threat detection, according to Schurman.

The platform aims to provide end-to-end data and AI analysis, publish insights, build community, and provide cloud, data, and software consulting. PhenomAInon deploys data and AI services alongside modern data and AI engineering, per the website, to surface insights to explorers, researchers, organizations, publications, and communities through advanced data and AI analysis. Schurman has worked with the U.S. government's task force for unidentified anomalous phenomenon — any perceived aerial phenomenon that cannot be immediately identified or explained — known as UAPTF. The tool will run sensitive information and then get back custom video analysis. The public version of the tool will give the public the option to view videos and cases, and form their own analysis.

“We are working together with multiple teams both public and private to continue to curate the data sets, clear documents for public review, and provide advanced analytics and AI capabilities never seen before to the public,” Schurman tells InnovationMap. “From a data and analytics perspective, we are applying machine learning and advanced analytics to find correlations and anomalies in the incident reports across multiple data sets.

"Some of these are public, some are private, and some we are clearing for public review," he continues. "The analytics will go far beyond incident reporting and showcase heat maps, correlative incident maps to key private and public sector facilities, and trends analysis never reported — e.g. incident reporting correlated with time, weather, FAA, and drone flight data, etc. We also have a new content analysis platform where users will be able to eventually run their own AI and ML analysis on their own videos.”

Schurman was first able to show this to the world in 2019, when as an adviser for To The Stars Academy of Arts and Science, or TTSA. He also appeared on History Channel’s “Unidentified: Inside America's UFO Investigation” to show the Pentagon’s former Advanced Aerospace Threat Identification Program head and TTSA Director of Special Programs Luis Elizondo how the AI platform could be helpful in tracking data related to Unidentified Aerial Phenomena.

Now, PhenomAInon's app is a work-in-progress. While it soft launched in May of 2022, Schurman says they have several data sets that are awaiting clearing from the U.S. government, as well as the content analysis tool in development to launch possibly by the summer. Schurman also hopes they will curate the largest library of incident videos, images, and audio recordings.

The subject of UAP continues to attract new discussions from government officials and industry professions across aerospace, academia, and more. In Houston, Rice University's Woodson Research Center and its humanities department host one of the largest archives of UAP and paranormal data, notes, and research that include documents from CIA programs on remote viewing.

Schurman says he's looking to provide even more data and information in this space.

“This phenomenon, it’s implications to multiple aspects of our lives and possible security threats, all come down to a data problem and the organizations that have been in place to-date just have not had the level of cloud, data and AI engineering capabilities we take for granted and have access to in the private sector,” says Schurman. “My goal is to bring this all together, starting with PhenomAInon.”

nVenue's proprietary predictive analytics appear at the bottom right corner of the screen on Apple TV broadcasts. Photo via nvenue.com

This Houston-born sports tech is changing the game when it comes to fan-accessible data

by the numbers

Using technology to solve big problems has always been Kelly Pracht's career, but she never thought she'd be able use her skills for the sports world she's a lifelong fan of.

After spending nearly 20 years at HP Inc. in various leadership roles and across technology, Pract was watching a baseball game when something clicked for her. Baseball — and its endless data points and metrics — wasn't serving up analytics that the fans cared about. Teams and leagues had their own metic priorities, but fans just want to engage with the game, their team, and the players.

"I saw a gap in how we handle the data coming from the field and how that can impact the fan — and nobody was getting it right," Pracht, co-founder and CEO of nVenue, tells InnovationMap. "I saw technologists coming up with the most nonsensical solutions. For fans like me, coming from my crazy sports family from West Texas where my dad was a coach, I knew that these solutions were a huge miss."

She gives the example of a wearable technology for the viewer at home that can feel what it feels like for the players on the field who get hit. Pracht says it seems like companies were trying to fit technology into the sport, rather than thinking of what the fans really wanted.

She had the idea for a data-driven fan tool in 2017 and nVenue was born. She started building out the code and the team started testing it out at Astros games at Minute Maid.

"What great years to develop this platform. It was fun — these were not boring baseball games," Pracht says. The Astros have won their division four out of the past five years, including winning the World Series in 2017.

Kelly Pracht is the CEO and co-founder of nVenue. Photo courtesy of nVenue

At first, nVenue was using historical data, and that in itself was impressive. But then, Pracht and her team decided to take it live. After building its proprietary analytics platform, nVenue could use data to make predictions in real time.

"We spent over a year — all of 2019 — mastering timing and putting it into a platform," Pracht says, explaining how they built out the artificial intelligence and designed an app for fans to interface with. "We wanted to be able to predict and play. We had over 180 people during the 2019 World Series and playoffs."

The app and algorithm were good — and nVenue expanded into football. Then, the pandemic hit and sports halted completely. Pracht says they pivoted to a B2B model but wasn't seeing any real opportunities for the platform — until the 2021 Comcast NBCUniversal SportsTech Accelerator.

"In kind of a last-ditch effort, we applied to the NBC Comcast accelerator somewhere around August or September of 2020," Pracht says, explaining that she wasn't seeing a sustainable business so it was get into the program or close up shop. "And we got in. They just resonated with everything we said — we found our people."

The accelerator gave nVenue the jumpstart it needed, and as sports returned, the company found its momentum again. Now, the company is headquartered in Dallas with 14 employees all over and three — including Pracht — in Houston. The company has raised its $3.5 million seed round co-led by KB Partners and Corazon Capital and plans to raise a Series A next year.

After a few broadcasts last season, opportunity came knocking by way of Apple TV and Houston-based TV Graphics. The companies collaborated on a deal and, two weeks before the 2022 season started, nVenue got the greenlight to have onscreen analytics on Apple TV broadcasts.

"In under two weeks we structured the deal, convinced them it worked, pulled together every bit of testing we could — by then we only had one week of pre-season games to test — and we pulled it off," Pracht says.

The technology has tons of potential when it comes to sports betting, which is a growing business across the country. Pracht says nVenue isn't looking to compete with the providers on the scene, but instead work with them as an analytics tool.

"We broke down the market down to microbets or in-the-moment bets that are going to happen annually by 2025 — it's 156 billion microbets a year, which turns out to be 3 billion a week," Pracht says.

She adds that new technologies in the streaming world – like no-delay, latency streaming — is only going to make the sports betting world more lucrative, and nVenue will be right there to ride that wave.

A new, data-intensive technique can create a better profile of a firm and its profit forecast. Photo via Pexels

Focusing on data can enhance business forecasting, Houston researcher finds

Houston voices

Earnings summaries are the corporate version of a Magic 8 Ball, something used to forecast future performance and profit. But Rice Business professor Brian Rountree has found that magic has its limits, and that by delving into a few additional areas of interest, investors can get a more accurate prediction of a company's future earnings than current techniques allow.

Plenty of studies analyze how to use performance summaries to calculate a firm's potential and future profits. Building on the abundant literature around this approach, Rountree, working with colleagues Andrew B. Jackson of the UNSW Australia Business School and Marlene Plumlee of the University of Utah, devised a new, additional technique for forecasting profits. By dissecting an assortment of operating details, the researchers discovered, it's possible to create a more precise forecast of a company's financial future.

Rather than replacing prior work on the subject, Rountree's team delved deeper into the significance of details within existing data. Their focus: whether including a firm's market, its overall industry and any unique activity specific to the firm makes for a more reliable profit forecast. Their conclusion: Firms can indeed improve their predictions if they separate returns on net operating assets (RNOA) into separate components and use those figures in their projections.

Normally, firms use market and industry related data to create future profit predictions. For example, a major oil company might use data on market conditions and the overall state of the oil industry to build its profits prediction. The resulting financial literature might be peppered with statements such as, "Like the rest of big oil…" or "The overall market for oil remains soft."

While this type of data is typically used to make projections, Rountree and his colleagues used the market and industry information more formally by creating the equivalent of stock return betas — a statistical measure of risk — for corporate earnings. In addition, they allowed for adding firm-specific information to market and industry information to help forecast earnings.

To conduct their study, Rountree's team used Compustat quarterly data to calculate firm, industry and market RNOAs from 1976 to 2014. Next, they broke these figures down and separated the results into different categories.

Their resulting formula differs from the conventional approach because it doesn't rely on one average set of market and industry-related data for each firm. Instead, it assumes varying factors for each company. The devil is in these details: Calculating specific market, industry and firm-idiosyncratic components improves the chances of forecasting profits correctly.

Correctly breaking down and separating profitability details to plug into the new formula is no small task. Separating company data into just three components requires up to 20 quarters of figures about prior profitability.

Once the information is processed, a researcher must then be vigilant for "noise" — incidental, irrelevant data that can lead to errors. Finally, Rountree warns, the breakdown process may not work as well for forecasting bankruptcy as it does for profits.

Used correctly, however, the technique is a practical new tool. By breaking down profitability into market, industry and firm-specific idiosyncrasies, researchers can improve forecasts strikingly compared to conventional calculations of total RNOAs.

The most accurate profit forecasts in other words, demand more than just a figurative shake of an industry Magic 8 Ball. To find the most reliable information about future earnings, a company instead has to flawlessly juggle years' worth of specific details about their particular firm. But the reward of planning based on a correct forecast can pay for itself.

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This story originally ran on Rice Business Wisdom. It's based on research by Brian Rountree, an associate professor of accounting at Jones Graduate School of Business at Rice University.

InformAI can use its data technology to help doctors with preventative care and diagnoses. Courtesy of InformAI

Houston artificial intelligence startup aims to impact medical field

Data driven

A Houston-based startup has a new technology that allows hospitals and medical establishments better access to its own data – which translates into more effective diagnoses and preventative care.

InformAI — founded by Jim Havelka, CEO, in 2017 — is introducing the technology to the Texas Medical Center. Havelka saw a need within the medical industry for this type of service.

"There were several things missing," says Havelka. "One was access to very large data sets, because it wasn't really until the last five or 10 years that digitalization of data, especially in the healthcare vertical became more widespread and available in a format that's usable. The second convergence was the technology, the ability to process very large data sets."

InformAI currently offers four unique solutions using artificial intelligence and deep learning algorithms: Paranasal Sinus Classifier; Brain Cancer Classifier; Patient Outcome Predictors; and Surgical Risk Predictors.

According to the website, both medical image classifiers assist physicians in detecting the presence of medical conditions. The Paranasal Sinus Classifier detects and distinguishes medical conditions prevalent in the paranasal sinuses. The classifier assists physicians by evaluating sinus medical conditions at the point of care, speeding up radiologist workflow by flagging medical conditions for further review, and providing a triage of pending sinus patient study reviews. The Brain Cancer Classifier focuses on several tumor types and has the potential to provide radiologists and surgeons with additional insights to inform their diagnoses and treatment plans.

In addition to the classifier solutions, the predictors are also key to patient care, as InformAI patient outcome predictors evaluate the risk of adverse outcomes from a surgical procedure.

"Our data set has 275,000 surgical procedures that we can use to look for patterns, and then use that to understand how a patient may react to going through that surgical procedure and that's a very valuable input to surgeons," Havelka tells InnovationMap. Patient outcome risks include mortality, stroke, prolonged ventilation, infection, re-operation, and prolonged hospitalization.

"The innovation is the ability to use artificial intelligence to augment the capabilities of the physician and flag diagnostics for them to consider," says Havelka. "For example, one of our image classifiers that reads three-dimensional CT head scans has the equivalent of thirty lifetimes of an ENT contained in the AI labeled training dataset. It would take thirty lifetimes of an ENT to see that same number of scans and associated disease state patterns. InformAI currently has 10 full-time employees and works with radiologists-in-residence in building solutions and conducting research. The startup partners with the Texas Medical Center, Nvidia, Amazon, and Microsoft.

"They're quite interested in what we've built because it's really cutting edge technology that we're doing," Havelka tells InnovationMap.

Havelka and his team also work with some of the largest physician groups and imaging companies in the country to build products. "At the end of the day our core competency is the ability to take data, medical images or patient data, and put it into a usable format to assist physicians in making better treatment decisions for patients," says Havelka. "We can flag and detect patterns, disease states, and risk profiles that can improve the decision making of the physicians for the patient.

InformAI has plans to fundraise this year, with a goal of raising $5 million to $6 million in a round.

Luther Birdzell, founder and CEO of Houston-based OAG Analytics is on a mission to democratize data for his upstream oil and gas clients. Courtesy of OAG Analytics

Houston entrepreneur is using his analytics company to change the oil and gas industry

Featured Innovator

Luther Birdzell has been on a mission to democratize data for the upstream oil and gas industry since he started his company, OAG Analytics, in 2013.

For him, there's just not enough data scientists for hire to do the same thing internally for different companies. He thought of a way where he can give clients an easy-to-use platform to have access to data that could save oil and gas companies millions of dollars. So, that's exactly what he did.

"Over the past five and a half years, we've built that platform," Birdzell says. "We are currently helping to optimize over $1 billion in capital deployment around drilling and completions."

The company has grown to 25 employees and tripled its revenue last year. The team is forecasting another year of high grow for 2019.

Birdzell spoke with InnovationMap to talk about his start in software, the company's growth, and why nonprofit work has been important to him as a business leader.

InnovationMap: Did you always know you wanted to be an entrepreneur?

Luther Birdzell: When I was about two years old, my grandfather ran a meat business in New York City — in the meatpacking district, back when that area actually had meat packers. It just was in my bones from a really young age that I wanted to start a business.

IM: How did you get into software development?

LB: I studied electrical engineering in college. For my first seven years, I worked within consulting, implementing systems that made data more valuable to subject matter experts. I was primarily supporting management teams and mostly tech teams.

Then, I met the founders of iTKO, who were doing software testing for clients, and I helped them figure out a way that was complementary to what they were doing. We took a capability that can enable software developers that can help companies reduce their data center costs by a lot. It was a capability that was really restricted to specialized programing. Together we figured out how to make that a capability that anyone in an IT company used. That resulted in companies being able to higher fewer people to maintain servers, as well as reduce other costs. Companies were saving of millions of dollars per year per project.

IM: When did the idea for OAG come to you?

LB: Computer Associates bought iTKO from us in 2011. When I resigned from CA in 2013, it was very clear to me that artificial intelligence, big data, machine learning, and the cloud, were all tech ingredients for adding more value to data. Then the oil and gas business came into focus.

When I founded OAG Analytics, our mission then — and still is today — was to build a platform for the upstream oil and gas industry that enables them to manage their data, introduces world-class machine learning in minutes without having to write a single line of code, and allow them to run simulations on the resulting analysis.

IM: What makes OAG successful?

LB: My vision was to create a platform that could be trusted to support billions of dollars of capital optimization through transparency and control. A black box doesn't work for the kind of problems we're helping our customers optimize. They need something that's easy to use, simple, powerful, and also gives them complete control.

IM: What's the barrier of success for your clients?

LB: We have customers who have increased their capital efficiency on drilling programs that are about $500 million by over 25 percent, while still getting the same amount of oil out of the ground.

IM: What was the early reception like?

LB: We found a lot of interest in talking about how it works. In 2013, 2014, 2015, well over half the industry knew enough about this technology from other industries to have high confidence that it would affect the oil and gas industry one day. They were willing to spend an hour or two on what it is and how it works. But the number of companies who were really willing to invest in a meaningful way was really small.

There were companies, like EOG Resources, for example started spending millions of dollars developing this technology in house. Other companies seeing EOG and Anadarko success, raised the bar on the level of proof.

There's an increasing number of companies in the industry who realize that AI isn't a futuristic thing anymore. There are companies using it today, and the companies using it right are making more money. But, they're learning it's hard to do right. It could take years and millions of dollars to develop this yourself, but we're helping companies get up to speed in a matter of months, and our total cost for the first year is well under a million bucks to do this. They want us to train them how to use it, then act as support, rather than run it all for them.

IM: Do you plan to stay in just upstream oil and gas?

LB: We're 100 percent focused on upstream oil and gas, and always have been, but as we continue to grow, we're going to follow the market and what customers want. Repurposing our platform for other applications in oil and gas, energy, and even beyond that. We're evaluating. The vision has always been to democratize AI, and oil and gas is where we started.

IM: Do you have an exit strategy?

LB: As far as exits, I get asked this a lot. I don't believe in exit strategies. I believe in building a great company. I've seen a lot of founders make a lot of mistakes trying to cut corners to get to early exits. Our goal is to be a great company, and that starts with the right vision and then getting the right people and hires.

IM: How has Houston been as a place to have a startup in energy?

LB: Houston is unparalleled in the oil patch or the ability to support day trips. There's two airports and tons of direct flights to other cities in the oil patch. It's the only city you can cover all the other cities from with day trips. The efficiency of being able to be on site with customers is such an advantage.

There are a lot of industry experts in and around Houston, but a startup software company works very differently from an oil company. I think we have a long road ahead of us before we have an ecosystem in place to support startups and give them the best chance of success. Some of that comes from advisers, some from the ecosystem, and some part of it just takes time. But once those pieces come into play, talent follows. I think Houston is a very natural hub for energy tech.

IM: Volunteering is an important part of your business. Why is that something you've focused on?

LB: Something in the DNA of our business is giving back. We do that through direct community action. We've volunteered as a company, and we're always on the lookout for ways we can engage with and make the most contribution to the community. We do this primarily for personal reasons, but the universe has been very generous over my career with reciprocating a professional upside.

You volunteer in high school to get into college, then maybe some in college. And you might think, "oh that's for philanthropists or retired people and I'll get back to that later." But the reality of that is it feels better doing some of that now, so we do.

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Portions of this interview have been edited.

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Early-stage accelerator returns to Houston, announces finalists

prepare for take-off

CodeLaunch, a traveling seed-stage accelerator, is returning to Houston for its latest cohort.

The startup competition sponsored by software development company Improving will have its ultimate showdown on February 28. The final competition pairs six startups with six startup consulting companies.

Jason W. Taylor, CodeLaunch president and founder, says CodeLaunch isn’t your typical startup showcase, as it incorporates music acts, comedy, and crowd networking. Mirroring the set-up of a TV show, the six finalists all present their working products in front of an audience amid these performances.

“I would describe CodeLaunch as the next generation of venture-tainment in North America and the greatest startup show on earth,” Taylor explains.

The 2024 Houston CodeLaunch participant startups — and their mentor partners — are as follows:

Prior to pitch day, all six teams will receive hands-on instruction from CodeLaunch mentors on how to construct their pitches and free professional software development from their partners. Taylor says the strong relationships between CodeLaunch and these developers played a major role in setting the competition in Houston.

“We love Houston and we’re back for a third year in a row because the Houston startup ecosystem works together better than other major startup ecosystems I’ve seen,” Taylor says. “We have some great software development partners in Houston that are building code for those startups.”

Last year, Houston-based startup Energy360, with the mentorship and help of Honeycomb Software, took home the Championship belt and a $100,000 investment offer from Cyrannus VC fund for their energy management system Matt Bonasera, Energy360’s enterprise architect, says he is grateful for the entrepreneurial community CodeLaunch provides, in particular the team’s mentor Oleg Lysiak, Honeycomb VP of Partnerships and Business Development.

“I happened along this great community of people who are really passionate about supporting each other,” Bonasera says.

Lysiak agrees that CodeLaunch is an ideal opportunity for young entrepreneurs looking to hone their skills and expand their product capabilities. Lysiak says he is looking forward to defending Honeycomb’s title as top consultant development team.

“My whole philosophy is to connect people and have different collisions and collaborations,” Lysiak says.

Houston startup completes testing, prepares biosimilar insulin drug for clinical trials

next steps

A Houston biotech startup is one step closer to releasing its marquee drug for the global insulin market, which is projected to break the $90 billion threshold by 2029.

rBIO says it recently completed testing of the properties of R-biolin, an insulin drug that’s biologically identical to Novo Nordisk’s Novolin drug. The patent for Novolin about two decades ago. In March 2023, the Dutch drugmaker announced it was slashing the list price of Novolin by 65 percent to $48.20 per vial and $91.09 per FlexPen.

Executives at rBIO are now pursuing a partnership with a contract research organization to manage clinical trials of R-biolin. If those trials go well, R-biolin will seek approval to supply its insulin therapy to diabetes patients around the world.

Washington University in St. Louis is rBIO’s academic partner for the R-biolin project.

The rBIO platform produces insulin at greater yields that traditional manufacturing techniques do. The company is striving to drive down the cost of insulin by 30 percent.

About 38 million Americans have diabetes, with the vast majority being treated for type 2 diabetes, according to the U.S. Centers for Disease Control and Prevention (CDC). Many people with diabetes must take insulin to control their blood sugar levels.

Research company iHealthcareAnalyst predicts the global market for insulin will surpass the $90 billion mark in 2029.

“There has been a lot of talk in the media about reducing the cost of insulin for diabetic patients, but what is often overlooked is that the domestic demand for insulin will soon outpace the supply, leading to a new host of issues,” Cameron Owen, co-founder and CEO of rBIO, says in a news release.

“We’re dedicated to addressing the growing demand for accessible insulin therapies, and … we’re thrilled to announce the viability of our highly scalable manufacturing process.”

Professionals from the University of California San Diego and Johns Hopkins University established rBIO in 2020. The startup moved its headquarters from San Diego to Houston in 2022.

CEO Cameron Owen and Chief Scientific Officer Deenadayalan Bakthavatsalam work on insulin purification in the Houston lab. Photo courtesy

How AI is changing product management and what you need to know

guest column

For the past 14 months, everyone has been talking about ways artificial intelligence is changing the world, and product management is not an exception. The challenge, as with every new technology, is not only adopting it but understanding what old habits, workflows, and processes are affected by it.

Product managers — as well as startup founders leading a product function — more than any other role, face a challenge of bringing new life-changing products to market that may or may not be received well by their users. A product manager’s goal is complex — bring value, stay ahead of the competition, be innovative. Yet, the "behind the scenes" grind requires endless decision making and trade offs to inspire stakeholders to move forward and deliver.

As we dive into 2024, it is obvious that AI tools do not only transform the way we work but also help product managers create products that exceed customer expectation and drive businesses forward.

Market research and trends analysis

As product managers, we process enormous amounts of market data — from reviewing global and industry trend analysis, to social media posts, predictions, competition, and company goals. AI, however, can now replace hours, if not days, of analyzing massive amounts of data in an instant, revealing market trends, anticipating needs, and foreseeing what's coming next. As a result, it is easier to make effective product decisions and identify new market opportunities.

Competitive analysis

Constantly following competitors, reviewing their new releases, product updates, or monitoring reviews to identify competitor strengths and weaknesses is an overwhelming and time consuming task. With AI, you can quickly analyze competitors’ products, pricing, promotions, and feedback. You can easily compare multiple attributes, including metrics, and identify gaps and areas for improvement — all the insights that are otherwise much harder to reveal quickly and efficiently.

Customer and product discovery

Of course, the most intuitive use case that comes to mind is the adoption of AI in product and customer discovery. For example:

  • Use AI for customer segmentation and persona creation to help visualize personas, prioritize user motivations and expectations, and uncover hidden behavior and needs. You can then create and simplify customer questionnaires for interviews and user groups and target customers more accurately.
  • Analyze quantitative and qualitative data from surveys, support tickets, reviews, and in-person interviews to identify pain points and unmet user needs and help prioritize features for future updates and releases.

Roadmap and sprint management

AI provides value in simplifying roadmap planning and sprint management. Resource optimization is often a gruesome task and AI can help with feature prioritization and resource allocation. It helps teams focus on critical work and increase their productivity. You can even analyze and manage dependencies and improve results across multiple sprints months in advance.

Prototyping and mockup generation

There is no product manager’s routine without multiple mockups, wireframes, and prototypes that explain concepts and collect feedback among stakeholders. AI has become a critical tool in simplifying this process and bringing ideas to life from concept to visualization.

Today, you can use textual or voice descriptions to instantly create multiple visuals with slight variations, run A/B tests and gather valuable feedback at the earliest stage of a product life cycle.

Job search and job interviews

Consider it as a bonus but one of the less obvious but crucial advantages of AI is using it in job search. With the vulnerable and unstable job market, especially for product roles, AI is a valuable assistant. From getting the latest news and updates on a company you want to join, to summarizing insights on the executive team, or company goals, compiling lists of interview questions, and running mock interviews, AI has become a non-judgmental assistant in a distressing and often discouraging job search process.

Use AI to draft cold emails to recruiters and hiring managers, compare your skills to open positions’ requirements, identify gaps, and outline ideas for test assignments.

We already know that AI is not a hype; it is here to stay. However, remember that customers do not consume AI, they consume your product for its value. Customers care whether your product gets their need, solves their problem, and makes their lives easier. The goal of a product manager is to create magic combining human brain capabilities and latest technology. And the best result is with a human at the core of any product.

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Natasha Gorodetsky is the founder and CEO of Product Pursuits, a Houston company that helps early stage and venture-backed startups build products and create impact.