• Rohan Doctor was a managing director at Goldman Sachs when he founded Louisa AI.
• The startup uses AI to feed deal ideas and networking prompts to bankers and investors.
• Here's why he wants to bring the dealmaking playbook to startups.

Cold call after cold call, Rohan Doctor wasn't getting as far as he would've hoped.

The former Goldman Sachs managing director had emailed a list of digital strategy execs at banks and private equity firms to try to sell them on his startup, Louisa AI. But he only got a handful of replies back.

Two years since its launch, Louisa AI has secured about a dozen clients, including some of the biggest names in corporate America. They include Goldman Sachs, VC firm Insight Partners, and, more recently, one of the biggest AI chipmakers and a top consulting firm. But he didn't secure those contracts from cold outreach. He used his own startup's technology, which proactively prompts deal ideas based on people's personal and professional connections, to get in through the front door.

Now, Doctor wants to bring his dealmaking playbook to other startups ahead of an anticipated M&A boom.

"If we're able to close more deals through warm relationships this way, then other startups can, too," he said.

The near-term outlook for M&A activity has gotten brighter, with lower interest rates reducing the cost of borrowing. Wall Street execs are optimistic that Trump's return to the White House, and any business-friendly regulations that may come with it, will be a tailwind for dealmaking. Also, companies resetting their valuations could spur more transactions to close as price expectations align between buyers and sellers.

Meanwhile, in Silicon Valley, VCs and founders are hopeful about the anticipated looser environment, which could boost tech building and dealmaking. VCs, which rely on selling startups in M&A deals for many of their returns, have been dampened by the Federal Trade Commission's antitrust stance on M&A.

Louisa AI was built to suggest potential deals based on the data it's exposed to. It ingests information about who and what employees know by plugging into company CRMs, messaging platforms like Slack and Symphony, and email providers. Since spinning out of Goldman Sachs in 2023, Louisa AI has raised $5 million in seed funding. It suggests about $1 billion in deal values per quarter, Doctor said.

It also highlights mutual connections to establish a warm introduction, which can make all the difference in the multi-billion investment banking industry built on relationships. While running the bank-solutions group at Goldman Sachs, Rohan Doctor used his network to close transactions worth tens of millions of dollars. As a startup founder, it's been a different story.

"I've tried the cold outreach and just emailing," Doctor said, adding that the startup stopped doing that after it didn't yield good results. What has worked for Doctor is realizing he knows someone who knows someone.

Louisa AI scored the chipmaker contract after the AI flagged that one of Doctor's staff used to work for someone who now worked at the chip manufacturer. With the consultancy, one of Louisa AI's investors connected Doctor with the consulting firm they used to work for. He declined to name these firms due to non-disclosure agreements.

"Everything needs to be warm when it comes to big companies doing big things with other people. It has to rely on trust," he said.

• Macroeconomic signs, like lower interest rates and a new administration, point to a rise in M&A.
• Bankers anticipate more AI dealmaking to benefit data and infrastructure companies.
• Execs from Goldman Sachs, Bank of America, and Axom Partners outline their 2025 predictions.

AI offers the promise of a dealmaking gold rush in years to come, and investment bankers are looking to cash in on deals involving data and infrastructure companies selling the proverbial pickaxes and shovels.

"Everybody's rightly caught up in the lore of AI and what the industry leaders are doing regarding building out platforms, but people forget; unless you have good data management and good data integrity, you can't fully deploy any application of AI," said Neil Kell, Bank of America's chair and global head of technology, media, and telecom for equity-capital markets. "Companies that are looking to be acquisitive are focused on this," he said.

Brandon Hightower, a founder and partner at the tech-focused M&A advisory firm Axom Partners, attributes an increase in AI-themed deals to "an arms race" earlier this year around infrastructure and talent.

Those are two themes that are expected to see continued momentum, according to interviews with four AI bankers. Tech companies focusing on managing, moving, and securing data will be at the forefront of the AI M&A wave. Other "pickaxe and shovel" companies include those focusing on developer tools and resource optimization. AI dealmaking is also poised to touch non-technology sectors, like customer service, commerce, and industrials.

While there hasn't been a lot of dealmaking activity among pure-play generative AI companies, tens of billions have been spent on technology companies that are building infrastructure around AI like storage, server, cloud, and software companies, according to Scott Denne, a principal research analyst at S&P Global Market Intelligence.

In 2024, some $82 billion was spent on AI- and AI-related acquisitons, up from about $55 billion in 2023, according to data from 451 Research, an arm of S&P Global Market Intelligence. This includes purchases of companies selling AI products or products built with AI, as well as companies that sell software, hardware, and other tech to support AI development and deployments.

There are several reasons the timing may be right for companies to shop around. Lower interest rates have reduced the cost of borrowing. The gap between buyer and seller price expectations is shrinking as companies, AI ones included, reset their valuations. Election uncertainty is behind us, and president-elect Trump's pick of Andrew Ferguson to run the Federal Trade Commission is looking like a positive for Big Tech, according to Wedbush Securities.

Ferguson was tapped "at a key time in the AI arms race in which we expect the strong to get stronger as Mag 7 gets the engines started up again on M&A," Wedbush analysts wrote in a note to clients Wednesday.

All eyes on data

Because generative AI is still relatively nascent, bankers are focused on the raw ingredients that are critical to AI models'success.

"Two of the most interesting areas to watch next year will be data infrastructure, management, and analytics companies. The second is developer tools," Jung Min, the co-COO of Goldman Sachs' TMT division, told BI.

Also important is data security, according to Bank of America's Kell, which he said continues to be very relevant and vital for those companies thinking about M&A.

Eventually, the front-facing application layer will be the biggest area, he said. But "in that creation phase, the infrastructure and the developer tools, those are the things that really matter first" after the models are trained, Min said.

That's because companies are trying to figure out "how to get better quality data" and "more efficient flows of data," Axom's Hightower said.

It's something even the biggest AI companies are opening their wallets for. When Axom worked on the sale of database analystics firm Rockset to OpenAI earlier this year, the deal came down to enabling faster data retrieval and improving the data pipeline, according to Axom cofounder Alan Bressers.

The focus on data infrastructure is spotlights two big-data giants Databricks and Snowflake, which respectively have made a handful of acquisitions related to data and AI this year.

"If you have data and if you own the models, those are two key components. How do I bring those together? And if you've got Databricks and Snowflake holding a lot of enterprise data, that's a natural place for a future winner in the AI world," he said.

Optimizing the back end

A need to scale is also driving tech companies to acquire infrastructure players, Axom's Hightower said. He pointed to Nvidia's purchase of Seattle-based OctoAI, a deal that Axom worked on "so that it can scale and do certain aspects of AI workflows in a more efficient manner," he said. It's at least Nvidia's second acquisition this year with an eye toward scalability.

Earlier this year, Nvidia set out to buy Run:ai. The deal, which is still in the regulatory approval process, could help the chipmaker run compute more economically by allowing more work to be done on fewer chips.

There's also a greater focus on lowering inference costs, essentially the price of asking an AI models a question and having them generate a response. While it's well known that training LLMs can come with astronomical prices, getting them to beam back an answer might come at an even steeper price. It's something that AI adopters are learning the hard way, prompting a "very near-term thing where you can see M&A because people can say there's real dollar value from inference savings," Axom's Bressers said. He noted potential buyers interested in this part of the tech stack could be the newer generation of cloud companies, like CoreWeave or Lambda.

Some cross-sector action

While most of the AI and AI-related deals will likely be between tech companies, Goldman's Min anticipates some transactions in the industrial space.

"Companies that already automate or help automate the supply chain, a lot of those interaction are already software to software or machine to machine, so those are ripe for AI to really deploy there," he said.

Other fertile grounds for acquisition going into 2025 include customer service and customer-relationship management companies, potentially spurred by some of Salesforce's recent AI ambitions, Axom's Hightower said. This year, the CRM giant Salesforce made "a hard pivot" to AI agents with a product that lets clients build their own custom ones to interact directly with clientele. Competitors like ServiceNow, Braze, Klayvio, and HubSpot to broaden their suite of solutions and add data and data connectivity to their offerings, he said.

• McKinsey helps banks and financial institutions with their generative AI efforts.
• It outlined the dos and don'ts of seeing a return on AI investments in a report.
• Business Insider spoke with McKinsey's Larry Lerner about what will separate winners and losers.

The bill is coming due for Wall Street banks' AI investments.

It's been two years since generative AI captured the attention and dollars of bank leaders. They amassed teams of technologists to experiment with generative AI and run proofs of concepts. Some of those have since scaled to enterprise-wide initiatives used by thousands of employees. Now, leaders are beginning to question when these investments will pay off.

"That is the $20 billion question," according to Larry Lerner, a partner in McKinsey's banking practice.

For a handful of firms, Lerner said tangible returns are starting to emerge in the form of current cost savings, future cost avoidance, and incremental revenue. But for many, the reality is "POC purgatory," Lerner said, referring to proofs-of-concept pitfalls where firms get stuck in the experimentation phase and "become very tepid about really leaning in." In those cases, the "institution has spent the last two years investing and investing and not seeing anything at all," Lerner said.

According to an October report from Evident AI, which tracks AI adoption in financial services, only six out of 50 banks disclosed dollar-level cost savings or revenue lifts as a result of their AI investments.

So, what separates the frontrunners from the laggards? According to fresh research from McKinsey, it can come down to a few key decisions around concentrating efforts on a couple of uses, having CEO buy-in, and using generative AI in conjunction with other technologies. Most of all, it'll involve a mindset shift where AI is viewed and treated as a business opportunity rather than a technological problem, Lerner said.

Lerner outlined what will separate the winners from the losers. He declined to comment on specific companies.

Viewing AI as a business problem, not a tech one

Leadership teams have to recognize that generative AI is a business opportunity, not just a technology play, Lerner said. Because of that, he said business leaders should bear the brunt of the accountability, rather than that responsibility falling solely on tech leaders' shoulders.

"The institutions that make business leaders accountable for delivering their results will over time tend to do better because there's a much stronger partnership," Lerner said.

Concentrating firepower

Generative AI has lead to more value when there are only a handful of use cases, instead of every business unit doing a little bit here and there and seeing what sticks, Lerner said.

"Instead of having 60 use cases across 15 different business lines and functions, narrow down to three areas where you want to go deep," where you're reimagining the entire domain or workflow has led to a faster path to value, Lerner said.

Choose areas where ROI can actually be tracked

It's becoming increasingly clear that generative AI's main strength in saving workers time can't always be traced back to bottom-line impact, which is leading to some frustration in the boardroom.

"The value of what you're doing depends on how you're going to repurpose your time, and that's really hard to do," Lerner said. "Because it's an indirect sort of lever, it's very difficult to actually measure and get people to agree that there's value."

On the other hand, AI tools like call-center copilots and AI-powered marketing campaigns that improve the customer experience can generate incremental value that is measurable, Lerner said. One large bank referenced in the McKinsey report is projecting a 10% revenue increase thanks to a new analytics platform to target new customers and cross-sell products to existing ones.

For buy now, pay later fintech Klarna, leveraging an OpenAI-powered call center agent is estimated to bring some $40 million in profit this year, the company said in a blog post earlier this year. At the time, the AI was doing the work of 700 full-time agents, according to Klarna.

Lerner said he's starting to see some banks modify forward-looking hiring plans, especially in the contact center, thanks to the increase in self-service and faster resolution times. "That cost avoidance is absolutely measurable," he said.

Reusability is key

Build something once and redeploy it a hundred times, Lerner said. Doing so can accelerate development times and let companies scale faster because the tool has already gone through the required risk, security, and compliance approvals, he said.

Execution will come down to adoption

Getting workers and customers to adopt a new way of doing something or a new technology is one of the most important parts of the value equation. It's an old challenge that banks have had with previous technology cycles. When it comes to AI, "most companies have done a pretty bad job of getting adoption to the level that's going to yield the results that they want to yield," Lerner said.

• Reflexivity is a startup cofounded by two former hedge fund traders.
• It sells software to hedge funds and institutional investors to speed up the research process.
• The 4-year-old upstart just raised its $30 million Series B.

A startup looking to transform how investors and traders use data just received funding from some of the biggest names in the hedge fund world.

Reflexivity, formerly known as Toggle AI, raised its $30 million Series B in late October. Interactive Brokers and Greycroft led the round, which included participation from billionaire investor Stanley Druckenmiller and Greg Coffey, the Australian founder of hedge fund Kirkoswald. Existing investors include Millennium Management's founder, Izzy Englander, and General Catalyst.

Reflexivity was founded by two former hedge fund traders who were all too familiar with the woes of wrangling disparate data sets to find an investing edge, or at the very least, to not miss out on an opportunity others are seizing. The startup aims to mitigate that by combining third-party data from a dozen providers like S&P Global and newsfeeds, in addition to proprietary internal information, for a full-view analysis and also flagging the potential impact world and market events may have on a portfolio.

"When you are an investor inside a major hedge fund, one big fear that is always present is that you are going to miss something," cofounder and CEO Jan Szilagyi told BI. "It's always exciting to have a trade that you are the only one that's in it, but the thing that is far worse is to miss on the trade that everybody else but you is in."

The four-year-old startup recently changed its name to more closely align with how the platform helps with the investment process, Szilagyi told Business Insider.

"Reflexivity is the act of examining one's own assumptions, beliefs, and judgment systems, and thinking carefully and critically about how these influence the research process," he said, referring to a term popularized by legendary investor George Soros.

Szilagyi was a portfolio manager for nearly 20 years at firms including Druckenmiller's Duquesne Capital Management and Fortress Investment Group. The fintech's president and other cofounder, Giuseppe Sette, also worked in asset management including a stint at macro giant Brevan Howard. They remember the investment analysis process as one that "seemed hopelessly broken" because critical data sources were fragmented and spread out across different providers and systems, Szilagyi said.

The firm estimates the potential market for its services is $16.4 billion. Reflexivity so far has about 20 institutional clients, tallying some 15,000 individual users. Clients include trading platforms like Interactive Brokers, banks, including Japan's largest in MUFG, and several hedge funds, including Millennium Management, Soros Fund, and ExodusPoint. The startup was highlighted as one of Business Insider's up-and-coming fintechs in 2023.

It has a valuation between $115 million and $150 million, Szilagyi said.

How Reflexivity works

The upstart's platform lets stock-picking investors analyze data that covers about 40,000 securities from a dozen different providers, including Refinitiv and the London Stock Exchange Group, the Federal Reserve, and S&P Global. It is also built to alert customers, mostly discretionary investors who work at hedge funds and asset managers, to market events and their potential ripple effects on a given portfolio.

If there's a big move in treasury yields, Reflexivity will automatically examine the ripple effects and see how that could impact banking stocks. In this hypothetical example, Reflexivity would see that its user, say a hedge fund trader, has Wells Fargo stock in her portfolio, and flag to her that Wells Fargo stock historically reacts very well to a rise in yields.

Behind the scenes, a proprietary knowledge graph and generative AI-powered user interface helps users connect the dots and better understand investing relationships, Szilagyi said.

Szilyagyi says he also has an answer to a question many Wall Street technologists are facing with hallucinations, or generative AI's tendency to make up answers that are presented as fact.

Reflexivity's answer is a so-called closed system, wherein the AI models can only pull answers from data that's been pre-vetted by the startup. The reason other models, like OpenAI's ChatGPT, hallucinate is because it operates on an open system that takes in data from anywhere on the internet, Szilagyi said. If it can't find anything, it'll be inclined to make up an answer because these tools are built to deliver some kind of answer, he said.

On top of that, Reflexivity also programmed its models to not force an answer to every question. About 5% of the time, Reflexivity will say it doesn't have the ability to answer a given question if it's unable to generate an answer from the data it's been given, Szilagyi said.

"For finance professionals, the ability to get the candid and honest answer is absolutely critical because it only takes one, two hallucinations to be extremely costly when it comes to trading," Szilagyi said.

Here's Reflexivity's pitch deck it used to raise its Series B.

(Because the startup only recently changed its name, these slides include its former name, Toggle AI.)

• Business Insider asked 27 venture-capital investors to nominate the most promising fintechs.
• Fintechs using AI to help Wall Street firms, bankers, and consumers lead this year's series.
• Here are the top 15 AI fintechs, according to VCs.

Fintech investors still see at least one bright spot in the industry, despite funding to the sector hitting one of its lowest points since the pandemic.

Total funding to fintechs fell once again last quarter, according to CB Insights' third-quarter data. Only 753 deals were inked, notching the lowest quarterly level since 2017.

However, the dealmaking drought could ease up in the next year as a result of antitrust scrutiny softening and VCs might be more willing to open up their pocketbooks. One area that investors will likely hone in on once they do is AI.

Earlier this year, Business Insider asked dozens of VCs to identify the most promising fintechs to watch. Nearly one-quarter of the startups they named are leveraging AI as a key part of their offering. Indeed, it is difficult to point to one area of finance where AI startups aren't threatening to change the way people bank, invest, save, and work.

Some of the startups on this list are business-facing, helping dealmakers negotiate debt agreements, streamlining tedious processes for junior bankers, or automating manual processes for accountants and CFOs. Others use AI to serve consumers, whether it's helping them figure out the best way to pay off debts or maintaining access to healthcare between jobs.

The startups named haven't raised beyond a Series C and include a mix of investors' portfolio companies and ones they have no financial interest in.

Here are 15 of the most promising AI fintechs to watch, according to top VCs.

BeatBread

Cited by: Deciens Capital (investor)

Total raised: More than $150 million

What it does: BeatBread uses AI to analyze and predict revenue potential for the music industry, providing funding advances to a broad range of artists.

Why it's on the list: "Artists of all sizes want independence and ownership over their music, to work with their preferred partners, and to control their own destinies. Historically, there hasn't been a real alternative to the major label advance for artists to get the capital they needed to scale their careers, which locks them into the label ecosystem," Dan Kimerling, the managing partner at Deciens Capital, said.

"2024 has been a pivotal year for BeatBread, marked by strategic moves and partnerships that further solidify its mission," Kimerling said, referring to its partnerships with the administrative publishing company Kobalt and its subsidiary AMRA to offer artists increased royalties and faster payments. Other strategic moves include a series of deals providing funding to independent labels to expand how BeatBread provides capital to artists.

Brico

Cited by: TTV Capital, Homebrew

Total funding: $8.1 million

What it does: Brico helps financial institutions and fintechs manage their licensing by using AI to automate applications and renewals.

Why it's on the list: "With Brico, businesses can effortlessly navigate the complexities of acquiring, renewing, and managing compliance for various financial licenses — including Credit, Money Transmitter, Mortgage Loan Originator, and more — in all 50 states," Lizzie Guynn, a partner at TTV Capital, said. "Brico makes regulatory compliance seamless and cost-effective with its user-friendly tools that reduce time and money spent on financial licenses."

"It's addressing a very manual and expensive process that nearly every financial services company needs to deal with on an annual basis," Satya Patel, a partner at Homebrew, said.

Cascading AI

Cited by: QED Investors, Vesey Ventures

Total raised: $4.1 million

What it does: Cascading AI, through its main product Casca, offers loan-origination software for the banking sector with an integrated AI assistant that allows firms to extend their hours.

Why it's on the list: "Customers do not operate on the 9-to-5, Monday-to-Friday schedules that banks do," Laura Bock, a partner at QED Investors, said. "When a pizzeria's oven breaks, the owner is inquiring about a loan after closing shop. While today, it might take nearly three days to hear back from a loan officer after submitting an application, financial institutions using Casca's AI platform are able to unlock 24/7, 365 support for current and potential customers."

Dana Eli-Lorch, a founding partner at Vesey Ventures, said: "Their flagship product, an AI-powered loan assistant, enables manifold increases to banks' productivity and loan conversion rates, all while enhancing both accuracy and applicant experience. Casca exemplifies the powerful impact AI can have on financial services, driving significant operational efficiency and customer satisfaction."

Clerkie

Cited by: Flourish Ventures (investor)

Total raised: $41 million

What it does: Clerkie embeds its AI debt-automation software in financial institutions' mobile apps, allowing consumers to make financial decisions about their debts and discover solutions if they're struggling to pay them off.

Why it's on the list: "Clerkie's data flywheel and network create a win-win scenario for both consumers and financial institutions. Consumers enjoy a seamless experience within their banking app, with flexible solutions tailored to their specific cash flow needs, helping them avoid the collections process and protect their credit scores. Banks benefit from direct ROI through loan repayment while maintaining customer relationships," while also expanding loan-to-value ratios, Flourish Ventures' Emmalyn Shaw said.

She added that "Clerkie assumes no balance-sheet risk, serving as the debt-network and debt-payment infrastructure for financial institutions."

Comulate

Cited by: Pathlight Ventures (formerly Exponent Founders Capital)

Total raised: About $5 million

What it does: Comulate automates insurance statement processing, reconciliation, revenue recovery, and forecasting.

Why it's on the list: "Leveraging AI to drive real revenue lift for insurance carriers is driving success in a category" that's historically been hard to break into, Charley Ma, a cofounder and managing partner of Pathlight Ventures, said.

Coris

Cited by: Pathlight Ventures (investor)

Total raised: $3.7 million

What it does: Coris builds software for fintechs and other tech companies to manage risk and fraud among small- and medium-size business clients.

Why it's on the list: "Aggregating unstructured data on SMBs to generate insights at scale is challenging. Coris is at the forefront, leveraging a variety of methods across LLMs, ML, and good old-fashioned software to establish itself as the leading platform for managing SMB risk and fraud — already working with clients like Mindbody and ClassPass," Pathlight's Ma said.

Fintary

Cited by: Harlem Capital (investor)

Total raised: $2.5 million

What it does: Fintary helps insurance companies manage their finance and accounting needs by using AI to automate workflows.

Why it's on the list: "They have been invited to their customers' conferences in order to share the product with their customers' customers," Henri Pierre-Jacques, the cofounder and managing partner of Harlem Capital, said. He said Fintary has grown more than 10 times since Harlem's investment last fall, adding that "the quick ramp has been one of the fastest we've seen for a preseed company."

Greenlite

Cited by: Greylock (investor)

Total raised: $4.8 million

What it does: Greenlite automates compliance processes using AI for fintechs and banks.

Why it's on the list: "Greenlite has seen exceptional customer demands with enterprise banks and fintechs and has proven one of the few enterprise-grade applications for generative AI — automating tedious compliance workflows like alert handling, periodic reviews, and document processing, improving efficiency and reducing human error," Seth Rosenberg, a general partner at Greylock, said.

Iris Finance

Cited by: Redpoint Ventures

Total raised: $3.5 million

What it does: Iris Finance offers consumer-facing companies AI-powered financial planning and analysis software.

Why it's on the list: "While the notion of AI bookkeeping is very much in vogue today, replacing Quickbooks is hard — and not something most brands or outsourced accountants are looking to do in the near term," Redpoint's Clark said. "Iris, instead, complements Quickbooks with a more holistic AI-powered CFO-in-a-box for brands, enabling them to seamlessly track and improve day-to-day sales and margin performance across channels, which much more closely aligns with what founders want and how modern brands are managed."

Materia AI

Cited by: Bain Capital Ventures

Total raised: $6.3 million

What it does: Materia AI helps accountants organize their data, enabling them to automate parts of their work.

Why it's on the list: "With a decline in new auditors and an immense volume of manual data entry, professional-service audits are the perfect place for an AI copilot," Alysaa Co, principal at Bain Capital Ventures, said. "LLMs enable the automation of work like ingesting large sets of unstructured financial data, searchability, comparing against historicals and across the industry, and direct citations for where the data comes from."

Nilus

Cited by: Vesey Ventures (investor)

Total raised: $8.6 million

What it does: Nilus offers an AI-powered cash and treasury management platform for fintechs, financial firms, marketplaces, and other companies moving money.

Why it's on the list: Nilus "provides better data connectivity combined with AI to transform the CFO suite: a trend we are actively investing behind," Lindsay Fitzgerald, a general partner and the cofounder of Vesey Ventures, said.

"With Nilus, treasurers can skip the manual reconciliation work that previously took most of their day and focus on actions that can drive bottom-line impact. We think Nilus is poised to become the default software for modern treasury teams, displacing decades-old workflow tools like Kyriba and GTreasury," she said.

Noetica

Cited by: Avid Ventures, Index Ventures

Total raised: $7.85 million

What it does: Noetica helps deal professionals negotiate debit agreements with their data using an AI platform that benchmarks terms in corporate debt transactions.

Why it's on the list: "Noetica is a capital-markets data company for corporate debt, a market valued at trillions of dollars. Its AI-powered software allows professionals to upload any credit or bond document and compare all terms to similar public and private deals," Jahanvi Sardana, a partner at Index Ventures, said.

"Corporate debt terms are time-consuming and difficult to benchmark, leading deal professionals, such as lawyers and investment managers, to often miss higher-risk terms, as well as opportunities for negotiation. By building the largest proprietary dataset of corporate debt terms, Noetica is changing how these deals are negotiated and transacted," Tali Miller, a founding investor at Avid Ventures, said.

Novella

Cited by: Avid Ventures (investor)

Total raised: $2.5 million

What it does: Novella is an AI-powered insurance wholesaler specializing in excess and surplus insurance, which addresses higher-risk situations that standard carriers don't usually cover.

Why it's on the list: "Given the complexity of E&S insurance, it is sold through wholesalers who have relationships with specialty carriers, and retail brokers must work with these wholesalers to access these carriers. However, brokers have been frustrated by the leading wholesalers such as Ryan Specialty, Amwins, and CRC Group, whose lack of technology and system integrations lead to slow, inefficient, and opaque quoting processes," Avid Ventures' Miller said.

"The E&S market continues to grow," Miller said, adding that E&S direct premiums written in the US climbed to more than $86 billion in 2023, more than doubling since 2018.

"Using data and AI, Novella aims to reinvent this massive industry by making the information transfer between brokers and carriers fast and error-free and, ultimately, automating quote creation," she added.

Rogo

Cited by: Two Sigma Ventures

Total raised: $26 million

What it does: Rogo is building a generative AI assistant to help investment bankers and analysts do their jobs more efficiently.

Why it's on the list: "Rogo's platform is purpose-built for the complex data needs of the financial sector, allowing nontechnical users to query vast amounts of financial data using natural language processing. This is a game changer for institutions like banks, investment firms, and insurers," Frances Schwiep, a partner at Two Sigma Ventures, said.

"I see immense potential in Rogo's ability to give first-of-its-kind access to critical financial analytics, positioning them as a key player in transforming how financial institutions interact with their data to drive more informed decisions across the industry," she added.

See the pitch deck for Rogo's $7 million seed.

When

Cited by: TTV Capital (investor)

Total raised: $7 million

What it does: When uses an AI assistant to help exiting employees maintain access to healthcare by providing affordable alternatives to COBRA and making it easy to compare pricing and deductibles.

Why it's on the list: "There are more than 700,000 companies in the United States with 20-plus employees, which means they are required by law to offer COBRA. Last year's 721,677 planned job cuts brought some of the largest reductions in company head count that we've seen in the past two decades," TTV Capital's Guynn said.

"Offering an alternative to expensive, inflexible COBRA not only makes common sense but also economic sense. COBRA participants are three times more costly than active employees, which is especially burdensome for self-insured companies. To date, companies that offer When's fixed-dollar health-insurance premium reimbursement have seen an 80% conversion rate from COBRA. Employees that applied their When benefit to available plans have saved as much as 50% in out-of-pocket healthcare costs," Guynn said.

• Business Insider selected 25 young professionals, 35 and under, as its rising stars of Wall Street.
• We asked these up-and-comers what TV show, book, or movie best represents the finance industry.
• They shared some parallels and even pointed to works about nonfinancial subjects.

There's no shortage of colorful characters depicting Wall Street. There's the serial-killer investment banker, the corporate raider who declares that "greed is good," and the crooked, if charismatic, stockbroker, to name a few.

Two of those are fictional movie characters, and one was based on a real person, but they've all shaped the public's perception of what working on Wall Street could be like.

If you ask successful people at some of the biggest banks, asset managers, trading firms, or hedge funds whether they see their reality accurately perceived on the screen or in books, they'll tell you that working on Wall Street is a little less colorful than it's often painted to be.

"I don't know that there's a great movie or book depicting life on Wall Street," Mark Zhu, 34, a managing director at Blackstone, told Business Insider. "The day-to-day is a lot more boring than you think. It's a lot of calls and a lot of emails. There's not as much flamboyance or out-there behavior. It's almost not movie-worthy. Why would you pay money to watch somebody just sit in front of a computer doing Zooms?"

So maybe they think all that partying on HBO's show about twentysomething investment bankers, "Industry," is a little overdone, but there are still some elements the entertainment industry gets right occasionally.

We asked up-and-comers on Wall Street about the shows, movies, or books that best represent their daily lives. While no one representation was perfect, the young professionals talked about some of the parallels they saw. Some even shared some nonfinancial references that give a window into their world.

Here are the shows, movies, or books that give a flavor of what it's like to work on Wall Street.

Shows: "Industry"

The hit TV show "Industry" — full of sex, drugs, and spreadsheets — just wrapped up its third season.

"My friends in the last few years have nonstop bothered me about 'Industry,'" Justin Elliott, 29, a vice president of institutional rate sales at Bank of America, said.

"They see a crazy show about the industry and say, 'My God, I can't believe that happens in your world every day.' From what I've seen, there's definitely some thrills from getting a trade done that might mirror the show a bit, but it's a very exaggerated depiction of life on Wall Street."

"I don't know that any of them do a great job, but I am quite a fan of 'Industry,'" Erica Wilson, a vice president at the private credit firm Blue Owl, said. "I am still behind on the third season, but I think that show is fun."

"Succession"

Though the blockbuster show "Succession" isn't specifically about the banking industry, Daniela Cardona, a 29-year-old investment banker at RBC Capital Markets, watched it in its entirety and found some similarities in high-stress moments.

"In the last season, when they're trying to merge the two companies, there's one scene that always makes me giggle. I don't think this is fully accurate, but I do think it's funny — they're in a conference room, and Kendall says, 'Just make it up!' and they're all with their laptops sitting in the middle, and the consultants are looking at him like, what do you mean, make it up?" Cardona said.

"There have been instances where it sometimes feels that way — where you're in a time crunch and it's 3 o'clock in the morning."

"Scrubs"

Ben Carper, a 34-year-old managing director at Jefferies, pointed to the medical comedy sitcom "Scrubs" as a better representation than anything that features board rooms and trading floors.

He said the show had a "similar high-pressure environment where there are some opportunities for amusement and humor, but generally a pretty vigorous focus on doing a job well done."

Movies: "Margin Call"

The 2011 drama "Margin Call" follows the 24 hours after an analyst at an investment bank discovers it has taken on more debt than it can handle — illustrating the early stages of the 2008 financial crisis.

"I think it picks up the cadence of working at a big bank the best," said Austin Anton, 32, a principal at Apollo Global Management.

"The Wolf of Wall Street"

"The Wolf of Wall Street" follows the story of Jordan Belfort, who actually only worked at a Wall Street firm for a few months before the 1987 stock-market crash. He goes on to run his own brokerage, which ultimately scams several people, but the movie highlights the debauchery, opulence, and excess that ensued during his run.

"This almost sounds weird, but I'm going to say 'The Wolf of Wall Street,'" Matt Gilbert, a managing director at Thoma Bravo said. "The absurdity of that movie, to some extent, I do think, kind of incorporates some aspect of our job."

While finance is the backbone of the economy and certainly has global implications, what bankers and investors do on a day-to-day basis isn't saving lives, the 35-year-old added.

"I think the fact that you could have a comedy wrapped around the finance world is important, and it always makes me take a step back and think through, sure, I want to win every deal," he said. "Our fiduciary duty at Thoma Bravo is to produce the best returns for LPs, but this job is supposed to be fun. I'm supposed to work with great people. We're supposed to laugh together. I think if people take this job too seriously, that's when burnout and other things happen."

"The Big Short"

"The Big Short," the movie based on the financial journalist Michael Lewis' book, chronicles how Wall Street helped fuel the US housing crisis in 2008 and the investors who profited from it.

"It's not our day-to-day, but I think it is an OK representation of what happened at the time," said Chi Chen, 34, a portfolio manager at BlackRock. " Maybe it is not all factual, but it is a good one that is representative."

"The Internship"

Patrick Lenihan, a portfolio manager at JPMorgan Asset Management, said "The Internship," which features two old-school salesmen trying to restart their careers through an internship at Google, reminds him of the importance of having and supporting a diverse team.

"I feel like that team with Owen Wilson, Vince Vaughn, the rest of them, and how they come together at first, you see there's just a variety of different people that you're like, 'Oh, this is going to fail,'" he said. "But I think a large part of my success is going back to that teamwork, getting the right people in, and ensuring that diversity of opinions."

Books: "Market Wizards"

BlackRock's Chen, who focuses on fixed income, said that to really gain insight into the investing industry, it's best to read the "Market Wizards" book series, which features interviews with top traders.

"A lot of those investing stories for that book series are more from two, three decades ago, when market volatility was much higher. But we have seen a comeback of market volatility since 2020," she said. "So I have always enjoyed that whole series of books."

"Free Food for Millionaires"

Elliott, the Bank of America VP, recommends Min Jin Lee's novel "Free Food for Millionaires."

"It's about a Korean woman navigating life who ends up on Wall Street in an admin capacity. But really, it's a story about belonging and identity — about trying to make it in a world and industry you didn't initially know much about," he said.

"To me, it's a lot more humanistic. It gives me a bit more of a personal perspective when I think about my journey on Wall Street. When I think about the people — and understanding people is so much of this job — I go back to 'Free Food for Millionaires.'"

"The Man Who Solved the Market"

There's no fictional piece of media Bridgewater's Blake Cecil has found to reflect life in finance; he said shows and movies "feel quite distant" from his day-to-day.

A biography of the late hedge-fund billionaire Jim Simons, "The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution," reflects how the deputy chief investment officer and his colleagues approached challenges.

"It resonated with my experience of working with people who are using algorithms to solve problems that often hadn't been asked before," Cecil said.

"The Inner Game of Tennis"

Harrison DiGia, a vice president at General Atlantic, had another book recommendation: "The Inner Game of Tennis" by W. Timothy Gallwey.

"This book is all about the mental game and trusting your intuition and yourself. You use practice and your preparation before a competition so that when the time is right, or you have a big opportunity, you're ready, and your mental game is as strong as it can be," DiGia, 31, said.

"When I think about investing, a lot of it is setting yourself up to get that big opportunity and making sure you're prepared and can have a clear mind when that pressure situation comes. I'm a huge tennis fan, so I think about this when I'm on the tennis court, but I think about it in a professional setting as well."

"Unreasonable Hospitality"

In the book "Unreasonable Hospitality: The Remarkable Power of Giving People More Than They Expect" by Will Guidara, the co-owner and general manager of Eleven Madison Park describes how he manages his business, his customer-service style, and the things he'd do at Eleven Madison Park to go above and beyond.

Craig Kolwicz, an investment banker at Moelis, said the "unreasonable hospitality" described in the book (such as having an employee run out to get a hot dog for a customer who you overheard saying they hadn't had one in New York yet) isn't dissimilar to the type of service that could differentiate an investment banker.

"It depicts a restaurant that's an extremely expensive restaurant where there's an extremely discerning clientele base. They could go to all these other really fancy, really nice three-Michelin-star restaurants in New York or in the world," the 35-year-old managing director said.

"How do you differentiate yourself? There's a lot of investment bankers out there and there's a lot of really smart clients and folks that we work with all the time — and how do we get them to stay with us? How do we get them to hire us on the next deal? It's some of the stuff that we do," he said. For example, he'd recently flown to Los Angeles for an 11:30 a.m. pitch meeting and flown back.

"It's like hospitality, but it's kind of an unreasonable client customer service to do something like that," Kolwicz said.

• Finance firms are keen on AI agents that can automate combinations of tasks.
• Demand for AI agents is giving birth to a new class of startups and VCs hungry to invest in them.
• It was a topic of conversation at the Evident AI Symposium in New York on Thursday.

"Talk to this like a teammate and treat it like a teammate."

That's Danny Goldman's guidance to private-equity customers of his startup, Mako AI, which offers a generative AI assistant for junior finance professionals and is backed by Khosla Ventures, an early investor in OpenAI.

His hope is that "engaging with Mako looks much more like engaging with a real human associate than a software tool," he previously told BI. Goldman, who worked in private equity before cofounding Mako AI, predicts that in a year or two, every junior on Wall Street will have their own AI direct report.

It's not just juniors, either. JPMorgan CEO Jamie Dimon, is a "tremendous user" of the bank's generative AI assistant suite. Teresa Heitsenrether, JPMorgan's chief data and analytics officer, said at a conference last week that JPMorgan is working toward giving employees AI assistants that are specific to them and their jobs.

Wall Streeters, say hello to your new coworker. Across the industry, AI agents are beginning to permeate the labor force as assistants who can help humans prep for meetings, write their emails, and wade through troves of information to answer questions almost instantaneously.

In many cases, AI agents are still limited to specific, individual tasks like querying internal data and creating PowerPoints and emails. To take AI agents a step further, technologists and startup investors are fueling a shift to so-called multi-agent systems that coordinate several AI agents to complete more complex tasks more autonomously.

Some tech executives at the Evident AI Symposium said they could see a world with more artificial intelligence agents than humans by 2025. But what will work and life look like in an increasingly hybrid world with humans and bots? Well, that's still being worked out, according to a number of tech executives at the Evident AI Symposium Thursday.

"What's really exciting about agents is that we are still figuring out the tasks they're actually good at, the tools they know how to use, the tools we have to teach them how to use," said Gabriel Stengel, cofounder and CEO of Rogo, which is building the generative AI equivalent of a junior banker.

Another question that still needs to be answered is how to define when an agent is smarter or not than a human, said Kristin Milchanowski, chief AI and data officer of BMO Financial Group.

To some extent, benchmarking humans against AI agents is already happening. In a recent University of Cambridge study that compared who could run a business better, AI outperformed humans on most metrics including profitability, product design, and managing inventory. But they fell short when it came to making decisions on the fly.

Heitsenrether, speaking at the Evident AI conference, told the audience that, over time, she expects AI to be seamlessly embedded in an employee's workflow. By this time next year, she said that she hopes to have a clearer picture of what a more personalized AI assistant for each employee might look like.

But unlocking more autonomous uses of AI is going to require more than technological breakthroughs.

"We don't have a lot of trust right now in these systems," Sumitra Ganesh, a member of JPMorgan's AI research team, said at the symposium.

"We have to slow-walk it to release it to people who are experts who can verify the output and go, 'Okay, that looks fine, you can take that action,'" Ganesh said. "But that's kind of babysitting these agents at this point," she added. "But hopefully, it's like training wheels — at some point, we will be confident enough to let them go."

• Coding languages are a foundational element of any tech job, but not all are made equal.
• Python and SQL are among the most popular languages; C++ and Tableau are more specialized.
• Business Insider spoke with recruiters and tech workers to identify the top eight languages to know.

Big Tech firms like Apple and Amazon have signaled a move away from the complicated coding language C++, but there's still a place for engineers who know the coding language on Wall Street.

Apple created Swift to replace its use of C++, the company's primary coding language for its devices. Amazon recently awarded a Stony Brook University professor an approximate $100,000 research grant to continue his work to automate converting existing C++ code to Rust, a coding language created in 2006. Even the White House has joined the conversation around C++, urging software developers to move away from the language due to cybersecurity concerns in a February report earlier this year.

But the financial space is still "one of the heavy users of C++ that's really doing cutting-edge stuff," one industry executive told Business Insider. High-frequency trading firms and exchanges rely on C++, a notoriously complicated language that can offer more control over the underlying hardware. It's also prevalent in the video game industry.

Citadel Securities, for one, recently hired a C++ expert from Microsoft to lead training initiatives on the language. Looking at current open technology positions, trading firms Virtu Financial and Hudson River Trading are among the firms also seeking out C++ experience.

It's also good to know in quantitative finance, one of the few bright spots in the current technology hiring slump, Matt Stabile, a tech recruiter who works with buy-side firms including Two Sigma and Susquehanna International Group, told BI.

In today's machine-to-machine world, having some experience with programming languages is a must. Coding languages, like Python and Java, are how humans can communicate with computers by providing a set of instructions for a system to execute. As it turns out, not all programming languages are made equal and some are more relevant to certain corners of Wall Street than others.

Business Insider spoke with recruiters, Wall Street tech execs, and industry insiders and analyzed job postings to learn about in-demand skill sets.

Here are the programming languages to know:

Python

Areas of interest: Applicable across finance firms, job titles, and levels

Firms using it: Banks, hedge funds, and investment firms

As the fundamental language for engineering work across Wall Street, Python has long been at the top of the skills list for buy- and sell-side firms alike. It has been a favorite at Capital One and Man Group.

From visualization to statistical analyses to modeling and machine-learning applications, Python has multiple use cases. It also lends itself to those who don't have deep coding backgrounds because it is flexible and applies to a wide range of users, Ori Ben-Akiva, director of portfolio management at Man Numeric, a quantitative-focused division of the publicly traded hedge fund Man Group, previously told BI.

When it comes to data science and machine learning roles, "Python is king of the road," said Stabile, who runs his own recruiting shop called Stabile Search.

SQL

Areas of interest: Anyone who works with databases, data

Firms using it: Almost every financial firm

As data becomes more centralized in financial firms' strategies — from marketing to identifying new deal opportunities and analyzing risk — it's helpful to know SQL, which is one of the most common and basic ways to query or pull information from a database.

SQL is a relational database language, meaning it's designed to be able to tie different data tables together. For any tasks that have to do with analytics, you'll likely find SQL, Deepali Vyas, global head of fintech, payments, and crypto at Korn Ferry, told BI.

C++

Areas of interest: Low-latency applications

Firms using it: High-frequency trading players and exchanges

For applications and systems where speed (or a fast response time) is the name of the game, experience as a C++ developer is going to come in handy. That's especially true at high-frequency trading firms and exchanges, where companies edge each other out by being microseconds faster than the competition.

The coding language has a reputation for being trickier to master than others, and its ability to interact more closely with technical hardware can lead to nasty coding bugs, but it also generally affords the user more control and speed.

Tableau and Power BI

Areas of interest: Data visualization, front-office analysts

Firms using it: Wealth managers, banks

When Wall Street tech execs talk about data, it's often broken up into organizing it and finding insights within it.

Korn Ferry's Vyas said the latter benefits from tools like Tableau and Power BI, which visualize and contextualize data. These types of graphics are especially useful if you work in wealth management or advisory, where dashboards and data tables are regularly used.

Java

Areas of interest: Big banks with more legacy technology

Firms using it: Banks and some buy-side firms

Like Python, Java is widely used on Wall Street. The coding language secured an early foothold in the world of banking because it was believed to have security features that restricted data access, while also offering portability, or the ability to be transferred between machines.

As a result, many big banks are tethered to Java, but other firms like Two Sigma have also relied on the coding language.

Rust and Go

Areas of interest: App development

Firms using it: Fintechs, banks

Technically, many of the coding languages on this list — like Python, for example — are open source, or available for developers to use without a proprietary license.

But several open-source languages have become more in-demand in recent years, including Go and Rust. When the banking fintech Stash built much of its core banking offering from the ground up in 2022, tech leaders at the company highlighted the use of Go — which they said was picked up quickly by engineers and cut the implementation time for "substantial" new pieces of code to roughly 3.5 days.

Fintechs aren't the only financial firms embracing Go and other open source tools. At Blackrock, much of the firm's cloud work was built upon open-source software. Wells Fargo in recent years has embraced Rust and Go as languages the bank is becoming more comfortable.

Editor's note: This article was originally published in 2022 and has been updated with new information.

Former BI reporter Carter Johnson also contributed to the previous reporting.

• JPMorgan tech exec Teresa Heitsenrether talked about the bank's ongoing adoption of generative AI.
• The bank has rolled out its "LLM suite" to 200,000 employees.
• Speaking at the Evident AI Symposium, Heitsenrether explained how it's been taken up and by who.

Before a business review with JPMorgan CEO Jamie Dimon, Teresa Heitsenrether runs her presentation through one of the bank's generative AI tools to help her pinpoint the message she wants to convey to the top boss.

"I say, what is the message coming out of this? Make it more concise. Make it clear. And it certainly has helped with that," Heitsenrether, who is responsible for executing the bank's generative AI strategy, told a conference in New York on Thursday.

Dimon himself is a "tremendous user," she said, and is waiting for the ability to use the bank's tools on his phone.

"He's been desperate to get it on his phone and so that's a big deliverable before the end of the year, " Heitsenrether added.

JPMorgan, America's largest bank, has now rolled out the LLM Suite, a generative AI assistant, to 200,000 of its employees.

The tools are the first step in adopting AI technology across the firm. Heitsenrether, JPMorgan's chief data and analytics officer, speaking at the Evident AI Symposium, said that the next generation would go beyond helping employees write an email or summarize a document and link the tools with their everyday workflow to help people do their jobs.

"Basically go from the five minutes of efficiency to the five hours of efficiency," she added, saying it will take time to reach that goal.

'The flywheel effect'

The response to the LLM rollout has been "enthusiastic" and has created "healthy competition" between teams, she said. The wealth and asset management arm was the first division to use generative AI, piloting a generative AI "copilot" for its private bank this summer.

"When the investment bank found out they said 'Well, wait a minute, we want to be on there too,'" she said. "It does create a flywheel effect."

JPMorgan offers courses and in-person training for employees to use the firm's generative AI tools, such as how to prompt a chatbot properly, but the bank is also leaning on superusers, or the 10% to 20% of employees who are "really keen" to help with adoption.

"We embed people within different groups to be the local source of expertise to be able to help people that they work with understand how to adopt it," Heitsenrether explained.

The most common superusers seem to be those who clearly see the benefits of generative AI, such as a lawyer who saves hours by getting a synopsis of contracts or regulations instead of reading them all.

Despite Wall Street's interest in generative AI, getting workers to actually adopt the technology has been a key hurdle for finance firms, Accenture Consultant Keri Smith previously told BI. As a result, training and reskilling efforts have come under the spotlight, she said.

Heitsenrether said that they're trying to engage with the "pockets of resistance" now because it will be harder to convert them once the technology becomes intertwined with workflows.

She also said that the sooner people engage with AI, the less skeptical they are, and they see how it can augment, not replace, their jobs.

"Having it in your hands I think demystifies it quite a lot," Heitsenrether said. She used the example of a developer using it to more quickly write a test case. She said if they see the benefits, they realize "this is not something that's going to be done without me, but it's just a way to make my work that much more effective."

What's next: AI assistants

By this time next year, Heitsenrether told the audience that she hopes she'll be talking about "enabling employees with their own assistant" that's specific to them and their jobs.

Some of the legwork needed to develop those more autonomous forms of AI is currently being done in pilots, Sumitra Ganesh, a member of JPMorgan's AI research team, said during another panel.

Even still, the early use cases for AI workers will likely be constrained because these systems still need a human in the loop to ensure the reliability needed in such a regulated industry.

"We don't have a lot of trust right now in these systems," she said. Having an expert in the loop who can verify AI outputs is "kind of babysitting these agents at this point, but hopefully, it's like training wheels — at some point we will be confident enough to let them go," Ganesh said.

• Jeff McMillan is Morgan Stanley's head of firmwide AI.
• His team vets all new AI pitches, but business leaders ultimately decide what to use.
• He outlines the multiple steps involved from pitching an idea to getting it into production.

If Jeff McMillan does his job right, it will look very different in three years.

"Think about it. We don't have a head of PowerPoint at Morgan Stanley or Excel," McMillan told Business Insider. "These are just enabling technologies," he added.

He was named Morgan Stanley's head of firmwide AI in March to help integrate the technology across the firm. While much of his job these days is focused on getting businesses up to speed with AI and implementing it efficiently across the bank, he said his ultimate goal is for the technology to be ingrained into workers' everyday lives.

Since his promotion, McMillan has led the rollout of a few generative AI tools in the bank's wealth-management division, and has more use cases in the pipeline, he said. The bank's push into generative AI has been fueled by its early partnership with ChatGPT-maker, OpenAI, and coincides with Wall Street's recent obsession with generative AI to boost productivity and reduce grunt work for workers.

McMillan encourages employees to pitch new AI solutions. His firmwide team acts as a filter and vets the ideas, which can come from practically anyone who's done the required training at the bank. To avoid creating an unruly situation where thousands of technologists, analysts, and bankers are building their own AI tools, he's devised a rigorous multi-step process that involves pitching solutions to some of the firm's top execs and devising a business value proposition.

As part of his role, McMillan co-chairs an AI steering group formalized earlier this year, with Global Director of Research Katy Huberty. The steering committee, which has representatives from each department, vets all AI use cases pitched by employees.

The steering group is working through more than 30 use cases that are in various stages on the way to launch, McMillan said. AskResearch, an assistant that gives investment bankers, salespeople, and traders information found buried in tens of thousands of research reports, is the latest generative AI product to make it through the process since launching McMillan's team.

Many of the pitches the steering committee sees fall into two buckets: use cases that are relevant to several groups, or that matter to a specific team or group of users. For the former, McMillan is able to coordinate teams across the firm to collaborate and build solutions together, with the aim of increasing reusability.

By structuring the AI approval process this way, McMillan hopes to enable the bank to innovate without sacrificing safety.

"While there might be creative tension between experimentation and process, I believe that a rigorous process will ultimately allow us to develop and deploy technology faster and more efficiently," McMillan said.

Inside the 8-step process

Although pitching AI solutions is open to anyone at the firm, there is some leg work involved. Mainly, workers have to complete specific training on governance and AI principles and meet standards around information security.

The AI steering group meets every other week to listen to the pitches, usually going through five or six proposals. The steering group usually either approves or approves with conditions, like rethinking an aspect of the solution or coming together with other teams that pitched similar ideas. In some cases, pitches are rejected — something McMillan says he generally tries not to do.

"I don't want to be in a position where I'm telling people no. I want to tell people yes, and this is the best way to get to it," McMillan said.

For presentations that are approved, the next steps typically involve identifying the people who will execute and figuring out who from tech, legal, compliance, and risk needs to be involved. Workers going through this process also have to articulate deliverables and identify the risks, as well as having a plan for mitigating those risks. That might be a standard set of questions and answers used in testing or making certain teams aware of the potential risk.

They will also have to put together a business value proposition that outlines the benefits, which could be quantifiable, such as decreasing margin or operating costs, finding new revenue streams, or decreasing risk.

Every other week, the AI steering committee meets to review the status of these projects. At the end of the process, the group pitching presents a final time to the steering group for go-live approval to ensure all the conditions are met. Finally, use cases go into production.

"What we're doing is we're helping them prioritize. We're grouping them, and then my team, we handhold you. We say, okay, what are you trying to do? We help you set up the environment. We make sure you've got the right level of APIs, we are by your side as you work with our legal, compliance, and risk process," McMillan said of his business partners.