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Small bank, big moves: Why a bottom-up strategy beats rip-and-replace in Gen AI

In our last article, we covered why Gen AI can help small banks remain competitive and improve six specific use cases: fraud detection, customer service automation, personalized marketing, document and data processing, knowledge bases, and operational analytics.

In this article, we will explore what implementation strategies work the best for these use cases, how to align the culture piece with the technological adoption to render plans into reality and build for competitiveness and efficiency. 

The implementation playbook

Complete overhauls are difficult to undertake and don’t work well for Gen AI implementation. Gen AI is new, and the regulations, technological infrastructure, and providers around it are still developing, making a rip-and-replace approach  risky. 

Therefore, institutions with less than $10 billion in assets would be better served if they think bottom-up. That means identifying point solutions and specific use cases that are likely to have a sizable impact. Learnings from these roll-outs can then be used and workshopped to build a firm-wide strategy. 

Ryan Lockard, Principal at Deloitte recommends the following six-step plan:

1) Identify the right use cases: Undertake an organization-wide review of which processes are likely to deliver the highest ROI post-Gen AI implementation. 

2) Find the right partner: Choose consulting partners, hyper scalers, and/or fintechs that have banking and compliance expertise. 

3) Go API-first: Undertake an API-first integration strategy because it aids with shorter deployment times and significantly reduces disruption to legacy infrastructure.

4) Dip your toes first:  Start out with a pilot project and ensure that you’re measuring and tracking ROI, so your organization can learn as much from these tests as possible. 

5) Stay on top of regulations: The regulatory climate around Gen AI is likely to change considerably as the technology evolves. Small FIs can benefit from working with partners and technology providers that have responsiveness to regulations built into their systems. 

Apart from what Lockard recommends, it is important for FIs big and small to recognize that Gen AI is no longer just a technological tool. It’s changing how people think of and structure their world. While the biggest players in the industry are busy touting how great the tool will be for their bottom lines, many fear job loss. In such a climate, effective, informative, and considerate communication with employees is key to ensuring that Gen AI is actually adopted and intelligently leveraged in the office. 

6) Communicate learnings: Executive adoption and ownership of a Gen AI strategy is key to getting buy-in from more junior managers and the rest of employees. 

Effective communication across the organization can help with adoption as well as provide opportunities for teams to identify which of their own processes could use a boost from Gen AI integrations. 

How to tackle change management 

Gen AI is not just changing how employees work, but is likely to reconfigure how work is done altogether. So, it’s important that employees see executives champion and lead the way forward with Gen AI usage, according to Lockard. This should involve sharing their personal observations and experiences of using the tech as well as clear communication around the importance of the vision that led to integrating Gen AI.

Firms also should recognize that Gen AI implementation may have to come with an educational component, helping employees to train and understand how the technology works. Enabling employees to overcome this learning barrier can bolster trust in the organization while also upskilling existing staff, rather than looking for new talent with AI expertise, where competition is already fierce, according to Lockard. 

How to re-work hiring requirements

As Gen AI solutions become a part of the technology stack, small banks and FIs will have to rework their job specifications to include digital and partner management skills, along with vendor management, data literacy and cloud integrations for more technical roles, says Lockard. 

However, unlike big banks that have started to build large AI teams in-house, small banks would be better served with maintaining a select group of internal stakeholders that can “effectively orchestrate and govern AI solutions delivered by trusted partners. Once hired, commitment to continuous learning is essential, ensuring existing staff are regularly upskilled on the latest AI tools and workflows to stay agile and competitive in a rapidly changing landscape,” he shared. 

Micro case study: How Bangor Savings Bank built an employee-centric AI-strategy

Through its partnership with Northeastern University’s Roux Institute, Bangor Savings Bank announced a two-year “Accelerating Insights” program which will help build data fluency and skills in ethical AI usage among its 1100 employees. Bangor staff will get access to bespoke learning modules built by The Roux Institute, a research center affiliated with the university. 

The program’s curriculum was developed through an analysis conducted by The Roux Institute, which examined organizational documents like employee role specifications and performance assessment standards, according to Liz Kohler, Managing Director of Strategy, Operations and Growth at The Roux Institute. Through the educational program, Bangor’s employees are building data fluency skills along with learning to use AI ethically. 

The bank hopes that in the future, through the upskilling achieved by the program, it’ll be able to undertake much more ambitious Gen AI programs, having improved the baseline skills and performance of its staff. 

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Small bank, big moves: Six Gen AI use cases that move the needle for small banks (and three that don’t)

Doing more with less is the small bank mantra but burdened with legacy tech and consumers’ preference for digital experiences, small banks and FIs have their work cut out for them. 

Especially when it comes to Gen AI. 

On the one hand, the potential Gen AI offers for increasing competitiveness, CX, and efficiency demands action, on the other, are the constraints of time, money, and resources, bogging down any meaningful discussion on AI strategy. 

But small banks must find a way to break free of these chains of legacy and size, especially when the biggest players are making billion dollar investments in Gen AI and capturing thousands in the talent pool.

This article is the first in a series of two focused on how small banks can capture the competitive advantage of Gen AI. In this edition, we will discuss what use cases may supercharge small banks’ efficiency and customer experience and which use cases aren’t likely worth the investment for firms under $10 billion. 

Use cases that work

With limited resource availability, small banks and FIs need to identify areas where they can get the most bank for their buck. 

Six use cases emerge here, according to Ryan Lockard, Principal at Deloitte:

i) Fraud Detection: Bad actors are already using Gen AI to game financial systems for their gain. Criminals were able to defraud Americans out of $21 million between 2021 and 2024 using voice cloning technology the efficacy of which has been significantly improved by  modern LLMs. Most big organizations are fighting this fire with fire, and using Gen AI tools to identify new types of fraud tactics as well as improve their fraud detection systems. Small banks can use these same systems to adopt real-time fraud monitoring with automated alerts to stay ahead of criminal activity.

ii) Customer Service Automation: Bank of America is about to enhance its award-winning digital assistant Erica with Gen AI. Small banks and FIs can learn from this playbook, and integrate Gen AI technology in their digital customer service agents to drive better response times and enable their staff to focus on higher-value processes.

iii) Personalized Marketing: Smaller FIs often operate with a limited marketing budget and team. Here, Gen AI tools can help better audience analytics by improving segmentation and targeting as well as help FIs create more with less through content generation. 

For example, Duke University Federal Credit Union (DUFCU) recently integrated Vertice AI’s copywriting tool called COMPOSE. “The marketing team can prioritize delivering high-quality content that drives new member growth. COMPOSE is equipping us to elevate our standards of excellence, while streamlining our efforts, ensuring our acquisition campaigns are highly personalized, on-brand and efficient,” said DUFCU’s Director of Marketing Jennifer Sider. 

Industry-specific tools offered by financial technology providers like Vertice are a critical differentiator here. They are trained to be compliant with financial services regulations, and can keep up with evolving regulations with relatively low-lift from FIs, and also learn from internal material to ensure messaging is in-line with the tone of the firm.

iv) Document and data processing: There is a misunderstanding in the market that just because data is available, lenders and FIs have the capabilities in place to be able to utilize it effectively.  61% of lenders report being overwhelmed by the volume of data available. Gen AI can prove to be of significant value here. 

For example, the $2.5 billion, Kentucky-based Commonwealth Credit Union integrated a tool by Zest AI called LuLu Pulse, which uses Gen AI to consolidate multiple data sources like NCUA Call Reports, HMDA, and economic data, allowing the firm to gain insight into how their products and services compare to their peers. 

Additionally, Gen AI-driven KYC processing can also accelerate onboarding, improving customer experience and minimizing the chance of errors. 

v) Operational Analytics: Small FIs can also use Gen AI to take a closer look at the health and efficiency inside their organization. Gen AI powered operational analytics can help firms identify process bottlenecks and improve resource allocation, allowing these firms to build as much efficiency into their lean workforce as possible. 

vi) Knowledge bases: Access to Gen AI-powered knowledge bases can prove to be useful for small teams, offering them quick access to information around internal policies, simplifying employees’ workflow. Banks like Citizens are already implementing such tools allowing everyone in the management chain to access information about topics like employee benefits.

Use cases that DON’T work

Given limited resources and time, small FIs need to make sure that their approach to Gen AI integrations focuses on use cases that are meaningful. 

Highly complex and infrequent: Processes like complex lending decisions, where human expertise and understanding play a big role, aren’t suitable for Gen AI implementations. Low-volume, high-complexity tasks are also unlikely to yield ROI for small FIs, according to Lockard. 

Poor data quality: Firms must also keep in mind that Gen AI’s output is only as good as its data. So any use cases that hinge on poorly structured legacy data aren’t a good fit for Gen AI implementation, shares Lockard. When assessing whether a  use case will benefit from Gen AI implementation look for well-labelled and annotated because it allows Gen AI models to learn more quickly and produce better outcomes. 

Code generation: Additionally, while people are getting excited about Gen AI’s ability to write code, only firms with in-house development teams may be able to fully leverage such features. Without in-house technical expertise to properly vet, customize, and maintain AI-generated code, organizations may face security risks, integration failures, and compliance issues that far outweigh any potential benefits.

Gen AI use case suitability checklist: 

  • Does the use case occur frequently enough to justify investment? 
  • Is there a clear goal and KPI that would help measure the impact of Gen AI integration? 
  • Would the lack of human intervention in this use case severely negatively impact results?
  • What mechanisms are in place to take corrective action in case something goes wrong?
  • Who will be held accountable for mishaps? 
  • What regulations impact Gen AI usage in this use case and is there tolerance for regulatory action against the organization?
  • Is the underlying data infrastructure and data ready for Gen AI integration? 

Gen AI implementation holds massive potential for those small FIs that can rally their C-suite and employees to adopt the tech.  

“By automating manual tasks, Gen AI drives operational efficiency, reducing both costs and error rates. It also transforms the customer experience, delivering personalized, always-on service that can rival what the largest institutions offer. But perhaps most importantly, cloud-based AI solutions empower small banks to bring new products and services to market at a speed closer to that of Universal Banks and GSIBs, closing the innovation gap and leveling the playing field.” 

— Ryan Lockard, Principal at Deloitte

Identification of the right use cases is the first step in building a Gen AI strategy, stay tuned for the second part of our series to learn how to get started once the feasibility studies are over.

We will cover: 

– Implementation strategies
– Change management
– Role of technology providers
– Talent acquisition 

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The double-edged sword of Gen AI: Harms and risks for consumers and employees and why nobody talks about it

For our dedicated content series on Gen AI in financial services, we have had some of the biggest names in the industry speak to us about use cases that are unlocking pools of revenue and increased efficiency for these firms. These conversations have focused on Gen AI’s work in the back office at the biggest banks and fintechs in America, and how hundreds of teams across the industry are using the tech for tasks like software development, customer service, and summarization. 

But missing in these conversations is a deep and serious discussion on the risks and harms that can come with adopting Gen AI. In this article, I break down why the industry doesn’t like to talk about the potential harm from using Gen AI and what these risks even are. 

Why nobody talks about potential Gen AI harms 

Bad press: Gen AI adoption is allowing companies to position their brand as tech-forward and cutting-edge. External facing conversations on potential harms and risks do not make for good marketing, especially in a climate that is convinced of Gen AI’s capability to propel us into a new future. 

The financial industry is responsible for people’s money, and so these companies often have to prioritize an image of safety that bolsters people’s trust, and discussions that undermine this image are perceived as harmful to this marketing play. 

AI is complicated: Digital literacy is critical to understanding how AI works and its possible implications. While most AI practitioners are well aware of AI’s “black box” nature and the complex algorithmic overhead that goes into making AI algorithms explainable, consumers as well as non-tech bank employees may not have the same interest in understanding what AI is, how it works, where it can break, and how it impacts their lives. 

Products and features which are layered with user-friendly UX are much more approachable and demonstrate tangible value when used. Dedicating hours to understanding how the backend works is a harder goal to justify to board members, employees, and customers with likely no short term advantages other than building a more aware community. 

Gen AI is new: The novelty of Gen AI impedes the construction of sophisticated federal and state level regulations and sufficiently proactive company policies. This means financial leaders have no choice but to keep pace with competitors, adopt Gen AI, and watch their deployments closely for signs of harms and risks. The limited information in the market and vacuum of regulations on education, misuse, consumer and employee protection regarding AI does not encourage open conversations. 

Despite the reticence in the industry to openly discuss potential harms and risks, one can make a pretty good argument that such a conversation is absolutely critical to the Gen AI-fueled utopia the industry is dreaming to build: Organizations willing to lead real conversations have a chance to position themselves as thought leaders and, more importantly, may be able to coax the industry into coming out of its silos and collaborate to build industry-wide standards that can help mitigate potential lawsuits and harms faced by consumers and employees.

What are these potential harmful impacts I’m referring to? There are quite a few, but covering each one in one article is nearly impossible, so I’ll include the ones that have the closest ties to use cases already active in the industry. 

AI’s bot sized problem(s) for FIs

“Generative AI agents threaten to destabilize the financial system, sending it swinging from crisis to crisis,” writes the Roosevelt Institute. Gen AI tools are available to everyone, including bad actors that can use it to defraud customers, launch cyberattacks on FIs, and execute strategies to manipulate the market.

Moreover, an organization’s internal tools have the capability to subject customers to discriminatory behaviors, privacy breaches, and hallucinations, as well. Considering that many FIs are currently using Gen AI in the back office, similar adverse effects can be experienced by employees, too. 

The Gen AI powers that be: “The provision of AI agents may be an oligopolistic market, if not a natural monopoly” according to the Roosevelt Institute. This means that FIs that want to adopt Gen AI may face higher prices with the impetus for continued innovation being relatively low. It also means that bad actors can concentrate on these providers and exploit single points of failure that may expose an array of organizations and their customers to malicious activities. 

Source: European Central Bank

Conflicts of Interest: The market is obsessed with agentic AI. But it’s unclear whose interests these agents will act on behalf of if two negotiating parties are using the same agent. Moreover, if multiple Gen AI agents are drawing from the same data bank, they run the risk of reacting to market conditions in identical ways, opening up chances for algorithmic biases against certain products. They may also encourage large groups of customers to act in a similar manner, which can lead to bank runs or stock market crashes.

How consumers and employees maybe at risk due to Gen AI

Plain old vanilla AI has been reported to make decisions that can lead to discrimination in credit decisioning algorithms. In 2022, Lemonade wrote in its 10-Q that its “proprietary artificial intelligence algorithms may not operate properly or as we expect them to, which could cause us to write policies we should not write, price those policies inappropriately or overpay claims that are made by our customers. Moreover, our proprietary artificial intelligence algorithms may lead to unintentional bias and discrimination.”

This risk does not disappear with Gen AI. While a broad infusion of Gen AI in the credit decisioning process has yet to become commonplace, without stringent policies on what data Gen AI can or can not use, and how its decisions and outcomes will be governed, the industry has yet to build tools that will help prevent systemic discrimination against certain types of consumers barring them from accessing credit. 

“Nonbank firms like financial technology (fintech) companies, which are already subject to significantly more permissive regulations than banks, may be especially inclined to deploy AI in assessing customer worthiness for their products,” writes the Roosevelt Institute, a sentiment which is in line with industry behaviors, where fintechs have been much faster at adopting and launching Gen AI-facing features like chatbots and dedicated Gen AI to research stocks. 

It’s (not) a fact: Consumers and employees are also at the risk of being impacted by hallucinations. Although the biggest banks in the industry have yet to launch consumer-facing chatbots, most are now coming on record to talk about the productivity gains their employees are experiencing by using internal Gen AI chatbots. 

Source: AI Multiple Research

The most commonly cited use cases are customer service agents using Gen AI to quickly access answers to customer questions, technology teams using Gen AI tools for software development and code conversion, and team-agnostic tools that help employees access company policies regarding day to day questions about processes. 

The issue here is that it is unclear how these firms respond when employees take the wrong action based on the information they receive from Gen AI agents. 

The question we need to ask is this: Is it enough to say that “Gen AI can make mistakes, so please double check the answers to ensure accuracy” when a whole marketing engine is dedicated to positioning these tools as “time-savers” and their users lack the digital competency to understand the tools they are using? 

Sidebar: Gen AI in credit unions

We have extensively covered how the biggest banks are activating Gen AI use cases to benefit from efficiency and productivity gains. But smaller institutions are also hopping onto this train. We heard from two industry players: 

  1. Commonwealth Credit Union: Recently, the $2.5 billion, Kentucky-based CU decided to fill in this gap by integrating a tool by Zest AI called LuLu Pulse, which uses Gen AI to consolidate multiple data sources like NCUA Call Reports, HMDA, and economic data. This ultimately allows lenders to gain insight into how their products and services compare to their peers by querying the platform.
  2. Duke University Federal Credit Union (DUFCU): The firm is experimenting with how the new tech can enable it to expand reach and build a stronger marketing funnel. It recently integrated Vertice AI’s copywriting tool called COMPOSE. 

For DUFCU’s Director of Marketing Jennifer Sider, purpose-built tools focused on the financial services space offer her a significant advantage over free Gen AI tools available to the public. It’s also better than the manual alternative of managing the whole copywriting process alone.

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Looking back and moving forward: 2025 trends for Open Banking and Gen AI

Two technologies have dominated the conversations in this industry for the last year: Open Banking and AI. Open Banking, because the 1033 rulemaking started a discussion on who owns the customers and their data, and AI, because, well, Gen AI.

Lets dive into how Open Banking and Gen AI impacted the industry in 2024 and how we think adoption is going to fare in 2025.

Looking back: Open Banking in 2024

The 1033 rulemaking, although contentious, is definitely pushing Open Banking from a possibility to an inevitability, and it’s a shift that is reflected globally. “Paired with innovations across the industry and regulatory advancements in different global markets have helped move Open Banking forward this year and will be instrumental to its sustained growth,” said Jess Turner, EVP, Global Head of Open Banking & API at Mastercard.

Banks’ strategies to Open Banking adoption and the 1033 rulemaking is primarily determined by their size, according to Ulrike Guigui, Managing Director at Deloitte, who says:

  1. Large banks and regional banks are building out the infrastructural and technological components needed for compliance and are also exploring use cases and opportunities.

  2. Medium-to-small sized banks are looking towards vendors to access the tech or are not prioritizing this topic in light of the lengthy timeframe to comply.

The infrastructure build that Open Banking requires can be a challenge for some banks: “For banks on their path to compliance with rule 1033, the challenge has been to put in place a cross-functional strategy that addresses the ‘builds’ that will be required: consumer portal, enhanced third party risk management and developer (API) portal. Mapping data from proprietary and vendor systems, putting in place the infrastructure to handle the anticipated volume of API calls as well as the operational capacity to handle increased consumer call volume, are time consuming tasks in an environment of stretched resources,” said Guigui.

With banks finding that they may soon need to act on Open Banking either due to competition or regulation there is also a push towards raising the overall industry standard. Mastercard’s Turner adds that she has observed a call to make the ecosystem more secure as well as an added emphasis on ensuring that consumers are at the center and have control of their data.

Trends: Open Banking in 2025

As the industry navigates the infrastructural challenges and the changing regulatory landscape, we can expect to see the following trends emerge: i) More financial inclusion for consumers: Traditional credit decisioning systems may be excluding a significant portion of customers from accessing liquidity and credit. But Open Banking may be able to bring more customers into the fold. “Open banking’s innovative use of alternative data – for example, using data like rent payment history, cash flow and balance analytics to prove creditworthiness – create more opportunities for those outside of the credit mainstream to take control of their financial lives,” said Turner. This is not necessarily a new use case but the regulation-driven adoption of Open Banking and the formalization it is making necessary may finally push alternative data usage from being on the periphery to being more widely deployed.

ii) More business for vendors: Technology providers and vendors play a huge role in helping this industry shift towards new technologies, and while Open Banking isn’t ground breakingly new at this point, the number of vendors that offer these capabilities isn’t too large. “With the final passage of the rule and revised timelines, industry participants now know they need to prioritize this broad implementation and there may well be excessive demand on the few vendors who are active in Open Banking,” said Guigui.

iii) Products and experiences: Deloitte’s Guigui expects to see product development to be focused in CX and building operational efficiencies.

  1. Customer experience: Existing customer-facing processes like account opening and underwriting may become easier due to better availability of data.

  2. Operational efficiency: Banks might be able to utilize the increased availability of data to improve anti-fraud measures. There will be opportunities for banks to leverage data to improve fraud and related back-office activities.

Looking back: Gen AI in 2024

Last year, the industry warmed up further to Gen AI. It was a welcome shift from discussing whether Gen AI could end the world towards discussing what it could do for us and how to ensure it does everything safely.

Big banks like JPMC, Morgan Stanley, and Truist all found back office tasks that could benefit from the tech. Some small but forward thinking banks are realizing that building AI literacy and capabilities in their workforce is essential and launching strategies that help their workforce upskill. Meanwhile, fintechs like Public and Lili continued to move faster and found ways to augment their customer-facing interfaces with Gen AI-powered digital assistants.

“2023 was all about education and doing proofs of concept, while 2024 was about leveraging those learnings to build enterprise AI platforms and beginning to move GenAI use cases into production. At the end of 2024, we have seen solid progress from financial institutions using Gen AI in their organizations and expect this to continue to ramp up in 2025. These use cases have been focused on efficiency plays and supporting workforce acceleration but have included a human in the loop,” said Kevin Laughridge, Principal at Deloitte.

Trends: Gen AI in 2025

So far, firms (specially banks) have used Gen AI to make their existing processes more effective. But as ease with the technology increases it’s likely that Gen AI would start to feature more heavily in products and impact firms’ bottom lines.

i) Pushing into the front office: Even though there are very few active projects that hint at banks implementing Gen AI in the front of the office, it’s unlikely that it will always be this way. It took banks years to act on chatbots but they did eventually. The natural evolution of the chatbot is enhancement through Gen AI. Similarly, as comfort levels with the technology increase driven by the formulation of internal governance policies, moving implementation to the front of the office will become less daunting.

“We expect to see the GenAI use cases in financial services begin to move from the middle and back office to supporting more front office functions and move to support more revenue generations vs. driving efficiencies,” said Laughridge.

ii) Open Banking + Gen AI: There is also a possibility that both Open Banking and Gen AI technologies will come together to enhance already existing products like PFMs – layering on top of one another to enhance product experiences through automation. “In synergy with informed consent protocols, open banking data, coupled with responsible Generative AI, can optimize a consumer’s financial management, essentially acting as a personal wealth manager,” said Mastercard’s Turner.

iii) Infrastructure builds: FIs hoping to take better advantage of innovations offered by Gen AI will need to invest in improving their technology and make bigger strides in their data modernization journey. “Financial institutions will need to galvanize their AI platform; this is more than simply picking an LLM, it is a platform that enables scaled AI with many LLMs in a controlled environment,” he added. iv) Managing people:  As firms continue to deploy Gen AI first in the back office they will need to make a concerted effort to ensure their employees not only possess the technical skills to leverage Gen AI, but the data literacy to understand its risks. “FIs will also need to work through change management of their employees, continuing to show why AI is a workforce accelerator and enabler of business value,” he said.

Sidebar: Banks’ barrage-like movement when it comes to Gen AI

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Generative AI in Finance: A Team Member or a Tool?

Sarah Hoffman is Principal AI Evangelist at AlphaSense.

Have you ever said “please” or “thank you” to ChatGPT or Gemini? Recently, OpenAI stated that chatting with AI like a person can result in misplaced trust, and the high quality of the GPT-4o voice may make that effect worse. Even without voice, these systems are highly responsive and seem to “understand” user needs, making some people treat them almost like humans. We are seeing this trend across different industries, applications and even personal relationships, like the possibility of marrying AI, and beyond interpersonal relationships, such as viewing AI as a higher power. But AI isn’t human. It’s built on data, algorithms, and code. While AI can mimic human interactions, it doesn’t possess intuition, emotions, or the ability to make moral decisions.

However, with human guidance, it can be an extremely valuable tool. At AlphaSense, we’ve seen the immense demand for generative AI-driven search. Since launching our first generative AI feature in 2023, customers reported that they are saving 11 to 50 additional hours per month. McKinsey estimates generative AI will add over $200 billion in value for the banking sector, and 43% of financial services companies use generative AI.

Does that mean we should start thinking of AI as a colleague, capable of taking on everyday tasks just like a human?

Keeping AI in Check

Imagine a seasoned financial analyst facing a complex market decision. Beside them, an AI system rapidly sifts through data, spitting out predictions in seconds. Tempting as it is to lean entirely on AI, there’s a nagging feeling that something doesn’t add up—a geopolitical event, perhaps, or an emerging market trend that hasn’t been fully quantified. This scenario underscores one of the biggest risks of anthropomorphizing AI: over-reliance.

Financial teams require vast amounts of data to guide their success, and leveraging generative AI that can not only source, but can extract insights from, that data is critical. That said, AI lacks the contextual understanding and critical thinking that financial professionals bring to the table. How do we leverage AI’s strengths without falling into this trap?

Financial institutions need a structured approach to implementing AI. First, it’s crucial to define AI’s role clearly. Rather than viewing AI as replacements for human workers, teams should see them as powerful tools that enhance human capabilities. Start by identifying specific, well-defined tasks that AI can handle, and implement a regular review process. AI needs to be monitored, and its outputs should be audited regularly to ensure accuracy and reliability. Training your team is equally important. Financial professionals should be educated not just on how to use AI but on when to trust its recommendations and, most importantly, when to rely on their own judgment.

What Generative AI Can Do for Financial Teams

While AI shouldn’t replace human decision-making, it’s incredibly effective in taking on routine tasks that free up time for more strategic and creative work. Also, generative AI can spark innovation and accelerate learning in ways once thought impossible. Some examples:

Streamlining data analysis for investment decisions: Consider the flood of financial data, including broker research, global news events, and earnings calls. Generative AI can process this information at lightning speed, highlighting key trends and insights that might take analysts days to uncover. At AlphaSense, our AI capabilities are layered over premium, pre-vetted content so that users can not only uncover information quickly but can also have the peace of mind that the insights they are seeing are trustworthy and reliable. In a high-stakes industry, neither speed nor accuracy should be sacrificed.

Boosting creativity and innovation in investment strategies: Generative AI can serve as a powerful brainstorming tool for financial professionals, helping to generate new ideas and perspectives. AI can simulate various market scenarios, analyze historical trends, or identify patterns that may be missed by human analysts, sparking new ideas for investment approaches and highlighting potential risks.

Accelerating financial learning: Generative AI can also act as personalized tutors, rapidly synthesizing complex financial information, news, regulatory updates, and market insights to help professionals keep up. For instance, rather than simply analyzing data, AI can break down emerging trends, explain the impact of new regulations, or summarize the key points of lengthy reports. AlphaSense’s first generative AI tool, Smart Summaries, exemplifies this. The tool provides highly accurate summaries pulled from millions of documents across equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own content. This allows finance professionals to quickly grasp new concepts, deepen their expertise in specialized areas, and expand their knowledge in an industry that’s constantly evolving.

A Tool, Not a Teammate

AI is here to stay, and its role in financial institutions will only expand. But as powerful as generative AI is, it is still a tool—not a teammate. By recognizing the technology’s limitations, establishing clear guidelines for its use, and training teams on how to collaborate effectively with AI, institutions can fully take advantage of generative AI’s potential.

As we move further into the age of AI, financial institutions have the opportunity to become more efficient and innovative. Understanding where AI fits—and where it doesn’t—in the team dynamic is an important step in driving long-term growth. The future of AI isn’t about making it human but using it to enhance our own human creativity and strategic thinking.