The Challenges and Future of AI in Business and Fintech

Council Post: The Devil Is In The Details When Working With AI Products

Gaurav Aggarwal is Co-Founder of Truva and Forbes U30. Helping SaaS businesses improve customer adoption and reduce churn with AI. The digital marketplace is flooded with AI products (some of them built with generative AI), and new AI tools are announced every day. In fact, the global AI market size is predicted to reach $826 billion by 2030, up from $135 billion in 2024. This abundance will breed confusion. More importantly, if issues arise with your AI products, customers won't think for a moment before switching to the next product. Even before the rise of AI, Zendesk research found that "50% of customers will switch to a competitor after just one bad support experience." As a consequence, AI is becoming an insanely tight playing field. Meanwhile, what most people are doing wrong is believing in the illusion of LLM tools like ChatGPT or Gemini being the universal cure to their problems. Some companies assume that they will be able to completely outsource tasks and roles to large language models (LLMs). The promise of AI can only be fulfilled when we realize something important: the devil is in the details. This old proverb means that achieving meaningful results demands more than just a superficial understanding; it requires deep comprehension. Tools like Github Copilot can generate entire blocks of code and suggest solutions, which speeds up the coding process. A programmer tasked with creating a part of an application, for instance, can use Copilot to instantly generate the necessary structure and endpoint functions, providing a substantial head start. This seemingly miraculous ability to produce functional code at the click of a button presents an enticing proposition: reduced development time and increased productivity. Research found in 2023 that GenAI "can improve a worker's performance by as much as 40% compared with workers who don't use it." Or, consider a content writer working on a series of articles for a blog. ChatGPT can quickly provide outlines, generate introductory paragraphs and suggest topics, accelerating the initial stages of content creation. This efficiency allows writers to focus on refining their ideas and ensuring the quality of their work. At first glance, these tools seem to offer a better solution to complex tasks. The promise of AI tools like Copilot and ChatGPT is undeniably compelling: They appear to simplify workflows and enhance productivity significantly. However, this surface-level ease is deceptive. We humans are creatures of habit. Whether it's the same leg you habitually slip into your trousers first or the way you instinctively start combing your hair, routines are deeply ingrained in our behavior. Similarly, while creating a tech product, clean and consistent code minimizes distractions, maintains focus and allows developers to allocate more mental resources to problem-solving and creative thinking. These are crucial factors given that reading code consumes significantly more time than writing it: In fact, it is said that time spent reading code as opposed to writing it is "well over 10 to one." A quote often attributed to Donald Knuth puts this nicely: "Programs are meant to be read by humans and only incidentally for computers to execute." While Copilot can generate a lot of the chunk of code, it doesn't account for the specific requirements of the project. The developer still needs to ensure that the generated code adheres to the coding style of the project and integrates seamlessly with other components. It needs to be consistent. This involves a deep understanding of the project's architecture, the underlying business logic and edge cases that the AI generally seems to overlook. A lot of software engineers feel, in my experience, that coding is often the easiest part of the development process and these aspects, like architecture, business logic and design of the architecture, require far more understanding to develop. Similarly, ChatGPT's ability to generate text quickly doesn't eliminate the need for humans. The initial drafts it produces might be ideas glued together and well-structured, but they often require substantial editing to align with the tone and style of writing. All sorts of AI-generated content is flooding the internet but one intriguing ongoing conversation is how many readers can instantly recognize AI-generated content. Recently, for instance, Paul Graham pointed out the use of the word "delve" in a likely AI-generated content he received. Another alarming concern with LLMs is the factual accuracy of the content generated by these tools. They tend to hallucinate and generate their versions of facts. Historically, LLMs like ChatGPT have been called stochastic parrots among the scientific community. This means they are like parrots that throw out words due to mathematical approximations. This has led to recent expensive mishaps like that with the customer support chatbot of Air Canada creating its own version of facts and lying to a customer. This can be a very dangerous outcome. In essence, while AI tools provide valuable assistance, they are not a universal cure. They excel in handling repetitive tasks and generating initial drafts, but the true value of these tools can be truly realized when combined with a deeper level of human understanding and expertise. Most business leaders agree that AI is going to be critical to success in the upcoming years. Yet, the reality is that most companies are lagging. The key to avoiding the low success rate of AI projects lies in initiating those that focus on specific solutions that can fulfill business requirements. Successful AI implementation requires clear business objectives, high-quality data and collaboration between teams. Without these elements, the promise of an AI space incrementally getting better will remain unfulfilled. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Council Post: Why Fintech Needs To Think Beyond AI

Like every other aspect of the tech world, the fintech industry has a certain weakness for buzzwords, particularly when they make a basic tool or function sound more sophisticated and impressive than it actually is. I've noticed this is particularly true in the midst of the AI craze. The best way to tout a product or feature as particularly high-tech right now is to call it "AI-assisted" and "AI-powered," even if that claim is tenuous at best. And automation, for its own sake, can often be a waste of resources that misses the bigger picture. In running toward an "all AI, all the time" future, crucial parts of other high-tech strategies and solutions are falling by the wayside. Minimizing repetitive work for employees using AI is an important and even admirable goal, for instance, but sometimes the juice isn't worth the squeeze, so to speak. Real technological advancement isn't just about being the most out-of-the-box. It's increasingly about being the most customizable and combining the latest available tools to solve the same business problems that never left. In other words, the AI craze is threatening to cannibalize how we think about and even market products as high-tech. Not all high-tech products and solutions are AI-powered, and that's perfectly fine. To that end, not all AI-powered products are incredibly sophisticated tech either -- in fact, some of the most useful AI-powered or AI-assisted tools are the more mundane and less marketable ones, such as text prediction in email programs like Outlook or Gmail. This can lead to a little bit of running with scissors when it comes to development. Caution is sometimes lost in the race to implement experimental new tools that promise unlimited potential and often an unclear exchange of sensitive data. Again and again, machine learning models are improperly touted as AI, as though human-written algorithms aren't still the basis of any tool used to train the model. (Lest we forget, AI assistance and results must be generated by an autonomous digital system.) Overpromising remains a persistent problem, and not just among eager startups. Even major players like Amazon can fall into this trap, as evidenced by their recent response to reports that their Just Walk Out store was monitored by remote (human) workers and not just AI. But what makes a genuinely advanced technological tool, if not AI? And what does high-tech look like within the context of fintech? It needs to be at least several of these things: scalable, highly embeddable, super efficient, business-oriented and extremely secure. Something that can be AI-free but no less high-tech for it are application programming interfaces, or APIs for short. On the most basic level, an API is software that allows two programs to communicate with each other. In most cases, APIs function in a pool, overlapping and serving third-party systems in a streamlined way. A simple API can be just a connection method to exchange data between two systems, but a high-tech one is a game-changer in fintech (and elsewhere), capable of providing high-value interactions like embedded financing solutions. One API can provide a multitude of services with high-tech architecture and tools under the hood. Speed, security, ease of implementation and functionality are key elements of a high-tech API solution -- AI need not be included. I think we'll see real high-tech advancement in fintech when this industry starts seeing AI as a tool rather than a marketing bauble and a skeleton key. Next-gen APIs are already being created to suit AI engines, thus replacing human-written algorithmic approaches to combine data sources and improve exchange methods. And since generative AI can understand data without converting it to a predefined format, many basic APIs will become redundant. In banking on APIs and combining the high level of security promised by APIs with generative AI's potential, fintech has a shot at achieving its potential without losing the high-tech forest for the AI trees. Fintech's problems won't be fixed by AI alone. Instead, we need to go all-in on thoughtful AI integration with other tools that are scalable, highly embeddable, super efficient, business-oriented and extremely secure. In other words, everything is considered moderation, even AI. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?