AI in Finance: Challenges, Adoption, and Future Prospects

The State of AI: Challenges, Adoption, and Future Prospects

The artificial intelligence (AI) landscape has experienced rapid evolution in recent years, with investments far outpacing short-term revenue expectations. This disconnect has led to a complex situation where tech giants and startups alike are facing challenges. Major companies like Cisco, Intel, and Dell have announced layoffs, while numerous AI startups have shuttered. The initial euphoria surrounding AI's potential to revolutionize industries has given way to more pragmatic concerns, with even industry leaders like OpenAI facing questions about their long-term viability. In this document, we examine the current state of AI through the lens of the innovation adoption cycle, exploring the challenges and opportunities as the technology moves from the innovator phase towards mainstream adoption. The primary hurdle facing AI adoption is the difficulty in effectively applying and integrating the technology. Many projects are failing to meet expectations, as organizations struggle to find practical use cases that deliver tangible value. This challenge is compounded by the rapid pace of AI development, which often outstrips an organization's ability to adapt its processes and workforce. The Hallucination Problem One of the most significant issues plaguing large language models (LLMs) is their tendency to produce convincing but false information, known as hallucinations. This problem undermines trust in AI systems and necessitates careful fact-checking, limiting their usefulness in scenarios requiring high accuracy. Managing Expectations There's often a disconnect between what AI models produce and what users expect. This misalignment can lead to disappointment and resistance to adoption, even when the AI's output is objectively good. Bridging this gap requires not only technological improvements, but also better education and expectation management. Data Quality and Quantity Training AI models on client-specific data has proven challenging due to two main factors: These issues necessitate additional data cleaning and augmentation techniques, increasing project costs and complexity. Cost Considerations The operational costs of running advanced AI models remain high. For instance, it's estimated that ChatGPT costs over $0.36 per query to operate, while their pricing ranges from $5 to $15 per 1M tokens. This pricing structure often results in services being offered below cost, which is unsustainable in the long term. Computational Demands Advanced AI techniques like Tree of Thoughts (ToT) require hundreds of model calls to generate a single output. This computational intensity drives up costs and limits the scalability of certain AI applications. The Innovation Adoption Cycle The current state of AI adoption aligns with the "Crossing the Chasm" model of technology adoption. We are currently in the innovator phase, characterized by high optimism, but also with a focus on "figuring stuff out" rather than widespread practical implementation. As the industry moves towards the visionary phase, companies are beginning to demonstrate real solutions in niche applications. However, this transition is accompanied by a crash in hype as the reality of the challenging path to profitability sets in. Unique Aspects of the Current AI Era Corporate Investment in Disruptive Technology Unlike previous technological revolutions, this era of AI is marked by significant investment from large tech companies in the US and China. However, the payoff for these investments may be 10-15 years away, raising questions about the long-term commitment of these corporate giants to funding AI research. The Research Lab Analogy The current situation draws parallels to the research labs of the 1950s and 1960s, such as Bell Labs and Xerox PARC. These institutions produced groundbreaking technology but often failed to capitalize on their innovations. There's a possibility that today's tech giants could face a similar fate, with smaller, more agile startups ultimately reaping the rewards of their research. The Innovator's Dilemma Major tech companies are actively pushing AI adoption to avoid falling victim to the innovator's dilemma. They're attempting to lead their customers towards AI adoption, even in the face of slow uptake. Microsoft's pricing strategy for Copilot, initially set at $108,000 per year for 300 licenses and later adjusted to $360 per year for a single license, illustrates the challenges in finding the right balance. Pricing Models: A Critical Challenge for AI Adoption One of the most significant hurdles in AI commercialization is determining appropriate pricing models. Companies are struggling to balance the need for sustainable revenue with the goal of driving adoption and creating value for customers. Recently the CEO of Cohere complained there is little margin in selling ChatBot services. Several pricing strategies have emerged, each with its own trade-offs. The complexity of AI pricing is further compounded by factors such as uncertain operational costs, difficulties in quantifying AI's value, data ownership concerns, rapid technological changes, and competitive pressures. As the industry matures, we can expect pricing models to evolve, potentially moving towards more sophisticated, value-based approaches and dynamic pricing in AI marketplaces. Successful strategies will need to effectively communicate the value of AI offerings while ensuring sustainable growth for providers. As AI becomes more powerful and pervasive, ethical considerations and regulatory challenges are coming to the forefront. Issues such as bias in AI systems, privacy concerns, industry compliance. and the potential for AI to be used in harmful ways are becoming increasingly important. Navigating this complex landscape will be crucial for the industry's long-term success. AI Education and Workforce Transformation There's a growing need for AI education at all levels, from basic digital literacy to advanced technical skills. Organizations must invest in reskilling and upskilling their workforce to effectively leverage AI technologies. This transformation of the workforce presents both challenges and opportunities for individuals and organizations alike. AI Explainability and Transparency As AI systems become more complex, the need for explainable AI (XAI) grows. Stakeholders, including end-users, regulators, and developers, need to understand how AI systems arrive at their decisions. Improving the transparency and interpretability of AI models is crucial for building trust and ensuring responsible deployment. Energy Consumption and Environmental Impact The training and operation of large AI models require significant computational resources, leading to high energy consumption. As AI adoption grows, addressing the environmental impact of these systems will become increasingly important. Developing more energy-efficient AI architectures and promoting sustainable AI practices will be key challenges for the industry. AI Governance and Standardization As AI becomes more prevalent across industries, there's a growing need for standardized governance frameworks and best practices. Establishing industry-wide standards for AI development, deployment, and monitoring will be crucial for ensuring responsible and consistent use of the technology. Copyright and IP Laws Copyright holders in certain countries are concerned about their information being used in training AI models. Japan and the United States exemplify the extreme positions countries can take. In Japan, AI's can be trained on copyright information without any legal repercussions. Yet, in the US the large copyright holders believe it is a legal violation to train an AI on copyrighted material. A legitimate concern is that, of course, AI models can consume so much information, way more than any human can possibly absorb in a lifetime. There are definitely deals that are going to get done with the really massive models who will get access to this information, but is this generally helpful or useful for the general forward step of AI? Conclusion The AI industry is at a critical juncture. While the technology has shown immense promise, it faces significant challenges in terms of adoption, cost-effectiveness, and practical implementation. As we approach the "chasm" in AI adoption, the focus must shift towards developing quality applications that deliver tangible value to customers. The future of AI will likely be shaped by how well the industry can address these challenges. This includes improving the technology itself, developing sustainable business models, navigating regulatory landscapes, and effectively managing societal impacts. While the path forward may be challenging, the potential benefits of AI remain enormous, promising to transform industries and society in profound ways. As we move forward, it will be crucial for stakeholders across the AI ecosystem - from researchers and developers to business leaders and policymakers - to collaborate in addressing these challenges. By doing so, we can work towards realizing the full potential of AI while mitigating its risks and ensuring its benefits are broadly distributed across society. 1"Artificial intelligence is losing hype", Economist, 19 Aug 2024 2https://techcrunch.com/2024/08/15/tech-layoffs-2024-list/ 3"OpenAI could be on the brink of bankruptcy in under 12 months, with projections of $5 billion in losses", 25 July 2024, Kevin Okemwa, Windows Central 4"Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025", 29 July 2024, Gartner 5"Detecting hallucinations in large language models using semantic entropy", 19 June 2024, Sebastian Farquhar et al., Nature 6"The Impact of Poor Data Quality (and How to Fix It)", 1 March 2023, Keith D. Foote, Dataversity 7"You won't believe how much ChatGPT costs to operate", 20 April 2023, Fionna Agomuoh, Digital Trends 8https://openai.com/api/pricing/ 9"Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers or simply Crossing the Chasm", 2014, Geoffrey A. Moore 10"he Innovator's Dilemma: When New Technologies Cause Great Firms to Fail,", 1997, Clayton Christensen 11"What margins? AI's business model is changing fast, says Cohere founder", 19 August 2024, Maxwell Zeff, Techcrunch 12"7 AI pricing models and which to use for profitable growth", 22 May 2024, Alvaro Morales, With Orb 13"Ethical and regulatory challenges of AI technologies in healthcare: A narrative review", 2024, Ciro Mennella, Umberto Maniscalco et al, Heliyon 14"Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence", 2023, Sajid Ali et al., Information Fusion Image credit: Freepik https://www.freepik.com/free-photo/workers-using-ai-computing-simulation_134840249.htm#fromView=image_search_similar&page=1&position=0&uuid=6adc7e50-0e55-41bf-9410-f8edcbda3256

Competition and Cooperation: In an AI-Dominated World (A2)

Over the past ten years, the banking industry has undergone significant change. New technologies, the growing influence of Fintechs, and the involvement of Big Tech in financial services have transformed traditional banking. At the same time, we see rising customer demands: fast, personalized, and digital services are now a must. The central thesis of this series is that the next phase of change will be characterized by a deep integration of AI, particularly at customer interfaces. AI agents will increasingly be able to handle all interactions with customers. This comprehensive presence will fundamentally change the way financial services are delivered and experienced. Now is the critical moment to discuss these developments. As AI reshapes the rules of the game, the balance of power between traditional banks, innovative fintechs, and powerful big tech companies will be realigned. What role will these players assume in an AI-dominated future? What might their collaboration or competition look like? And what will this mean for customers and the market? A Look Back: Fintechs and Big Tech Over the Past 10 Years In the past decade, Fintechs and Big Tech have emerged as key players in the financial sector. With innovative technologies and a strong focus on customer-centricity, Fintechs have set new standards that have massively influenced traditional banking. In addition to startups, tech giants have also made their ventures into the banking sector. Successful examples like Apple Pay and Google Pay demonstrate how digital payment services have become an integral part of daily life through strong UX and seamless integration. Companies like Apple, Facebook, Amazon, and Google have also ventured out on their own in recent years. A notable example was Facebook's Libra project, which, despite its eventual failure, highlighted how serious Big Tech is about entering the financial world. Apple, on the other hand, has shown its commitment to developing new financial products through its partnership with Goldman Sachs and its willingness to engage in collaborations with traditional banks. The launch of the Apple Card was just the beginning; it was followed by products like "Apple Savings" and "Buy Now, Pay Later." What's notable is Apple's iterative approach: products are introduced, adjusted as needed, or even discontinued, showcasing the company's flexibility in responding to market changes. Currently, Apple faces a significant shift: its partnership with Goldman Sachs will end within the next 12 to 15 months. However, this does not signal the end of Apple's financial services activities. Rather, Apple plans to continue offering both the Apple Card and Apple Savings account with a new partner. The reasons for this split are diverse: dissatisfaction with customer service, regulatory investigations by the U.S. Consumer Protection Bureau against Goldman Sachs, and a strategic realignment as the bank shifts away from its consumer banking business. A key advantage that both Apple and Google have in these developments is their direct distribution to end customers through their devices. By integrating their financial products directly into the operating system and pre-installed apps, they can roll out new services faster and more effectively to the broader market than traditional banks. This direct proximity to users gives them a decisive edge in quickly establishing new products and responding to changing customer needs. In addition to these developments, technological change has been accelerated by open banking and API ecosystems. The banking world has been forced to open up its interfaces and enable data exchange with third-party providers. At the same time, regulatory changes such as the EU's PSD2 directive and the Financial Market Infrastructure Act (FIDA) have provided further impetus to the sector. These developments over the past few years have not only demonstrated how dynamic the financial sector is but also highlighted the importance of collaboration between banks, Fintechs, and technology companies. However, while partnerships have generally been at the forefront, the increasing use of AI signals a new area of tension, in which both competition and new forms of collaboration are possible. Competition and Cooperation: How Big Tech, Fintechs, and Traditional Banks Will Operate in a World Dominated by AI Big Tech and Fintechs have proven to be drivers of innovation in the financial sector in the past. While Fintechs excel through agility and specialized offerings, Big Tech brings its technological superiority and vast customer base to the table. For traditional banks, the question in an extreme bot-economy environment is whether they can keep up in a future dominated by AI or if they will be limited to acting as "data providers" for third parties. There is no doubt that banks possess vast amounts of customer data, but the challenge will be to both leverage this data and retain customer access through their own offerings. "The biggest new thing will be the growth of non-human customers." - Shameek Kundu, Chief Data Officer and AI Entrepreneur This quote comes from the June 2024 Citi Report "AI in Finance," which provides a detailed look at the future importance of AI in the banking sector. Citi recognized early on that AI chatbots and agents will fundamentally change the financial world. Particularly in dealing with "non-human customers," or automated AI bots, the interaction between banks and clients will be redefined. One practical example from the report illustrates how AI could make independent financial decisions on behalf of customers: an AI can, for instance, select financial products, compare interest rates, and even sign contracts based on the individual preferences of the user. This reduces the need for human intervention in routine decisions and could pave the way for entirely new forms of customer interaction. Citi emphasizes that for banks to remain relevant, they must also tailor their products to meet the needs of these bots. Financial products must be designed in a way that they can be understood and chosen by AI, and compliance processes need to be adapted to ensure that automated decisions are transparent and traceable. Strategic Partnerships: Are Alliances Between These Players Inevitable? The question of whether alliances between Big Tech, Fintechs, and banks are inevitable is increasingly coming into focus. In a world where AI becomes a key capability, strategic partnerships offer an effective way to combine expertise, technology, and reach. Even today, we see collaborations aiming for win-win situations: banks provide financial expertise and regulatory experience, while fintechs and Big Tech bring access to innovative technology and a broad customer base. The future of such alliances could be even more promising, potentially leading to new business models that blur the boundaries between these players. The key will be how effectively and efficiently AI is used, and how this technology will impact the balance of power within the financial sector. But which players will truly matter tomorrow? And who might suddenly lose their relevance? An interesting thought was recently shared by Brian Armstrong, the founder of Coinbase, on "X" (formerly known as Twitter). He pointed out that while AI currently cannot open bank accounts, they can already own crypto wallets. AI agents can now make transactions with stablecoins like USDC on the "Base" blockchain, interacting with humans, merchants, or other AIs. While AI agents may not be able to open bank accounts independently for now, there is nothing stopping them from using existing bank accounts for payments or other transactions. This development raises important questions about future partnerships: Who will be able to harness the best synergies in a world of AI-driven financial transactions? Banks could strategically partner with crypto providers to better meet the needs of AI agents and simultaneously strengthen their role within the digital financial ecosystem. Fintechs, on the other hand, could act as bridges, integrating traditional banking services into AI-capable platforms, effectively bridging the gap between conventional financial services and modern technologies. Currently, traditional banks are the weakest technological players in this environment. They often lack both tech resources and the necessary talent - the "war for talent" puts banks under pressure, and the lack of network effects in distribution makes scaling digital offerings challenging. That's why a willingness to collaborate will be critical: Which players will be able to form partnerships and build strong networks that allow them to lead in an increasingly AI-dominated financial world? A look at successful platform strategies already shows how crucial the right positioning is for success in an AI-driven financial ecosystem - particularly when it comes to distribution and dominance. Distribution and Dominance: The Example of Microsoft with Their Product Teams A brief detour... Microsoft Teams has quickly established itself as a dominant platform for productivity and communication. Through the integration of tools like chat, video conferencing, file sharing, and task management, Microsoft has created a comprehensive platform that simplifies the workday. The success of Teams is largely based on its ability to seamlessly integrate existing Microsoft customers into an ecosystem where all productivity tools are interconnected - reaching a critical mass of users. The strategy behind Microsoft Teams could, in principle, serve as a blueprint for the financial sector. By creating a central platform that brings together all relevant financial services, banks and Fintechs could enable users to manage all their financial activities in one place. Such a "financial platform" would significantly simplify access to financial services, becoming a preferred touchpoint for customers - much like Teams in the corporate environment. A key success factor for this platform strategy is embedding financial services directly into users' daily lives. "Embedded Finance" is a central concept here: bringing financial transactions to where they make the most sense. Customers no longer have to open a banking app to make transactions but can do so directly in their usual work environment or other everyday apps. This presents a significant opportunity for banks to increase their reach and be present where their customers are. The integration of financial services into non-financial platforms is not a new concept. The next step in this evolution could be the direct embedding of banking and AI-based financial services into daily workflows. For example, if an AI-based financial solution is integrated into platforms like Teams, Google Workspace, or Slack, users could request loans, check account balances, or authorize payments without having to switch applications. This seamless embedding not only enhances efficiency but also improves customer experience - fostering greater trust in a non-banking environment. Europe as a Special Case: Regulation and Innovation The EU plays a unique role in regulating AI systems, setting clear guidelines for the use of AI through the AI Act and existing data protection regulations (GDPR). These strict regulations influence global competition, particularly for European banks and fintechs that must adhere to these requirements. This raises the question of whether such legal frameworks could lead Big Tech to prioritize its innovations outside of Europe or even withdraw from the European market entirely. A specific example is OpenAI, which does not offer its new Advanced Voice Assistant in the EU due to AI regulations. The EU AI Act prohibits AI systems capable of recognizing emotions in natural persons and using them in certain areas such as the workplace or educational institutions. Since the Advanced Voice Assistant is based on GPT-4o and responds to non-verbal cues like speech speed to detect human emotions, this functionality conflicts with European regulations. Other companies like Apple are also affected, as they need to adapt their AI products - such as "Apple Intelligence" - resulting in delays or reduced functionality in the EU. This situation affects not only Big Tech but also European banks seeking to advance in the AI field. Many technically feasible AI applications are not allowed in practice due to the strict data protection and ethical standards in the EU. A prominent example is "credit scoring" or "social scoring." While AI systems in other countries are already used to grant loans based on social media data, online behavior, or other non-traditional sources, European banks are skeptical of such approaches. The compliance with regulations prevents the comprehensive use of AI for innovative scoring methods, limiting the range of AI tools available to banks in Europe. These regulations are designed to protect against discrimination and invasive data usage but simultaneously restrict innovation capacity. Both Big Tech and banks face technical and competitive challenges - from the development of new services to the efficiency of processes. The question remains whether European banks and fintechs will find an advantage in setting standards or whether the stringent regulations will hinder their ability to compete globally. On the other hand, these very regulations could bolster consumer trust - a crucial factor that will shape the adoption of AI-driven financial services. Strict data protection and ethical standards may help increase user acceptance by placing security and transparency at the forefront. In the long term, the EU could thus create a market where trust and ethically responsible use of AI form the foundation for successful financial innovations. The increasing use of AI presents both risks and opportunities for all players in the financial sector. On the one hand, AI opens up significant efficiency gains; on the other, it requires a rethinking of customer relationships and revenue streams in the age of bots. The key question will be how to leverage AI optimally without losing customer proximity - and who will ultimately provide a platform that combines trust and added value. "Black box" approaches will not be viable, either from a regulatory standpoint or in terms of customer acceptance. The coming years will reveal whether collaborations and strategic alliances will strengthen the financial sector or whether the disruptive power of AI will turn established structures upside down. Particularly in terms of building trust, there is still much to be done - and we will explore this topic more deeply in our next article.