In 2010 we collectively created, captured and consumed two zettabytes (ZB) of data.
Fast forward to 2025 and the amount of data generated is an almost incomprehensible 402.74 million terabytes per day. That totals 181ZB this year alone. And there is no sign of slowing down: By 2028, IDC expects data generation to grow to 394ZB.
"Today’s analysts can draw upon unprecedented quantities of data and research to drive their investment insights. But such massive resources risk becoming overwhelming without the tools to access and interrogate them effectively."
However, there are other challenges beyond the sheer volume of data. It starts with the question posed to Charles Babbage about his Difference Engine: If you put into the machine the wrong figures, will the right answer come out?
20th century coders coined the phrase ‘garbage in, garbage out’ (GIGO) to explain the concept. Put bluntly, it does no good to develop AI and machine learning (ML) solutions without clean data, which requiresrepeatable and scalable data management.Maintaining data integrity is the 21st century version of GIGO.
The same applies to research data. All the beautiful charts, graphs and diagrams in the world are useless if the underlying data isn’t clean, unbiased and parsed in a way that serves the end user. Which is why, as data inflation skyrockets, we are also developing the required tools.
According to data integration firm Qlik, “technically we’re reaching an inflection point where ingestion and transformation of data can be done in real time and hybrid transactional and analytical data can be stored and processed in the same place.”
This immediacy will affect everything from operational efficiency to risk mitigation and decision making. To that end, impartiality is essential.
“Any reputable investment research department should aim to build a 360-degree view of the landscape—not merely to confirm the opinions they already hold, they have to ensure they bring in and factor in views they personally disagree with. Recognising and interrogating the origin of information is fundamental to maintaining research integrity and impartiality.” — Clara Durodié, author, speaker, internationally recognized tech strategist
In a word, impartiality. It would seem logical that data is impartial, a nugget of truth in ones and zeroes, raw, unfiltered and ready to parse. If only it were so easy.
AI training models only serve to amplify the uncomfortable nature of data. A 2024 MIT study investigating LLM training reward models “found a persistent left-leaning political bias across nearly all these models.”The study’s authors were surprised by the results because the datasets were designed to capture objective truth. The authors admitted limitations: Creating datasets with zero bias is difficult, datasets are an imperfect representation of truth and falsehood and biases may be introduced both from prompts and the LLMs themselves.
In addition to data quality issues, AI output often lacks context and human expertise. The difference is, a human can explicitly say, “yes, I have an opinion”, whereas GenAI output not only has no idea if it is ‘telling the truth’, it also lacks clarity and depth. Put simply, scepticism regarding AI’s ability to produce nuanced financial research is not only natural, at this stage it is prudent.
“Do I have an opinion of my own? Of course I do. But that's not my role as Director of Research. I've got to be really careful about expressing an opinion that isn't based on any form of research. I can’t. I have to say things like ‘research shows’ […] if I say an opinion which isn't based in any fact, then I'm embellishing or denying or validating that which we already know.” — Director of Research, wealth management company
That said, there’s no putting the genie back in the bottle. AI investment in the financial sector was $35 billion worldwide in 2023 and is projected to grow to $97 billion by 2027. As a percentage of revenue, that trails only media, entertainment and sport.
The investment appears to be paying off. For example, JPMorganChase is focused on using AI to increase general productivity and to deliver efficiencies in software engineering and operations. At its Investors Day 2025, the firm announced that investments in technology and AI help its employees work more efficiently and absorb further volume growth. Presently there are more than 200,000 users of its proprietary generative AI, LLM Suite.
JPMorganChase accomplished this by fully embracing the opportunity AI and machine learning presents:
"We have immersed ourselves in an AI first mindset across all that we do. And AI is not just a tool, it's re-imagining workflows and it's changing the loading capacities for thousands of people on the frontline and in the back. We've looked at AI across many different industries and JPMorgan is playing an entirely different game." — Mary Callahan Erdoes, CEO, Asset & Wealth Management, JPMorganChase
For context, in the 2024 Evident AI Index, JPMC was one of only two banks to provide an estimate of overall return on AI-related investment. The question, then, is how can financial firms – and research teams in particular – develop and deploy AI and ML systems, at scale, in a cost-effective manner?
First, there are two challenges every firm faces.
In 2016, the Oxford Dictionaries Word of the Year was post-truth, meaning a situation where people are more likely to accept an argument based on their beliefs and emotions, than one based on facts. As scary as that sounds, generative AI (GenAI) has reached a level of sophistication that takes us one step closer to a post-truth information ecosystem.
Whilst the ouroboros is meant to symbolise an endless cycle of life, death and rebirth, what we’re observing today across the internet simply looks like a snake eating its tail. GenAI trained on internet-based text, audio and video is used to create yet more internet-based content, at speed and at scale, until it all begins to look and feel the same. Like eating at McDonald’s every day for years except it’s happening in months. And much of that GenAI content has a post-truth feel, i.e., based on beliefs and emotions.
If the emotional snake eats its tail enough times, what happens?
Two thoughts come to mind: First, the 2024 Oxford Dictionary Word of the Year: Brain-rot, meaning deterioration of a person’s intellectual state due to consuming excessive amounts of trivial online content.
Second, as we’ve seen with Google’s recent release of Veo 3, GenAI has achieved the ability to invoke a suspension of disbelief in even the most discerning consumer. Altered reality is no longer a tool reserved for elite Hollywood studios; it is available to the public starting at £18.99 a month.
Are we destined for the Social Singularity, where the intersection of technology, social media and human nature creates a doomscroll feedback loop leading to a tectonic societal shift? Certainly, we’ve already reached a point where distinguishing what is true is increasingly difficult.
This creates a dilemma:
“In a competitive attention economy there is an implicit penalty for information creators and distributors who wish to engage in the perpetuation of reliable information.”
The highly regulated finance industry is already tasked with being a standard-bearer of reliable information. What may not be so clear is that remaining visible to the next generation will require greater differentiation, greater originality and more nuanced insight than ever, because the next generation can get the same information (along with a lot of misinformation) at the swipe of a screen.
It will also require greater agility. And that means using some of the same tools as attention economy creators of ill repute and less noble aims. Which brings us to the second challenge financial firms face:
“Although technology has undeniably helped facilitate the provision of financial services through electronic bookkeeping, for example, digital technology has so far failed to deliver financial integration in Europe. In fact, non-interoperable technological ecosystems in each country – shaped by diverging national regulatory regimes – have created siloed pools of asset liquidity, further entrenching fragmentation.”— Pero Cipollone, Member, Executive Board of the ECB
Loosely speaking, the above is an example of tech debt. A broad definition that includes:
As Digital Transformation continues, there is also a kind of tech debt in research. The shift to digital-first strategies for distributing research isn’t always straight forward. That is partly due to outdated conventional document management systems and partly due to the way different generations consume content.
You may have experienced the uncomfortable situation where someone from a different generation is speaking and you’re not sure what they mean. Depending on the generation, it might even feel as if they’re speaking a different language.
Most certainly you’ve noticed another kind of generational drift based on technology. Whereas Baby Boomers might prefer a printed version of that report your team just created, Gen Z are digital natives. They grew up with smartphones, don’t remember life without the internet and most still believe those 500+ Instagram followers are real friends. For life.
Most importantly for research teams, they enjoy snackable content in the form of 30-90 second videos, memes and easy-to-understand infographics. Ask them to read a 50-page PDF… Not so much.
Then there are the generations between the Boomers and Gen Z. For example, Millennials tend to consume a mix of formats, including podcasts, webinars and detailed reports. They cross-reference research content – often relying on multiple trusted platforms. As such, they also suffer from information overload, more than any other generation.
One thing is clear, however. All generations want quality research. Indeed, they have a built-in need.
Digital content takes up most of the data stream created and consumed daily. Video, social and gaming together make up 76.27% of all internet data traffic. People consume content for a variety of reasons — to sate interests and desires, pursue ambitions and for entertainment. But at the most basic level, we are driven by primal force: Humans are hard-wired to seek information:
"We are so completely enthralled by information that one could, without exaggeration, say we are addicted to it. The addiction develops under the influence of another neuromodulator, this one called dopamine.” — John Coates, The Hour Between Dog and Wolf
In fact, dopamine is so addictive that given a choice between self-stimulating with dopamine or eating, many animals would choose to starve.
Humans are also addicted to novelty. What started as a survival mechanism, observing movement or weather patterns has, in the modern world, become the marketer’s dream. Not only do we pay attention to novelty, but we also suffer without it.
According to Coates, we “are built to ignore the world unless something important happens.” Of course, ‘something important’ is highly subjective, but one need only spend 10 minutes on social media such as TikTok or X to witness (if not experience) this phenomenon. Another example is the 24-hour news cycle with attention grabbing headlines and an ever-scrolling chyron.
The entire concept of Digital Transformation has made two things clear: There will always be ‘new’ content and people will always consume.
The ‘always on’ aspect of Digital Transformation has not been lost on financial firms. Constantly shifting markets means new and updated research is essential to serving clients.
However, there is an important argument to be made for too much content leading to overwhelm and information fatigue:
"Overall, our competitive and technologically enabled attention economy presents less than ideal conditions for well-reasoned decision-making. Information providers prioritize eye-catching appeal over veracity, diversity and informativity and human decision-makers are overwhelmed by information curated with little regard to its orientation to truth."
How, then, can financial firms best get research to clients?
By creating enough high-quality content in formats that tickle the information and novelty addictions, whilst understanding that you compete with the Financial Times print version at one end of the generational spectrum and, well… TikTok at the other end.
The good news is that this is where AI truly shines.
Imagine creating a single, high-quality research report and then handing it off to an AI with the instruction to make it useful for any medium or platform whilst meeting all compliance standards. Thanks to componentisation and AI driven automation, it is no longer science fiction.
For example, your research team might create an in-depth report intended for your top clients. Using a Create Once, Publish Everywhere (COPE) strategy, the team would re-purpose the report into a series of blog posts or short reports. Next it would create infographics for social media, a video script and a podcast episode. This keeps the core message consistent, while tailoring the delivery to the specific audience. This is the power of COPE. It makes connecting with clients of different generations much easier.
Because finance is a regulated industry, AI is not the “everything solving rocket ship” some would have you believe. But it does make accomplishing these tasks faster. Since timely, accurate information is crucial, this approach can save resources and increase efficiency.
One way this is achieved is through componentisation, a type of granular content management system. Imagine stripping a research report down to modular components, tagged and linked in the cloud, ready to be called up to create customised reports.
Now imagine the Baby Boomer family patriarch wanting to read an old-school printed report over coffee, whilst his Gen Z granddaughter wants the same information, but in snackable chunks via mobile-friendly video, soundbites and infographics.
Through personalised profiles, your clients of different generations (or learning styles) get the content they need, as they want it. Better yet, they’re more likely to consume it and make better decisions. Win-win.
Another use-case for AI-enhanced COPE methodology is regulatory updates across all platforms and touch points. Global finance companies already face the challenge of keeping abreast of different regulators. Using a tech-based approach, e.g., application programming interface (API) along with COPE, can streamline the process.
The ever-increasing glut of data makes creating content simple. Innovative technologies and tools make it fast. But nuanced research still requires human experience, depth of thought and clarity. Once the research is collected and parsed, however, we are living in a new age. More than ever before, it is possible to provide clients with the research they want in a format they love to consume, without compromising quality or running afoul of regulatory compliance.
