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Today, 18 months into the 10-year partnership, we’re seeing incredible progress in bringing that joint vision to reality—with generative AI playing a pivotal role. 

As Matthew Kerner, Corporate Vice President of Microsoft Cloud for Industry, noted at the Sibos 2023 conference, the partnership will ultimately evolve the customer experience at-scale across global financial markets to deliver advanced, easily accessible financial data and actionable insights through empowered workflows. 

In my previous blog post, I reviewed a set of principles that I recommend for successful AI adoption in financial services. Here, I’d like to share three essential lessons from our work with LSEG that are relevant to other financial services organizations, specifically on how to determine a good solution, strategy, and focus for AI innovations.  

Generative AI: The next phase of digital transformation  

On the day of the LSEG-Microsoft partnership announcement, barely two weeks had passed since the unveiling of ChatGPT by OpenAI. Generative AI had not yet captured the world’s imagination, although it soon would. While it may have seemed to some people to have arrived overnight, at LSEG and Microsoft it was the logical extension of technical advances that we’d been focused on for years. 

The emergence of generative AI represents the next phase of digital transformation. A central aspect of the first phase involved instrumenting operations, processes, and products across scenarios and industries to gain visibility into how things were working (or not) and organizing that telemetry into ever more sophisticated data stores to glean valuable insights. Generative AI supercharges that capability with large language models (LLMs) that go far deeper in mining data at incredible speed, and conversational interfaces that let people interact using natural language. The result is a democratization of empowerment and insights at a level not seen since the advent of the internet.  

This is good news for financial services organizations who have made bet-the-company investments on digital transformation. It means that they are well-positioned to capitalize on the foundational attributes of hyperscale cloud computing, including security, compliance, and assurance. From there, the factor that can help realize the greatest innovation with generative AI is a data strategy that ensures the right data is made available to the right LLMs, and ultimately only to the right people.  

With this broad baseline, here are three important generative AI lessons from our work with LSEG thus far.

1. Choose the right AI solution for the right problem

Generative AI is so cool that it’s tempting to try to employ the full scope of its capabilities. Resist this temptation. Focus your attention on problems that actually need solving, as opposed to searching for ways to put AI to work.  

Start with problems that burden your users—for example, laborious processes among people whose time is expensive—and work backwards. Examine the applications and environments in which people spend most of their time and consider how to optimize them. One thing we’ve learned is that integrating AI directly into existing experiences and seamlessly adding support to existing workflows is far more effective than trying to create new application destinations. If you can suddenly summarize a document in 30 seconds that previously required 10 minutes, you are certain to find value.  

In the LSEG partnership, we’re focusing on lighting up experiences within existing Microsoft investments, notably Microsoft Teams, Microsoft Power Platform, and Microsoft Fabric. Among the early highlights of this approach is a new solution in the works to streamline meeting preparation for investment bankers, built directly into Teams. 

We’re also working together to create custom chatbots and copilots within Teams to minimize switching to custom application environments, and answer questions such as, “Show me the P/E ratio of [company].” 

2. Apply your strategy for data management, tenancy, and residency 

The highly regulated nature of financial services means that system design and software architecture are uniquely complex. Different people within the same organization will often require different levels of access to key data under management, and those rules must be respected by an AI solution.  

Rather than developing new ways to handle data access and security for AI, the best approach is to build on top of existing solutions that deliver those capabilities to enact and implement the required access and regulatory controls for AI. If existing solutions are inadequate, then the key order of business is to upgrade. In other words, fix the foundations first, and then build your AI solutions upon them. 

This enables you to understand the topology of what is happening where—such as, which actions occur in which tenant (for example, LSEG customer data versus the Microsoft Graph)—while assuring comprehensive security and compliance. It will also allow you to continue your existing practices around data residency, as well as those for high-availability and disaster recovery (HA/DR). 

With LSEG, we’re focusing on innovations designed to evolve how customers gain value from their data to unlock new opportunities. This involves combining LSEG’s data and content sources in Microsoft Fabric and integrating them into the enterprise-wide data catalog and governance framework of Microsoft Purview. 

“Together with Microsoft, we are empowering our customers by increasing productivity while offering greater efficiency, resilience, and scalability across all workflows, and equipping the industry with the right tools for the next generation of financial professionals. Our multi-discipline practice of data trust is integral to LSEG’s open ecosystem for financial services, built on the foundation of transparency, security, and integrity of information. It aims to deliver rigorous data quality and governance processes, scalable technology powered by Microsoft Fabric and Microsoft Purview, and the “responsible AI” principles.”

Satvinder Singh, Group Head of Data & Analytics at LSEG 

3. Evolve towards greater customer-centricity 

In the early stages of innovation, a common challenge is how to deal with an overabundance of interesting opportunities and ideas from a very broad set of stakeholders. With so many compelling options in front of us with AI, we realized we needed to sharpen our focus to prioritize decision-making based on potential value to the business. 

It is important to resist the temptation to “boil the ocean” by trying to solve too many problems at once. Instead, identify a handful of use case scenarios that focus on benefiting the end customer in ways that measurably impact business goals. To achieve this, our teams developed a methodology involving scoring and ranking of potential initiatives to identify the most promising options. Then we surveyed LSEG’s end customers to help us better understand their needs and inform us on their preferences.  

By combining rigorous customer discovery and a clear validation prioritization process, we were able to identify opportunities we might have otherwise missed—for example, recognizing an emerging set of personas sitting at the intersection of data and AI that we could expect to grow in value in coming years. Embracing a customer-centric approach also created a discipline to quickly test and invalidate hypotheses that would be shown to offer minimal customer value at unacceptable cost. 

Looking ahead with LSEG and Microsoft 

As we move forward in our partnership, LSEG will continue to move beyond delivering data-focused products to offering services that are built on the company’s expertise, data assets, and insights gleaned through AI. This will help solidify LSEG’s pole position in the marketplace as it delivers new solutions to drive financial stability, empower economies, and enable customers to create sustainable growth. 

For every firm, there is a profound opportunity to reimagine financial services. We are excited to continue partnering with LSEG to deliver this value to customers and the industry at large. 

Learn more 

• Discover Microsoft Cloud for Financial Services. 
• Learn more about Microsoft perspectives on the era of generative AI:
  • The era of generative AI: Driving transformation in banking  
  • The era of generative AI: Driving transformation in insurance  
  • The era of generative AI: Driving transformation in capital markets  

The post 3 lessons in financial services AI transformation from the LSEG-Microsoft partnership appeared first on Microsoft Industry Blogs.

Today, this feels like a fading memory. Technology has advanced the financial services landscape, fundamentally and forever, and the trend is only accelerating. With Microsoft Cloud for Financial Services, the future is being redefined in real time, as Microsoft, our global partners, and customers across the industry are pushing the boundaries of what advanced analytics and AI can achieve.  

Since the introduction of ChatGPT one year ago, the interest in advanced data and generative AI has exploded in a way we haven’t seen in a generation. Financial services firms are moving quickly to evaluate and deploy solutions, and not because they have suddenly stopped being risk averse (they have not), but rather because they recognize the risk of doing nothing and quickly finding themselves at a competitive disadvantage.  

In my work with leaders across financial services companies, I’ve found it valuable to set the stage in understanding the AI opportunity with a set of core principles, which I’d like to share in this blog post.  

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6 principles for successful adoption of advanced data and AI

The following six principles can help illuminate the journey to successfully evaluating and implementing data and AI solutions in financial services.

1. Redefine value 

In the early days of my career, I relied on a specialist software platform that delivered the data and insights I needed to do my job. I synthesized that information with any other data I could imagine and find (hence the overload of screens). The metric of value in that world was my daily active usage (DAU) of that platform—for example, my eyes glued to the screen. That began to change when the platform could alert me about something important based on predetermined criteria, and later when a bot could ping me even when I was off the platform.  

With the arrival of generative AI, firms need to think differently about the value of their technology investments. It’s no longer about measuring the amount of time users spend on a specialist platform; it’s about understanding how well technology empowers people in the right moments and the right ways. It’s about data working constantly in the background, and the platform engaging users in the most high-value, low-friction ways possible.  

2. Meet users where they are

Once you embrace the power of automation, alerts, and bots, the next question is how to extend specialist platforms with high-value notifications, without incurring new costs and headaches. Over the past 18 months, many firms have had a major ah-ha moment, realizing that the answer is already on most of their employees’ desktops.  

Microsoft Teams is widely deployed in capital markets firms across the industry, and it is designed explicitly to enable things like distributing alerts and enabling bots—even serving as a unified front end. Microsoft Excel is also nearly ubiquitous, and it is likewise built for integration, especially when coupled with Azure and Microsoft Power Platform. So now we’re seeing a wholesale embrace of firms taking advantage of these solutions, which not only add value to users’ daily workflows, but also “springboard” them back to specialty platforms when need be. Critically, this also provides the benefits of security, compliance, and productivity that are essential to deploying mission-critical, AI-based solutions.  

3. Deliver timely, actionable insights

Investment bankers, asset managers, brokers, and other financial services professionals appreciate a good insight and dislike unnecessary distractions. A good insight is one that provides the right information at exactly the right moment to help the employee decide or close out an “approve, deny, investigate” workflow. This is a high bar that requires the ability to compress and structure data and deliver it in either a graphical or tabular format, which previously would have required the use of specialist software, but now can be delivered directly into Teams and automated easily and precisely through the use of a tool like Data Activator (now in public preview for users of Microsoft Fabric).  

A bad insight is one that only adds noise to the workday. This is a matter of personal preference, which is why it’s critical that insights be hyper-personalized. People must be empowered to control how they receive notifications—in what tools and on what terms. Firms will of course have requirements for the delivery of certain types of information. But people should be empowered to design and curate their own notifications, modify them as they go, and learn what works best for them over time with the help of AI.  

4. Atomize workflows

It’s not difficult to imagine the impact of timely, actionable insights on both efficiency and better business results. But it can be daunting to consider how to architect them and bring them to life.  

The answer is to “atomize” workflows, by which I mean examining the full range of tasks involved in key organizational and business processes, breaking them down into their component parts, then asking what resources are best suited to each. This sets the stage for creating new or reinvented workflows with AI-powered capabilities in optimal roles, so that you can best automate, optimize, and evolve processes over time. A good tool to help with this process is Microsoft Power Automate, a cloud-based service that lets you create and run flows that connect various services and apps, including sending notifications across the full set of Microsoft apps. 

The goal is not just greater efficiency; AI presents the likelihood that there will be entirely new tasks, workflows, and workers. The explosion of unstructured data and the democratization of AI (that is, making it broadly useful even to non-technical users) are giving rise to things that are impossible to even imagine today within the constraints of human capital costs. 

5. Embrace hybrid intelligence

In 1997, Garry Kasparov, the reigning world chess champion, was beaten by a supercomputer. However, it wasn’t long before the combination of humans and chess-playing programs performed better than any computer.¹ It’s not that human intelligence is greater than AI. It’s that hybrid intelligence—a mix of the two—is greater than either alone. The implication for building better workflows is that you don’t want to replace human effort with AI, but rather deploy it in ways that let people do their most important work better, while handing ancillary tasks off to AI.  

This is in addition to what I call augmented intelligence—a human doing all the work, only better with technology. With hybrid intelligence, there are some tasks where a human does not need to be involved. End-of-day reporting to regulatory authorities, for example, is a human-capital intensive task in which augmented intelligence is being employed now to improve efficiency. But where a firm might have a huge volume of data, hybrid intelligence might be the better option. The key consideration is knowing when and where a human needs to be in the loop. This obviously requires careful planning, evaluation, and oversight.  

6. Build on Azure OpenAI Service 

As employees in financial services firms put generative AI to work with the recently released new Microsoft 365 Copilot for commercial customers, the groundswell of interest and demand for customized solutions is growing dramatically. In my work with customers, I stress that the fastest and most productive route to such innovation is to build on Microsoft Azure OpenAI Service. It provides a powerful platform to build new solutions for use cases such as customer service, risk management, financial crime detection, and portfolio optimization. These can take the form of Microsoft 365 Copilot plugins when you want to empower users with your data, or Azure-based copilots for user experiences within your own applications. 

What makes Azure OpenAI Service so attractive is that it gives you the power of ChatGPT and GPT-4 while being deployed on your firm’s Azure tenant, so that all data and content stay within the bounds of the organization. That makes it easy to build intelligent solutions that take advantage of advanced machine learning and natural language processing capabilities, while maintaining security and compliance. Augmenting it even further is Microsoft Fabric, a new data analytics platform designed to simplify and streamline data and analytics workflows and lay the foundation for the era of AI.  

Where to go next 

Most financial services firms are well into their generative AI journeys, although it is still the early days for all of us. In our work with customers, we review these six principles and challenge the business and IT leadership to understand the unique needs of their business, where AI will deliver the most value, and how to make smart early moves in technology investments and early use cases.  

The critical first step of the journey is to get your infrastructure in order and complete your migration to a hyperscale cloud platform, taking care to also include a comprehensive data strategy. Next, you can identify the specific scenarios with the greatest business impact for your firm over the long run, work with your partners and with Microsoft to identify the data sets that are required to light up those scenarios, and invest in the relevant solutions and start exploring. 

You can learn more about Microsoft Cloud for Financial Services by visiting our website or contacting your Microsoft representative or partner. 

In my next blog, I will update you on our partnership with London Stock Exchange Group (LSEG) and examine key aspects and lessons that have relevance for every financial services organization eager to embrace advanced data and generative AI.  

¹Why Computer-Assisted Humans Are The Best Chess Players And What That Means For Technology Operations, Forbes.

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