Your AI, your edge: Building personal productivity with AI

Social media often paints a picture of an industry transformed by artificial intelligence (AI), with firms automating workflows, deploying AI agents, and reclaiming hours of productivity each week. But the reality is more nuanced. “There is a real fear of missing out when it comes to AI,” says Sahana Prabhakar, Principal Software Engineer at Microsoft and currently leading Product and Engineering for LSEG Analytics & AI. “From the conversations happening online, you could easily conclude that every organisation has already figured it out. In practice, most are still learning what works, where the value lies, and how to scale it effectively.”

However, the reality on the ground is quite different. About 65% of traditional financial services firms globally are only in the exploring or piloting stage for AI, while 24% are scaling and a mere 6% are transforming, according to an April 2026 survey by the Cambridge Judge Business School. Five percent of respondents said their engagement with AI was very limited or not at all.

Moreover, the same survey shows that traditional financial services industry professionals haven’t really engaged with AI as much as perhaps they should be. Some 47% said that their workforce was limited in its preparedness, and 8% said their workforce was not prepared. Only 41% said they felt it was moderately prepared and just 4% said it was highly prepared.

For professionals working in financial services, the real opportunity isn’t a switch to flip once their organisation rolls out an enterprise-wide AI programme – it’s a personal skill they can start to build today, and one that compounds with engagement. 

Sahana advises people working in financial services to familiarise themselves with the technology, get comfortable with it, and “develop the muscle” before moving on to higher value use cases. This isn’t a one-off training session. It’s a habit built the same way that any professional skill is, with repetition, feedback and refinement. Those individuals who benefit the most from AI won’t be the ones waiting for enterprise-wide deployment – they will be the ones who have built personal fluency by the time it arrives. 

AI doesn’t need to be about a grand transformation project – financial professionals can start by taking a look at their own working week. A good place to begin is with tasks that require hours of structured research, synthesis and drafting that follows a repeatable pattern. “Financial services professionals need to ask themselves, ‘How efficient or how productive can I get leveraging AI?’,” Sahana says. “You don't know what you know until you start to use it, and see what is possible. Then ask yourself, ‘Have I saved time? Have I taken a decision empowered by good insights, or have I mitigated a risk event that was supposed to happen?’”

As customers embrace AI to generate more insight from data, LSEG is continuing to develop AI capabilities within Workspace. AI Search became generally available in July and now has 17,000 active users, while Deep Research, designed for more advanced analysis and report-writing, has 7,000 users. Together with other Workspace capabilities, these tools demonstrate how financial professionals can begin incorporating AI into their individual research and preparation workflows.

Three tools on LSEG Workspace show what this looks like in action:

• Deep Research on LSEG Workspace enables financial professionals to do in depth analysis using LSEG’s trusted data sets, including Datastream. The AI tool understands financial industry terms, and so is able to respond to queries in a similar way. 
• Company Intelligence is a new Workspace agent that uses generative AI and LSEG data to help financial professionals prepare quickly and easily for meetings. It can significantly reduce the time spent preparing for meetings by bringing together relevant information in a single view.
• LSEG Workspace Search chat-based experience lets financial professionals query structured and unstructured data – transcripts, filings, broker research, news – directly with the same underlying intelligence layer now available through Microsoft Teams.

These AI capabilities have been developed through the LSEG-Microsoft partnership, combining LSEG's trusted financial data, workflows and domain expertise with Microsoft's AI and cloud technologies to help meet financial professionals within their existing workflows.

None of this replaces human judgement – it sharpens where financial professionals apply it. In financial services, over-reliance on AI outputs without cross checking creates real risk. However, the safeguard isn’t scepticism for its own sake – it’s insisting on citations. Financial professionals should ask an AI tool to show its sources if they are not provided automatically, down to the specific document or section that a claim was drawn from. If it can’t provide the source, treat the output with more caution. 

This is where trusted, well-governed data matters as much as the AI model itself. “Before any data reaches a large language model (LLM), you need to know its provenance – where it was sourced, when it was collected, and whether it can be used via appropriate permissions,” says Prajesh Manglani, Sales Director, Quantitative Data Solutions and AI, LSEG. “That distinction is what separates an AI tool giving you a confident-sounding answer from one that is defensible.”

In short, AI tools can help reduce the time spent gathering, analysing and synthesising information ahead of a decision, but they don’t replace the decision-maker. That’s the trust curve in action – confidence in AI outputs is earned through repeated, validated experience. Having trusted data fuelling AI can help financial professionals build confidence in AI outputs more quickly. 

Financial professionals don’t need to wait for an enterprise roll-out to start engaging with AI. They should pick one research heavy or repetitive task from their week – an earnings synthesis or a client briefing, for example – and route it through an AI tool they already have access to, such as Microsoft 365 Copilot or the tools available in LSEG Workspace. Keep working with the AI as it learns through engagement – as do its users.

That’s how the muscle gets built – and it’s the edge that compounds.