Across production lines, supply chains, quality systems, maintenance operations, and enterprise planning, vast volumes of data are generated every second. Over the past decade, the sector has invested heavily in ERP systems, MES platforms, IoT infrastructure, and increasingly artificial intelligence, all with the expectation that this would lead to faster, more efficient, and more predictable operations.

You have dashboards. You have reports. You have spreadsheets, BI tools, data warehouses and a growing stack of SaaS platforms generating signals by the minute. And yet, when it matters, you cannot answer simple cross-functional questions with confidence.
• How is retention trending against pipeline health?
• Where is delivery risk accumulating?

You have dashboards. You have reports. You have spreadsheets, BI tools, data warehouses and a growing stack of SaaS platforms generating signals by the minute. And yet, when it matters, you cannot answer simple cross-functional questions with confidence.

• How is retention trending against pipeline health?
• Where is delivery risk accumulating?

Over the past year, I have had more conversations about artificial intelligence than any other technology in my career.
AI has moved quickly from something organisations were curious about to something many now feel they must adopt. In boardrooms, product teams, and technology roadmaps, the conversation is rarely about whether AI should play a role.

Cargo operators are not short of data. Across aircraft scheduling, load planning, crew management, maintenance, network operations, fuel tracking, and financial performance, vast volumes of operational and commercial data are generated continuously, forming an increasingly detailed picture of how the carrier is performing at any given moment.

Airlines are not short of data. Across flight operations, crew scheduling, maintenance, commercial planning, customer servicing, and regulatory reporting, vast volumes of operational data are generated continuously, forming an increasingly detailed and
dynamic picture of how the airline is performing at any given moment.

Manufacturing organisations are not short of data. In many organisations, more data has not accelerated decisions. It has slowed them down. Across production lines, supply chains, quality systems, maintenance operations, and enterprise planning, vast volumes of data are generated every second. Over the past decade, the sector has invested heavily in ERP systems, MES platforms, IoT infrastructure, and increasingly artificial intelligence, all with the expectation that this would lead to faster, more efficient, and more predictable operations.

In 2026, the Financial Conduct Authority has continued to evolve its expectations around data, reporting, and oversight, placing greater emphasis on the ability of firms to provide timely, accurate, and fully auditable information on demand, rather than relying on periodic reporting cycles or manual processes that introduce delay and inconsistency. 

Chief Investment Officers are being asked to lead in one of the most complex, scrutinised and structurally fragile periods the industry has ever experienced. Regulatory expectations have escalated dramatically.
Clients demand transparency in areas previously shielded from scrutiny. Consultants have raised the bar on evidence-based investment discipline. And sustainability regulations require defensible methodologies based on auditable data.

Financial services institutions are facing a growing data paradox. Despite significant investment in advanced, cloud-native platforms such as Snowflake, Redshift, and BigQuery  technologies designed to transform analytics, reporting, and strategic decision-making — many teams remain disconnected from the insights they need. 

Organisations typically engage with embedded analytics in two primary contexts. The first is internal use: deploying Panintelligence to enhance operational decision-making, strengthen compliance, and improve efficiency. The second is product distribution: embedding Panintelligence within customer-facing applications to differentiate offerings, drive adoption, and unlock new revenue streams.

In 2026, the FCA’s scrutiny of banking data will intensify to unprecedented levels. Lenders unable to produce clear, explainable evidence of their decision-making will face not only regulatory risk, but reputational damage in a market where transparency defines trust.

Panintelligence helps fintechs and financial services firms meet new FCA “Data First” rules faster - with AI-driven compliance dashboards that reduce churn, cut reporting time by 90%, and build regulator trust.

The financial services sector has lived through decades of regulatory change. The FCA’s 2025 roadmap is not just
another adjustment to the rulebook. It is a fundamental shift in how data must be captured, evidenced, and shared. The
implications reach far beyond compliance teams.

Insights in this paper are drawn from FCA and PRA publications, portfolio letters, consultation papers, and supervisory statements, together with industry analysis from PwC, McKinsey, and the Bank of England. They are supported by anonymised case examples and internal Panintelligence research on data governance and AI explainability.

Compliance is no longer just a cost of doing business. In field services, it is fast becoming one of the most powerful levers for retaining customers, winning new contracts, and driving sustainable growth. The question is not whether you can meet the tightening standards, it is whether you can use them to outpace your competition. 

Financial services institutions are facing a growing data paradox. Despite significant investments in advanced
cloud-native platforms like Snowflake, Redshift, and BigQuery designed to revolutionise analytics, reporting, and
strategic decision-making many teams remain disconnected from the insight they need. 

This guide offers a practical roadmap for closing that gap. It explores five proven steps to activate your existing data investment, eliminate manual
reporting bottlenecks, and deliver real-time, auditable insight across your organisation without increasing risk or adding technical debt.

This expanded guide outlines the five essential steps leading retailers are taking to embed governed, real-time dashboards directly into the tools and
workflows their teams already use. With this approach, they’re reducing risk, unlocking faster decisions, and turning their data into a daily strategic advantage.

SaaS vendors are looking to applications that can provide their customers with the assurance that their data is safe and always protected. Our guide highlights key security areas SaaS vendors should consider when looking at third-party applications to embed.

Every sector reaches a point where the old playbooks stop working. Field services has reached that moment. Regulatory reform, accelerating technology adoption, and rising customer expectations are reshaping the market at pace. The organisations that act decisively will not only keep up — they will define the next decade.

Imagine this scenario. The FCA and PRA issue an urgent directive: produce a fully auditable portfolio of regulatory reports covering pricing fairness, Consumer Duty outcomes, claims performance, solvency metrics, and AI explainability — all within 48 hours.Your team scrambles to extract data from underwriting systems, claims platforms, actuarial models, and finance. Yet each source tells a subtly different story.

In our report, we provide fresh insights into how SaaS vendors are currently integrating or exploring the application of AI, its alignment with their broader innovation and investment strategies, and the obstacles that the SaaS sector must surmount to ensure the responsible and fitting use of this technology.

We delve into the reasons behind the escalating popularity of embedded analytics.

Additionally, we explore why most SaaS organizations proactively collaborate with embedded analytics vendors instead of pursuing in-house development. As well as outlining what to expect during an embedded analytics trial and deployment.

Many SaaS vendors see embedding analytics into their application as one of the main priorities in their product roadmap. But why is this?

This guide explains why this trend is growing amongst SaaS vendors and why most are actively engaging and partnering with an embedded analytics vendor rather than attempting to build themselves.