For more than a decade, organisations have been saying they want to be “data driven”. By 2026 that will stop being a slogan and start being a hard dividing line between those who can compete and those who cannot.
Across insurance, financial services, construction, pensions and wealth management, the same story is playing out. Artificial intelligence is moving from experiments into everyday workflows. Cybersecurity threats are evolving faster than ever. Boards are demanding clear value from digital transformation programmes. At the same time, many organisations are sitting on decades of untapped information in paper files, legacy systems and unstructured archives.
At Dajon, we see the practical side of this every day. When you strip away the hype, the organisations that get AI and analytics right in 2026 are the ones that have done the quiet, disciplined work of data migration, data integration, governance and scanning of historical records. They are building large, well managed data pools that can power analysis, surface vulnerabilities and reveal patterns that were impossible to see before.
This article looks at how that will play out in different industries, and what you can do now to get ready.
Why 2026 is a tipping point for data management
Several forces are converging to make 2026 a pivotal year for data management and digital transformation. Cloud adoption has crossed a threshold. Many organisations have moved obvious workloads to the cloud, but still rely on ageing on premises systems for core processes. To unlock the next level of value from AI and analytics, they now need to tackle the hard work of migrating complex, business critical data in a controlled way.
At the same time, AI is moving from isolated pilots to embedded tools. Data teams are no longer just experimenting with models. They are being asked to support AI assisted underwriting, real time fraud detection, predictive maintenance, automated compliance checks and personalised customer journeys.
Cybersecurity is also changing shape. Attackers are using automation and AI to probe for vulnerabilities at scale. Defensive tools are responding in kind, but they need access to complete, well integrated data to work effectively.
All of this is happening against a background of regulatory scrutiny. Supervisors and industry bodies are paying close attention to how firms use AI, how they secure data, and how they evidence fair outcomes. That puts data governance and lineage under the spotlight. In other words, 2026 is the year when data foundations stop being a back office concern and become central to strategy.
Insurance in 2026: Live risk engines, not static files
Insurance has always been a data heavy business. Yet in many insurers, a surprising amount of that data still lives in handwritten forms, scanned PDFs, legacy policy administration systems and unstructured claims notes. By 2026, the most advanced insurers will be operating very differently.
Underwriting and pricing will increasingly be supported by AI models that draw on integrated data from policy systems, claims histories, external data feeds and scanned historical documents. The insurers who win will be those who have created a single, trusted view of risk across these sources. That depends on three things.
Serious investment in data migration
Rather than simply wrapping legacy systems and hoping for the best, insurers will need structured programmes to move critical data into more modern platforms, while preserving history and audit trails.
Genuine data integration
It is no use scanning old claims files if they sit in a disconnected repository. The value comes when those digitised records are classified, indexed and connected to live policy and customer data, so that they can feed risk models and fraud analytics.
Targeted scanning of historical data
Many insurers are sitting on archive boxes of past policies, engineering reports, renewals and correspondence. In 2026, scanning and extracting key details from this material will give them a richer data pool for spotting long term patterns, emerging risks and subtle fraud behaviours that standard data sets miss.
Cybersecurity will be part of the same conversation. Insurance firms hold sensitive personal data and are prime targets for attack. As AI becomes more deeply embedded in underwriting and claims, the need for secure, well governed data pipelines will only increase.
Financial services in 2026: Cloud native, but grounded in reality
In banking and capital markets, data management in 2026 will be shaped by two competing forces. On the one hand, there is huge pressure to move faster. AI offers the prospect of instant credit decisions, real time financial crime detection, automated monitoring of trading behaviour and more personalised products. Senior leaders are understandably keen to capture these benefits.
On the other hand, banks and financial institutions know they operate in one of the most regulated environments in the world. They cannot simply throw data into a data lake and hope for the best. They must understand where data comes from, how it is transformed, who can access it and how AI models are using it.
The result is a shift in focus. We expect to see more banks treating data migration as a continuous discipline rather than a one off project. Instead of attempting risky big bang system replacements, they will plan staged migrations away from legacy platforms, decommissioning redundant technology while maintaining clear data lineage.
Data integration will also mature. Institutions will need to connect payment data, transaction logs, customer interactions, call recordings, scanned documents and third party sources into a cohesive, governed data estate. Only then can they safely feed AI engines for risk, compliance and customer insight.
Historic data will play a bigger role. There are still bank vaults and off site warehouses full of paper loan files, account records and KYC documentation. When these are scanned at scale and captured as structured data, they can enrich models for credit risk, collections strategies and lifetime value, and can also help identify historic issues that need remediation.
Cybersecurity will run through everything. Financial services firms will need to treat every data migration, every new integration and every digitisation initiative as a moment of heightened security risk, with careful control of access, encryption and monitoring.
Pensions and wealth management in 2026: Personalisation built on clean records
Pension providers and wealth managers face a very specific set of data challenges. They deal with long time horizons, complex products and intense regulatory scrutiny. They often have multiple administration systems, inherited from previous providers or from mergers and acquisitions. And they hold decades of records in a mix of formats, from physical files to images and ageing databases.
In 2026, the firms that stand out will be the ones that have put their data house in order. For pensions, that means consolidating member records into cleaner, more consistent data sets. It means resolving discrepancies between systems, plugging gaps with data capture from historical files and ensuring that key events in a member’s history are properly digitised and searchable. This is the foundation for more advanced capabilities. With a robust data management layer, pension schemes can begin to use AI to model retirement outcomes, flag potentially vulnerable members, and spot anomalies in transfer requests or benefit calculations.
For wealth managers, the opportunity lies in personalisation. Clients are increasingly expecting real time portfolio insight, proactive alerts, and advice that reflects their full financial picture. That requires integrated data from portfolio systems, CRM platforms, communication channels and scanned historical agreements or suitability documents.
Again, scanning of historical data is critical. Many firms have archives of older client files and signed instructions that still matter for compliance, but are effectively invisible to digital tools. Turning these into searchable, structured data helps satisfy regulatory expectations and provides valuable signals for risk and opportunity models.
By 2026, we expect regulators to ask tougher questions about data quality, data lineage and the use of AI in advice and administration. Firms that have invested early in data migration, integration and digitisation will be in a much stronger position to respond.
Construction in 2026: Data driven projects from design to operation
Construction has often been seen as a late adopter of digital technology, but that is changing quickly. Building information modelling, site sensors and mobile workflows are becoming standard on large projects. The next step is to treat data from these sources as a strategic asset rather than a by product.
By 2026, leading construction and facilities firms will have data management strategies that span the entire lifecycle of a building. During design and build, they will combine BIM models, project schedules, supply chain data, site imagery and safety incident reports into a single view. AI will help forecast delays, identify potential safety issues and optimise logistics.
Once assets are in operation, organisations will need to connect maintenance records, IoT data, energy usage and occupant feedback. This is where historical data becomes especially important. Many building owners still have paper O&M manuals, as built drawings and old inspection reports stored in filing rooms or off site storage.
Scanning these at scale, extracting key information and integrating it into asset management platforms allows for much better decision making. For example, it becomes possible to see where certain materials were used across an estate, to track warranty obligations, or to understand how design decisions have affected long term performance.
Digital transformation in construction is not only about slick site apps. It is about creating a reliable, secure data backbone that runs from the first sketch to the final decommissioning of an asset. That backbone depends on carefully planned data migration, data integration and digitisation.
Cybersecurity is also a growing concern in the built environment. Connected building systems create new entry points for attackers. Firms will need to treat building data, remote access credentials and control systems with the same seriousness as traditional IT infrastructure.
Other sectors in 2026: public services, healthcare and beyond
While this article focuses on insurance, financial services, construction, pensions and wealth management, similar themes are playing out in other data intensive sectors.
Public sector bodies are under pressure to modernise services, join up data across agencies and use analytics to improve outcomes. Yet they often operate with some of the most fragmented legacy estates. For them, scanning historical case files, migrating data from outdated line of business applications and integrating information across departments will be essential steps toward more intelligent, citizen centred services.
Healthcare organisations are also grappling with huge volumes of historical data in mixed formats. From handwritten notes and legacy imaging systems to disparate electronic health records, the opportunity is to create secure data platforms that support clinical decision support, population health analytics and operational efficiency, while meeting stringent privacy requirements.
In both cases, the same foundations apply. Robust data management, secure integration, thoughtful digitisation of archives and clear governance are prerequisites for safe and effective use of AI.
The common thread: Large, trusted data pools
Looking across all these industries, a common pattern emerges. The organisations that are ready for 2026 are those that have stopped treating data projects as isolated tasks and started to think in terms of building large, trusted data pools.
These pools have several characteristics. They are fed by structured data migrations from legacy systems, not uncontrolled copies. Historical data from paper files, microfiche and old digital formats has been scanned and captured in a way that preserves context and metadata. Data from different systems is integrated according to clear definitions and business rules, rather than simply dumped into a repository. Security and access controls are designed in, rather than added later.
Crucially, these data pools are actively used. They underpin AI models, dashboards, operational analytics and cyber defence tools. As a result, quality issues are spotted quickly, and there is a strong incentive to keep the data estate clean. This is where data management stops being a cost and starts being a source of competitive advantage.
Practical steps to prepare for 2026
If you want to be in that position by 2026, there are some practical steps you can take now. Start with a clear view of your information landscape. Map out the systems, repositories and physical archives that hold critical data for your organisation. Do not forget off site storage, legacy back ups and specialist applications owned by individual departments.
Next, identify a small number of high value use cases for AI, analytics or automation in your business. That might be better risk models in insurance, faster onboarding in financial services, improved site safety in construction or more accurate benefit calculations in pensions.
Then work backwards from those use cases to the data requirements. Ask which systems hold the necessary data, what quality issues exist and how much of the relevant history is still trapped in paper or legacy formats.
With that understanding, you can design structured programmes for scanning historical data, migrating key datasets to more modern platforms and integrating information across systems. It is important to build in governance, quality checks and cybersecurity controls from the start, rather than bolting them on at the end.
Finally, treat communication as part of the process. Business stakeholders are far more likely to support data migration and digitisation programmes if they can see the connection to concrete outcomes, such as reduced risk, better customer experiences or regulatory readiness.
How Dajon supports this journey
At Dajon, our role is to help organisations build the data foundations that make all of this possible.
We work with insurers, financial institutions, pension schemes, wealth managers, construction firms, public bodies and others to scan large volumes of historical data, capture it accurately and make it searchable and usable. We support secure data migration from legacy platforms to modern systems, ensuring that valuable history is preserved and properly governed. We design data integration and data capture workflows that give AI and analytics teams the reliable inputs they need, while keeping cybersecurity and compliance firmly in view.
If 2025 has been your year of experimentation with AI, 2026 can be the year you turn those pilots into robust, scalable capabilities. The key is not another tool or platform. It is a clear, disciplined approach to data management that brings together AI, cybersecurity, data migration, data integration and the intelligent scanning of historical records.
That is the work we specialise in, and we would be delighted to help you take the next step.