From chaos to clarity: How unstructured data powers automation

Date:
Thu, 05 Nov 2026 10:57:59 +0000

Description:
Why strong data foundations, governance and strategy are essential to unlocking agentic automation at scale.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter Unstructured data is like a massive library where the books have no titles, no catalogue, and no shelving system; all the knowledge is there, but impossible to use until its
organized.

While enterprise AI investments have increased over recent years, less than half of those AI projects in the past two years have delivered measurable business outcomes. While implementing the technology remains an ambition for organizations across the world this is a clear signal that value realization remains the industrys biggest challenge. Mixed results though have provided a valuable upside. This trial and error has uncovered the real issue that has been plaguing enterprises for years: they simply dont have the foundations
for any digital innovation, let alone AI. And organizations are finally
waking up to this reality. Latest Videos From TechRadar Watch full video
here: Michael Campbell Social Links Navigation

Chief Product Officer at Hyland. A key piece of this foundation is found in
an enterprises unstructured data. These are the emails, documents, PDFs, images, videos, chat logs, forms, reports, clinical notes, customer interactions that live outside of structured databases , and make up anywhere from 80% to 90% of an enterprise's content.

This unstructured data is critical to the successful deployment of AI and agentic automation in the enterprise because it provides context needed for enterprise AI agents to act in a way that aligns with the business and
enables agents to adapt to continuously changing business context. When done properly, with scalable human oversight and governance, enterprise agents act on a combination of AI-ready data, deep business context and industry expertise. You may like AI agents are ready to act, but do they understand your business? AI is scaling faster than organizations can control The AI
data problem nobody talks about: Why more information isn't making better decisions

Agentic AI acts less like code, and more like a co-worker. Its exactly this growth of capability that means charging ahead without a plan is risky.
Recent reports show that the UK Public Accounts Committee found poor-quality data trapped in legacy systems is a major barrier to AI, further proof that fragmented data, shaky governance and fuzzy objectives will derail agentic
AI, unless the foundations come first.

So, before committing time, effort and budget, every organization needs to
ask two crucial questions: is your infrastructure ready for agentic AI and
are the outcomes youre aiming for clearly defined? Are you a pro? Subscribe
to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. Prepare, then deploy Unlike assistive AI that responds to prompts, enterprise agents are designed to act. Because enterprise agents carry out tasks rather than just acting as an assistant, introducing them requires a thorough audit of your workflows and data. To deliver value, they must move through the same terrain people do: the systems where work is recorded, the channels where customers interact, and the rules that keep everything compliant.

This requires a deliberate strategy and clear governance approach. If you treat agents like plug-ins, they could potentially duplicate effort, create bottlenecks, or carry out incorrect, unwanted actions that ultimately result in more work for the people you were trying to help by implementing AI tools in the first place.

That is why the work must start before deployment. Before even thinking about implementing AI, businesses need a platform that allows them to connect and leverage the terabytes of unstructured data that have historically been difficult to access and even harder to govern and activate at scale. What to read next Poor data has become enterprise AI's weakest link The last mile of AI: Closing the gap between insight and action Why AI agent governance must start with enterprise data

Content like emails, photos, and videos can all provide valuable information and context for agents to operate more effectively. However, until now, much of this data has remained siloed and underutilized, gathering digital dust instead of training smarter systems. Strategy before scale A common mistake when onboarding any new technology is that organizations often try to run before they can walk. While its enticing to envision how AI, particularly agents, will transform the entire business, the first step is understanding where automation can deliver real impact.

Take Accounts Payable (AP) for example. In AP, agents are well-suited to work that is repeatable and rule bound. They can capture invoices from any
channel, convert them to clean, structured data, validate the key fields, and perform three-way matching against purchase orders and goods before posting to the finance system.

Crucially, because they sit inside the existing stack, approvers see what
they need without hopping between apps, and mobile sign-off keeps cycle times tight. Every step is logged, so audit trails and fraud checks arent bolted on later, but instead, a part of the flow.

While this automation saves hours of effort, human involvement remains the glue that holds it together. An agent can field most of the routine workload, but keeping human judgment where it changes outcomes is essential. Aim first, automate next In 2026, AI is no longer going to be an experiment. Leading enterprises are already putting this technology to work at scale, and those that keep treating it as a trial run will be left behind by competitors who are building on strong foundations. The experimentation phase is over, and organizations must start learning, both from their own mistakes and from
those who have succeeded.

Getting started means getting the basics right. That means curating and refining your existing data and implementing connected infrastructure that scales alongside innovation. Importantly, these investments are far more than just AI upgrades. Theyre changes in how decisions get made and how your organization future-proofs itself.

The winners in this new era wont be those who were first, but instead those who take the time to strategize, determine where automation will deliver real impact, and give their AI systems the foundations and resources they need to help your business flourish. Whats to come in the year ahead Over the next 12 months, enterprise agents will shift from assistive tools to governed operators that interpret, validate, and act on unstructured content at scale. Policyencoded autonomy, humanintheloop controls, and federated content intelligence will make multiagent collaboration a standard workflow pattern.

Triage, validation, compliance, and orchestration working in concert across finance, HR, customer service, healthcare, government, and insurance will be commonplace. Naturallanguage automation will become the universal interface, letting teams design, simulate, and deploy processes without specialized development, while closedloop feedback continuously improves cycle times, exception handling, and straightthrough processing.

At the same time, verticalized agent skill packs and repeatable blueprints will accelerate adoption, and advance productiongrade operating models.
Agents will observe work, propose optimizations, and safely execute under rolebased autonomy with full audit trails and rollback protection.

The backbone is a unified, federated content layer, connecting repositories and applications without forced migrations, so agents can reliably gather context, link evidence to source documents , and trigger downstream actions that deliver measurable outcomes.

Bottom line: the trial phase is over. Enterprises that get their data foundations, governance, and enablement in place now will run faster,
smarter, and safer with agentic automation. Those that wait will spend 2026 watching competitors turn unstructured content into an action engine -- and get left behind. We've featured the best AI chatbot for business. This
article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



======================================================================
Link to news story: https://www.techradar.com/pro/from-chaos-to-clarity-how-unstructured-data-powe rs-automation


--- Mystic BBS v1.12 A49 (Linux/64)
* Origin: tqwNet Technology News (1337:1/100)