There’s no denying the buzz around artificial intelligence (AI). Every day, headlines proclaim breakthroughs and forecast monumental changes in the way we work. Yet inside many organizations, AI initiatives can feel like an uphill battle—sometimes delivering questionable or “hallucinated” results and eroding stakeholder trust. Why does this happen so frequently? Often, it’s a data quality problem. And more specifically, it’s a lack of contextualized data for AI to rely on. 

Most enterprises store enormous volumes of information in disparate systems

A wealth of carefully structured data lives in enterprise resource planning (ERP) or customer relationship management (CRM) solutions like SAP. But beyond those neat rows and tables lies a far larger—yet somewhat unruly—body of unstructured data: documents, emails, PDFs, and manuals scattered across file shares, intranets, or email threads. In fact, analysts commonly suggest that 80% or more of enterprise data is unstructured. Worse still, much of this data remains untapped by AI because no one has effectively sorted, tagged, or linked it to the structured records that provide context and meaning. 

This is where OpenText Extended ECM (xECM) becomes a game-changer. Instead of leaving your unstructured data in silos, xECM automatically anchors relevant documents to SAP transactions (or other enterprise records) behind the scenes. Imagine an invoice stored in a content repository, for instance. If the system knows exactly which vendor, purchase order, and SAP financial entry it relates to, then AI-driven invoice automation can happen seamlessly. When an AI tool queries your data—be it a large language model (LLM) or a specialized algorithm—it doesn’t just find an invoice in a random folder; it discovers the correct invoice in full context: who it’s from, what it’s for, and whether it reconciles with the purchase order. 

This context is more than a nice-to-have. It can be the difference between AI delivering truly valuable insights and AI wandering off into speculation. High-quality, contextual data means higher trust in AI outputs, fewer hallucinations, and more immediate impact for your business processes. 

Where Are You on Your AI Journey? 

Organizations approach AI at varying levels of readiness. Some are just beginning their journey, scanning the market to see how AI tools might automate back-office tasks. Others are already advanced, using AI across departments, building internal AI expertise, and experimenting with emerging technologies like multi-agent systems. Whichever point you occupy on that spectrum, it’s worth asking yourself: “What do we want out of AI, and how can we ensure the data behind it is reliable?” 

For those just getting started, connecting AI to strong data sources like SAP and xECM can give you a smoother on-ramp. For those further along, it’s an opportunity to level up your AI projects—injecting more trustworthy, context-rich data so that accuracy and user adoption soar. 

Real-World Scenarios: AI in Action 

To see how contextualized data works in practice, consider Accounts Payable (AP) Automation. AI can read and extract key invoice details—vendor name, amounts, dates—and automatically cross-reference them with SAP records. If a mismatch arises, the AI flags it, saving finance teams from hours of manual validation. 

Another example is Contract Intelligence. Contracts often contain clauses that need compliance checks. If a contract is stored in xECM, it’s already paired with the correct SAP purchase order or project code. An AI solution can parse the contract language, compare it to internal policies, and highlight any red flags. What’s more, the AI can automatically suggest relevant references or related documents—because it has been trained on a dataset where each piece of unstructured content is tied to a real business process. 

We see similar benefits in compliance and regulatory checks—where AI scans policy documents in xECM for wording that might pose legal, safety, or operational risks—and in maintenance scenarios—where an engineer can quickly retrieve the precise equipment manual or work instruction they need from a massive repository. 

Overcoming AI Hurdles with Qellus 

None of this happens by magic. Organizations need to figure out which processes will benefit most from AI, assess data quality, and run pilot projects that demonstrate tangible ROI. That’s where we at Qellus step in. We begin with an AI Readiness Assessment—pinpointing the sweet spots for AI deployment within your existing technology stack. From there, we help you launch pilot projects that deliver quick wins and build momentum within your teams. 

Think about the invoice processing scenario: as soon as we can show a real drop in manual invoice handling and a corresponding uptick in accuracy, it becomes much easier to pitch AI expansion to stakeholders. Beyond these pilots, Qellus develops an AI Strategy Roadmap, ensuring that your organization has a systematic plan for gradually scaling successful pilots into enterprise-wide AI initiatives. 

And because technology never stands still, we provide ongoing support—guidance on integration patterns, data governance, and security best practices. This is crucial: AI thrives when fueled by consistent, clean data. As systems evolve, or as you bring in new data sources and cloud services, we ensure your AI foundation remains robust. 

Harnessing the Power of xECM and SAP for AI 

At a technical level, you can envision three layers working in harmony: 

1. Business Application Layer (where SAP and xECM reside) provides the structured and unstructured data.  
2. Orchestration Layer routes data securely and triggers AI-based workflows. 
   
3. AI Platform & Models—be it large language models, multi-agent frameworks, or specialized machine-learning algorithms—tap into xECM for context, returning highly relevant insights to end-users. 

This layered approach ensures your AI outputs don’t exist in a vacuum; they feed directly back into the business processes that generate value. When the AI flags a contract that needs legal revision or identifies an invoice discrepancy, the responsible team can act immediately—no manual searching through file shares, no guesswork. 

Bringing It All Together 

It’s easy to be dazzled by AI’s possibilities—self-driving cars, automated language translation, or hyper-personalized recommendations. But in the day-to-day enterprise world, the most impressive advancements often hinge on something less glamorous: data integrity. Without high-quality, contextualized data, even the most sophisticated AI can generate wrong or misleading answers. 

By contrast, when your unstructured content is anchored to the transactions and relationships that matter—like it is in OpenText Extended ECM connected to SAP—AI gains a formidable ally. Contextualized data transforms routine tasks such as invoice processing, contract review, and compliance checks, infusing them with speed, accuracy, and trust. 

If you’re excited by the potential of AI but anxious about its pitfalls, Qellus is here to help you chart a path forward. We’ll evaluate your current state, run a targeted pilot, and then lay out a strategic roadmap—ensuring that each new AI capability is built on the solid bedrock of meaningful data. That’s how you unlock AI’s true potential, delivering real business impact and setting the stage for continuous innovation. 

Ready to turn your unstructured data into AI gold?

Let Qellus guide you in harnessing the power of OpenText, SAP, and AI for transformative outcomes. 

Reach out to our team to discuss your AI journey. We’re here to help you build trust, improve accuracy, and unleash the full promise of AI in your organization.