Powerweave positions ewiz Procure around outcomes like faster sourcing cycles, lower process costs, and better supplier performance. What does the company see as the biggest gap between organisations that talk about AI in procurement and those that actually convert it into measurable savings?
I think the biggest gap is actually quite simple: everyone wants to talk about AI, but not enough organisations are talking about the data that AI depends on.
In procurement, that becomes a real issue. You may have duplicate supplier records, inconsistent category structures, fragmented catalogs, poorly classified spend or incomplete item data sitting across multiple systems. If that foundation isn’t clean and reliable, adding AI on top doesn’t necessarily create better decisions. In some cases, you’re simply getting to the wrong answer faster.
That’s why, at ewiz procure, we look at AI readiness as a data problem first and a technology problem second. We focus on cleaning, harmonising and governing procurement data, and then putting that data to work across sourcing, catalogs, supplier management and analytics. That’s when AI starts becoming genuinely useful rather than just experimental.
Once you have that foundation, you can connect AI to very practical procurement outcomes: faster sourcing, better supplier comparisons, greater spend visibility, less manual work and stronger buying compliance. And ultimately, those are the measures that matter.
So, for us, the question isn’t really, “How much AI are you using?”
It is “Is your procurement data good enough for AI to deliver decisions you can trust, and can you translate those decisions into measurable savings?” That’s where we see the real difference between organisations experimenting with AI and those actually creating value from it.
Trishna Patel, Business Head – Procurement, Powerweave
How does Powerweave help a global enterprise distinguish between data that’s “clean enough” for reporting and data that’s actually AI-ready for autonomous decision-making?
I think there’s an important distinction between data that is clean enough to tell you what happened and data that is trusted enough to help determine what happens next.
A global enterprise may be able to produce a spend report even when the underlying data has duplicate supplier records, inconsistent material descriptions, missing attributes or different category structures across countries and systems. But when you expect AI to recommend a supplier, classify spend, identify an opportunity or support an automated workflow, those inconsistencies become much more significant. The AI needs to understand what it is looking at and how different data points relate to each other.
That’s why, at ewiz procure, we look at AI readiness as more than a data-cleansing exercise. We focus on standardising and enriching the data, resolving duplicates, creating consistent taxonomies and validating the information. Just as importantly, we establish governance around it — where the data came from, how it was transformed, how confident we are in it, and where human review is required.
And this isn’t something you do once. Procurement data is constantly changing as new suppliers, materials, catalogs and transactions enter the organisation. So, maintaining that foundation is just as important as cleaning it in the first place.
For us, that’s really the test of AI-ready procurement data: not whether AI can read the data, but whether the business can trust the decisions and recommendations that come from it.
From a sustainability and supplier compliance lens, how is AI-driven supplier intelligence changing the way procurement teams verify ESG credentials and certifications at scale, rather than relying on periodic audits?
I think the biggest shift is that supplier compliance is moving from a periodic, point-in-time exercise to something much more continuous. An annual audit or assessment can tell you whether a supplier was compliant at that moment, but supplier information changes all the time — certificates expire, documentation changes, performance issues emerge and new ESG requirements come into play.
AI can help procurement teams manage that complexity at scale. With ewiz procure, for example, AI can extract information from supplier documents and certificates, identify missing or incorrect submissions, check expiry dates and guide suppliers on what they need to correct. But the important part is what happens next. Through our Supplier Management module, that information can be connected with supplier risk, performance and business criticality, and issues can be taken through corrective-action workflows rather than simply appearing as another alert on a dashboard.
There is an ESG dimension to this as well. Procurement, compliance and sustainability teams are often asking the same suppliers for different information through separate processes. We see an opportunity to bring that information together so that ESG data becomes part of the supplier decision-making context rather than sitting in isolation. Within the Powerweave ecosystem, our integration with Snowkap also allows procurement data to support broader ESG and Scope 3 visibility where that is relevant.
That doesn’t mean AI replaces supplier audits or procurement judgement. It helps teams identify gaps earlier, focus attention on the suppliers and issues that matter, and make sure the right action follows. So, the real shift is not from manual audits to AI audits; it is from periodically checking compliance to continuously understanding supplier exposure and acting on it.
As eAuctions increasingly use negotiation agents, how does Powerweave ensure supplier trust isn’t eroded when vendors sense they’re negotiating against an algorithm rather than a person?
I think supplier trust becomes even more important as AI plays a greater role in sourcing and negotiation. For us, trust isn’t separate from auction performance. If suppliers don’t understand the process or feel confident participating in it, you don’t get genuine competition — regardless of how sophisticated the technology is.
That’s why we’ve taken a supplier-first approach with ewiz procure eAuctions. The focus is on making participation simple and transparent, with clear event rules, an intuitive experience, phone-friendly access and regional-language support. This is particularly important when you’re working with a diverse supplier base where smaller or regional suppliers may have very different levels of familiarity with enterprise procurement technology.
We’re also very clear about the role AI should play. AI can help procurement teams compare bids, highlight commercial differences and support award recommendations, but it shouldn’t remove buyer accountability. Final commercial and award decisions remain human-led, with approval workflows, bid histories and audit trails helping ensure the process is governed and traceable.
And not every negotiation should become an eAuction. There are situations where technical complexity, supplier relationships or other strategic considerations matter more than price competition. The role of technology is to help procurement choose the right approach for the right sourcing event, rather than automate negotiation for the sake of it.
So as negotiation technology evolves, I think the principle is quite simple: AI can make the process faster and more informed, but suppliers should still understand the rules, procurement should remain accountable for the decision, and trust should never become the price of automation.
With deployments across India, the GCC, and wider APAC, what regional differences has Powerweave observed in supplier readiness, compliance expectations, or appetite for AI-driven procurement decisions?
One of the biggest lessons from working across markets is that procurement transformation cannot follow a one-size-fits-all model. Supplier maturity, product data quality, buying practices, compliance requirements and user behaviour can vary significantly between markets and business units.
We see this particularly in both supplier and procurement-team readiness. Within the same enterprise, you may have large strategic suppliers that are very comfortable with digital procurement platforms, alongside smaller or regional suppliers that need simpler workflows, mobile access, regional-language support or more hands-on onboarding. At the same time, procurement teams themselves can be at very different stages of digital maturity — some are comfortable with digital sourcing, analytics and AI-assisted decision-making, while others are still heavily dependent on spreadsheets, email and established manual processes. So, while an organisation may want to standardise procurement globally, the way you enable suppliers and internal teams often needs to reflect local realities.
The same applies to data and compliance. Global organisations need common standards and governance, but the quality of supplier and product data, the information being collected and the way it is maintained can vary considerably across markets. For us, the challenge is to create a consistent data and governance foundation while retaining enough flexibility to accommodate local requirements.
We also see different levels of comfort with AI. Some organisations are ready to use it for data extraction, classification, supplier-document checks and commercial recommendations, while still wanting human oversight around supplier approvals, awards and other important decisions. Our approach with ewiz procure is deliberately pragmatic: AI should help procurement teams make faster, better-informed decisions, but procurement remains in control.
And this is where the work after implementation becomes really important. Through our experience supporting procurement operations across 50+ countries, we’ve seen that supplier onboarding, training, catalog maintenance, user adoption and ongoing data governance are critical to making transformation work across different markets. In one global programme, our teams have trained and onboarded more than 5,000 suppliers across 50+ countries.
So, while the pace of digitisation and appetite for AI may differ across markets, the fundamentals are remarkably consistent: trusted data, engaged suppliers, enabled procurement teams, strong governance and a procurement experience that people are actually willing to use. Get those foundations right, and AI has a much better chance of delivering real business value.
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