The Globality Blog

Procurement teams, like all business functions, are looking to utilize the power of AI to deliver savings, efficiency, and strategic value — while navigating increasingly complex data and processes. We are meeting these challenges head-on with Glo, the most advanced AI Agent in procurement, and have introduced groundbreaking pricing analysis capabilities that empower Fortune 500 and Global 2000 companies to make faster, better buying decisions across all their enterprise spend.

Revolutionizing Procurement with Next-Gen Analytics
As part of the journey from scope to savings, procurement and business teams can simply ask Glo for detailed project pricing information in natural language. Our award-winning platform, which manages all spend from tail through to complex service categories, instantly creates detailed, actionable, and easily shareable data in the form of AI-powered summaries and dynamic charts and visualizations.

Leading Global 2000 enterprises, including Fidelity Investments, HP, Invesco, Santander, Tesco, and T. Rowe Price, have turned to Globality to better manage company spend. By synthesizing internal and third-party data, Glo enables these organizations to evaluate proposals with confidence and deliver measurable business outcomes.

Delivering New Business Value Amid Complexity
The need for advanced analytics in procurement is more urgent than ever. Gartner’s Procurement Predicts 2025 Report reveals that 63% of procurement organizations fear losing their competitive edge unless they improve their use of data and analytics. While AI promises increased productivity, reduced costs, and faster decision-making, these benefits can only be achieved if procurement teams trust the outputs of their technology.

This is where Glo excels. By providing real-time pricing insights and autonomous analysis capabilities, Glo enables procurement teams to focus on what matters most — strategic decision-making and driving sustainable, bottom-line value.

How Glo’s Capabilities Are Redefining Procurement
Glo’s enhanced analytic tools represent a major leap forward in the way procurement teams approach pricing and proposal evaluation. These features work together to solve critical challenges:

Analytics: Glo provides a foundational layer of intuitive, real-time analytics. Using Agentic AI, procurement professionals can create interactive dashboards, visualizations, and trend reports in seconds, ensuring smarter decisions at every step of the buying journey.

Next-Gen Proposal Insights: This feature builds on Glo’s analytic foundation, offering a deeper dive into pricing data. By using Agentic AI to perform analysis and make recommendations based on industry standards and historical data, Globality gives users a competitive edge in pricing strategies. With this tool, users gain a strategic edge in proposal evaluation and pricing strategies.

Glo Pricing Insights: This feature brings everything together by leveraging Globality's agentic architecture to provide immediate, actionable answers to pricing inquiries from procurement or business users. 

Driving Competitive Advantage with AI
These capabilities don’t just streamline processes—they revolutionize them. With Glo, procurement leaders can better manage risk, enhance productivity, and unlock new opportunities for value creation.

Our mission at Globality has always been ambitious: to transform enterprise spending into a smarter, more inclusive process. With nearly a decade of innovation behind it, we continue to lead the charge in AI-driven procurement solutions. By offering actionable insights, seamless automation, and a human-like approach to decision-making, Glo empowers businesses to thrive in an era of unprecedented complexity.

For procurement leaders looking to stay ahead, Glo’s status as the most advanced AI Agent in the industry, represents a vital competitive advantage enabling them to better manage risk, boost efficiency and productivity, and create new value that goes straight to the bottom line. Through Glo’s next-gen AI-powered analytics, companies gain the tools to drive better business outcomes in the face of unprecedented market and internal complexity.

Historically hard-to-procure spend areas that are complex and high-value, like indirect spend or sourcing of services, have proven an enduring bottleneck for technology. Machines struggled to comprehend nuanced requirements, leaving such tasks firmly in the hands of human expertise. However, the landscape is rapidly shifting. By 2025 and beyond, advancements in AI promise a revolutionary change. Procurement professionals could shed the burden of crafting exhaustive briefs. Instead, they could collaborate with AI systems honed by years of experience and vast datasets of procurement workflows.

These AI partners would provide immediate access to tailored insights and resources. This could enable procurement teams to launch efficient and highly customised sourcing strategies. This evolution not only optimises time and resources but also positions the CPO as a strategic leader within the organisation.

Synthesise in seconds to give accurate and data-driven answers

Customers describe this transformation as nothing short of game-changing.  A recent independent assessment into the benefits of the technology, conducted by analysts at global research and analysis firm HFS,  highlights just how transformative this technology is. It introduces an unprecedented level of transparency to procurement—something the industry has long strived for. Historically, bidding for complex services has meant painstakingly crafting lengthy, intricate RFPs. This has often made it challenging to fairly and thoroughly evaluate vendors’ solutions, pricing, and alignment with requirements like diversity, sustainability, and cultural fit.

Doing this repeatedly at scale is a Herculean task. But imagine a future where you can discuss every aspect of a sourcing brief with an AI partner. THat partner would be a tireless, infinitely patient system with flawless recall and meticulous attention to detail. These emerging AI systems are not just tools; they act as informed colleagues. They process and synthesise vast amounts of data in seconds, offering precise, data-driven insights.

Better still, they simplify the process of generating market-ready responses, ensuring alignment with your organisation’s policies on compliance, governance, and ethical sourcing. The result? Procurement teams are freed from 99% of the drudgery, empowered to focus on strategy and innovation, while achieving fairness and transparency at levels previously unattainable.

AI agents

By partnering with this new class of AI agents, buyers not only work smarter, they also enhance their reputation across the enterprise. After all, introducing new, data-driven processes that identify the best providers, proposals, and outcomes will lead to improved business metrics, enabling procurement to add more strategic value and help drive new growth. As the effectiveness of these processes becomes more visible, increasing numbers of stakeholders—including those typically a little suspicious of what they see as over-rigid procurement strictures—become engaged, bringing more spending under management.

However, it’s important to emphasise that, unlike other areas, in complex sourcing we’re not yet talking about machine-to-machine-only transactions. Complex purchasing decisions always involve relationships; buyers and sellers need to feel confident and trust each other. 

Developing synergistic partnerships with AI

To be honest, I can’t stress enough that relationships, both internal and external, will always be crucial in procurement. These will continue to be managed and led by people, while AI agents do the background work and the heavy-lifting and manual work that at the moment slows you down. In fact, a large part of B2B procurement will increasingly be driven and orchestrated by human experts working in productive and synergistic partnerships with AI.

My advice is to approach AI as you would any trusted colleague—by investing time and effort into building a strong partnership. The potential value lies in the prompts, strategies, and guidance you provide to unlock the power of machine learning, predictive analytics, and Agentic AI within your organisation. For example, you might ask, “Scan my spend portfolio to identify the top three categories with the most supplier fragmentation. Recommend which 15% of my supply base could manage the majority of my spend.”

Autonomous sourcing can then pinpoint categories like employee learning and development, cleaning services, and marketing events, which collectively involve over 2,000 suppliers. With sufficient data, it can instantly identify the 14 suppliers in each category capable of managing the majority of the volume. From there, it outlines three-panel sourcing projects, selecting the most suitable suppliers for each event and providing a clear, actionable framework for execution.

Applying lessons for your team’s benefit

Similarly, you could ask, “Who in my company has run a penetration testing services project in the last 18 months? What were the results, which suppliers bid, and what were the planned contract durations? Provide any insights I could benefit from.” The AI identifies three projects conducted in Europe, LATAM, and the US. Remarkably, it uncovers that one supplier was awarded two of these projects at different price points—something no one had realised.

Within seconds, the system also highlights that one project is performing better financially than any of the previous bids. It recommends consolidating all three projects into a single sourcing event to capitalise on efficiencies before the contracts expire. The AI then prompts the user: “Would you like to combine these volumes and create a new project?” The response? A resounding yes.

By the way, these are all queries you can make today, not tomorrow. So, my advice to the forward-thinking Chief Procurement Officer as we close out 2024 is to start thinking ahead. These are the kinds of prompts you should be exploring with your new AI partners in 2025. Now is the time to get familiar with how AI can reshape your procurement strategy and unlock unprecedented value.

Click here to book a demo of our award-winning AI-driven souring platform and see how you can super-charge your team's impact across the enterprise.

The statistics tell an alarming story: according to IBM's 2024 Cost of a Data Breach Report [1], the average cost of a data breach has now reached $4.88 million. The report highlights an emerging concern: attackers are increasingly leveraging AI and automation tools, adding complexity to the threat landscape and extending breach lifecycles by weeks or months.

But beyond these numbers lies a more nuanced reality; as AI transforms business processes, it creates novel attack surfaces and security challenges that traditional frameworks struggle to address. The rapid adoption of AI technologies across industries has fundamentally changed the security landscape, introducing complexities that weren't even contemplated in traditional security frameworks. Organizations now face the dual challenge of protecting not just their data, but also the AI models that process it, the training data that shapes these models, and the outputs they generate.

At Globality, we've learned that robust security isn't just about checking boxes; it's about building a comprehensive shield around customer data that evolves with emerging threats. While we're proud of our ISO 27001 certification and SOC2 compliance (both recently reaffirmed through rigorous audits), we see these as foundations rather than finish lines. These certifications represent our commitment to maintaining rigorous security standards, but our actual security practices go well beyond what these frameworks require. We've built our security infrastructure with the understanding that in today's landscape, compliance is merely the starting point.

Beyond Standards: Security in Practice

The rise of AI has introduced new complexities in data protection that few organizations are fully prepared to address. Traditional security models often fall short when confronted with the unique challenges of AI systems.

Data residency requirements have become increasingly complex, varying not just by country, but by data type and processing stage. Organizations must now track and control data flows across multiple jurisdictions, ensuring compliance with a patchwork of regulations while maintaining system performance. This becomes particularly challenging when AI models need to process data across borders or when training data comes from multiple jurisdictions.

The protection of both training data and model outputs presents another layer of complexity. At Globality, we've developed sophisticated approaches to this challenge. Our AI models fall into two main categories; classifiers for categorizing projects based on natural language descriptions, and clustering/ranking models for provider matching. Each requires its own careful security consideration. For classifier training, we've implemented a rigorous four-step anonymization and sanitization pipeline:

• We systematically remove client-specific identifiers and codes
• We employ Named Entity Recognition (NER) to identify and remove personal and company proprietary information
• We use sophisticated techniques like Normalized Google Distance (NGD) calculations to identify and redact client-specific entities
• We implement reproducible name mangling for sensitive terms

This process, performed with heavy involvement by Globality InfoSec personnel, ensures that while our models can learn from aggregate patterns, they never retain or expose sensitive client information. We're particularly stringent about personal identifiable information (PII); it's completely excluded from our training pipeline.

For our clustering and ranking models, which process a wide array of supplier data, we've implemented strict controls on data sourcing and usage. While we collect data from public, private, and proprietary sources, we maintain clear boundaries around what data can be used for model training. We've explicitly designed our systems to exclude sensitive proprietary data from model training without explicit customer consultation and approval.

Ensuring AI systems maintain security during both training and inference requires a comprehensive approach that traditional security frameworks don't fully address. During training, we must protect against data poisoning attacks and unauthorized access to training data. During inference, we need to guard against model extraction attacks and ensure that model outputs don't leak sensitive information.

To address these challenges, we've implemented advanced security measures that go beyond standard practices. For example, our Hold Your Own Key (HYOK) encryption capability gives customers complete control over their data while maintaining functionality, and our AI Governance Committee provides oversight of all AI changes from model improvements to prompt evaluation.

IP allowlisting is another critical component of our security infrastructure that we've implemented as part of a broader zero-trust architecture to protect every point of ingress (including both web application and REST API requests). This means every access attempt, whether from an allowed IP or not, undergoes rigorous authentication and authorization checks. We've also adopted the ISO 42001 framework for AI systems, ensuring security is built into our AI operations from the ground up, not bolted on as an afterthought.

Practical Security Advice for the AI Era

Based on our experience protecting sensitive enterprise data, we've developed a comprehensive set of security principles that organizations should consider as they build out their AI infrastructure:

• Treat AI model inputs and outputs with the same rigorous security controls as your raw data. Many organizations focus on securing their databases but leave model interactions inadequately protected. This creates vulnerabilities that sophisticated attackers can exploit. Recent research has demonstrated how seemingly innocuous model outputs can potentially be used to reconstruct training data [2]. That's why we've implemented end-to-end encryption for all model interactions, ensuring that data remains protected at every stage of processing.
• Implement granular access controls as part of a comprehensive zero-trust architecture. IP allowlisting is just the beginning; every access request should be validated regardless of its source. This means implementing strong authentication mechanisms, regular access reviews, and detailed audit logging. At Globality, we maintain comprehensive audit trails of all system access, enabling us to detect and respond to potential security incidents quickly.
• Encryption should be end-to-end, with customer control over keys. Our HYOK implementation demonstrates that robust encryption doesn't have to come at the cost of functionality. We've developed sophisticated key management systems that allow customers to maintain complete control over their data while still leveraging our AI capabilities. This includes automatic key rotation, secure key storage, and immediate revocation capabilities.
• Implement comprehensive data anonymization pipelines for AI training. This isn't just about removing obvious identifiers; it requires a sophisticated multi-layer approach that considers context and relationships between data points. Our experience shows that effective anonymization must combine rule-based systems, machine learning techniques, and careful human oversight.
• Embrace data minimization as a fundamental security principle. At Globality, we rigorously define what data we need and only use the data required. We also carefully select our models and vendors, and we don't allow third parties to use any data we send through their APIs for AI training. This approach follows a core security principle: the best way to mitigate risk is to minimize the data you hold in the first place. By being intentional about data collection and processing, organizations can significantly reduce their attack surface and potential exposure.

The Path Forward

The reality is that security in the AI era requires a fundamental shift in how we think about data protection. It's not enough to secure static data; we need to protect information as it flows through increasingly complex AI systems. This means implementing security controls that understand and account for the unique characteristics of AI workflows, from data ingestion through model training and inference.

Our commitment to security at Globality goes beyond maintaining certifications and implementing standard security measures. We're continuously evolving our security infrastructure to address emerging threats and protect our customers' data in an increasingly complex technological landscape. By sharing our experiences and insights, we hope to contribute to the development of more robust security practices across the industry.

This approach has not only helped us maintain the trust of our enterprise customers but has also positioned us to adapt to emerging security challenges as AI technology continues to evolve. The future of AI security will require even more sophisticated approaches to data protection, and we're committed to staying at the forefront of these developments.

Click here to book a demo of our award-winning AI-driven sourcing platform.

References

[1] IBM Security. "Cost of a Data Breach Report 2024." IBM Security, 2024. https://www.ibm.com/reports/data-breach

[2] Haim, N., Vardi, G., Yehudai, G., Shamir, O., & Irani, M. "Reconstructing Training Data From Trained Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) 2022. https://openreview.net/forum?id=Sxk8Bse3RKO

Procurement teams, like all business functions, are looking to utilize the power of AI to deliver savings, efficiency, and strategic value — while navigating increasingly complex data and processes. We are meeting these challenges head-on with Glo, the most advanced AI Agent in procurement, and have introduced groundbreaking pricing analysis capabilities that empower Fortune 500 and Global 2000 companies to make faster, better buying decisions across all their enterprise spend.

Revolutionizing Procurement with Next-Gen Analytics
As part of the journey from scope to savings, procurement and business teams can simply ask Glo for detailed project pricing information in natural language. Our award-winning platform, which manages all spend from tail through to complex service categories, instantly creates detailed, actionable, and easily shareable data in the form of AI-powered summaries and dynamic charts and visualizations.

Leading Global 2000 enterprises, including Fidelity Investments, HP, Invesco, Santander, Tesco, and T. Rowe Price, have turned to Globality to better manage company spend. By synthesizing internal and third-party data, Glo enables these organizations to evaluate proposals with confidence and deliver measurable business outcomes.

Delivering New Business Value Amid Complexity
The need for advanced analytics in procurement is more urgent than ever. Gartner’s Procurement Predicts 2025 Report reveals that 63% of procurement organizations fear losing their competitive edge unless they improve their use of data and analytics. While AI promises increased productivity, reduced costs, and faster decision-making, these benefits can only be achieved if procurement teams trust the outputs of their technology.

This is where Glo excels. By providing real-time pricing insights and autonomous analysis capabilities, Glo enables procurement teams to focus on what matters most — strategic decision-making and driving sustainable, bottom-line value.

How Glo’s Capabilities Are Redefining Procurement
Glo’s enhanced analytic tools represent a major leap forward in the way procurement teams approach pricing and proposal evaluation. These features work together to solve critical challenges:

Analytics: Glo provides a foundational layer of intuitive, real-time analytics. Using Agentic AI, procurement professionals can create interactive dashboards, visualizations, and trend reports in seconds, ensuring smarter decisions at every step of the buying journey.

Next-Gen Proposal Insights: This feature builds on Glo’s analytic foundation, offering a deeper dive into pricing data. By using Agentic AI to perform analysis and make recommendations based on industry standards and historical data, Globality gives users a competitive edge in pricing strategies. With this tool, users gain a strategic edge in proposal evaluation and pricing strategies.

Glo Pricing Insights: This feature brings everything together by leveraging Globality's agentic architecture to provide immediate, actionable answers to pricing inquiries from procurement or business users. 

Driving Competitive Advantage with AI
These capabilities don’t just streamline processes—they revolutionize them. With Glo, procurement leaders can better manage risk, enhance productivity, and unlock new opportunities for value creation.

Our mission at Globality has always been ambitious: to transform enterprise spending into a smarter, more inclusive process. With nearly a decade of innovation behind it, we continue to lead the charge in AI-driven procurement solutions. By offering actionable insights, seamless automation, and a human-like approach to decision-making, Glo empowers businesses to thrive in an era of unprecedented complexity.

For procurement leaders looking to stay ahead, Glo’s status as the most advanced AI Agent in the industry, represents a vital competitive advantage enabling them to better manage risk, boost efficiency and productivity, and create new value that goes straight to the bottom line. Through Glo’s next-gen AI-powered analytics, companies gain the tools to drive better business outcomes in the face of unprecedented market and internal complexity.

Our Chief Customer Officer Keith Hausmann highlights the big trends he expects to see in procurement in 2025 and as you would expect his predictions are dominated by AI and how it can help the function to operate more efficiently while driving savings and adding more strategic value:

1. The Year AI Goes Mainstream

In 2024, AI evolved from holding a Bachelor's degree to earning a PhD. Despite this, many CPOs are still sitting on the sidelines, watching peers in Marketing or Engineering dive into the AI revolution.

In 2025, this hesitation will end. The entire spectrum of AI—from LLMs to predictive analytics to autonomous agents—will become procurement’s new playground. To showcase its potential, we partnered with HFS Research to reality-check our claims about AI’s transformative power in sourcing.

What they discovered from our customers such as T. Rowe Price, Fidelity Investments and UCB Pharma surprised even us: automated spend management is delivering instant savings of 20% from more competition and transparency in the sourcing process while the technology enables business users to self-serve, allowing enterprises to competitively manage more spend, even with lower headcount.

If this is the emerging benchmark for AI in Procurement, it’s clear there’s no time left to wait. In 2025 more and more CPOs will embrace the shift to AI-driven strategies to stay competitive and drive better business outcomes.

2. From 'Should We Try AI?' to ‘How Fast Can We Start?’

For three or four years, we’ve been championing the remarkable ROI that AI delivers in sourcing. Back in 2021, we boldly predicted that for procurement to be recognized as a driver of strategic growth and enterprise value, CPOs would need to transform their operating models. This meant embracing a best-of-breed ecosystem that enables multiple digital solutions to work seamlessly together, enhancing user experience while leveraging advanced technologies to eliminate repetitive manual tasks and empower teams to focus on more meaningful, value-creating work.

Fast forward to 2025—and that vision is becoming reality. AI is elevating procurement professionals from being perceived as back-office operators to becoming consultants and trusted advisors with profound business acumen.

In that same set of predictions, we also highlighted a critical shift that’s now materializing: Procurement processes must become autonomous and self-serve whenever possible. By democratizing procurement—particularly for spend outside of procurement’s direct control—generative AI is making this transformation a reality.

Organizations we’ve been engaging with since then are now telling us that in 2025, the question is no longer if AI should be leveraged in procurement, but rather who will adopt it—and how quickly. The momentum is undeniable, building to such a degree that it’s hard to imagine any other technology dominating the conversation over the next twelve months.

3. The Next Big Thing: Agentic AI

Agentic AI has the power to completely revolutionize the field. Why? Because it promises to deliver exactly what CPOs have long been dreaming of.

When I talk to our customers about the future of [Globality’s AI Agent] Glo, I describe a scenario where they can simply ask: ‘Hey Glo— could you scan my spend portfolio and find the top three categories that have the most supplier fragmentation and recommend which 15% of my supply based could handle the majority of my spend?"’

When procurement leaders hear that, they tell me, ‘That’s my dream.’ Could 2025 be the year that dream starts to come true? Only time will tell, but my team firmly believes that Agentic AI is on the verge of making it a reality.

4. The Value of AI in Procurement is in the Billions and Counting

Early adopters of AI in procurement are delivering such significant value to their organizations that their contributions are now being highlighted in company annual reports, as has happened with BT. BT noted that '[Globality’s] generative AI features are accelerating our scoping processes and streamlining how we define our needs, while its new E-Negotiation and online NDA tools are simplifying the entire sourcing process.' Similarly, Santander has reported notable benefits, and Harold Wu, CPO at T. Rowe Price, achieved over  $40 million in operational savings—a success highlighted by the company CEO in The Financial Times’ Ignites news service. Expect more such public declarations to emerge soon.

Looking ahead to 2025 and beyond, we estimate that the augmentation of the CPO’s office with these advanced technologies will unlock substantial savings and redefine the strategic role of procurement in driving enterprise value.

5. A New Class of AI-driven CPOs Will Lead the Function

Forward-thinking Chief Procurement Officers who are stepping up to the plate to remind us of the value of the function, and what a new, AI-led, data-driven approach to it can offer are on the rise.

This isn’t solely about Globality’s customers—it’s a broader trend led by trailblazers like Paula Glickenhaus at Bristol Myers Squibb, Cyril Pourrat at BT Sourced, Charles Letizia at Tesco and Matt Prichard at Fidelity. These leaders are leveraging autonomous sourcing to revolutionize their organizations’ indirect spend strategies and embracing cutting-edge, digital-first methodologies.

There’s a palpable buzz within the profession about the new ‘rock star’ status of forward-thinking CPOs. Millennials and Gen Z business leaders are also driving this momentum, rejecting outdated, cumbersome RFx processes in favor of fast, efficient digital solutions. In 2025, we’ll see even more innovative CPOs emerging and a surge of interest from digital natives in this historically under-automated part of the business."

Click here to book a demo or here to explore more customer stories and see firsthand how AI-driven sourcing is shaping the Future of Procurement.

As the year draws to a close, it’s an ideal moment to reflect on the transformative impact of AI-driven autonomous sourcing—not only on the bottom line amid continued economic uncertainty but also on people’s lives across the globe. In 2024, organizations that embraced this next-generation technology have achieved incredible results, driving growth and efficiency while delivering tangible, life-changing benefits to individuals and communities.

Driving Speed-to-Market for New Healthcare Treatments

In 2024, Globality helped increase the speed of the clinical development cycle at a €5bn Pharma company, using the power of native-built AI to ensure novel therapies were with patients quicker, giving them new hope for conditions such as auto-immune and other rare diseases.

In healthcare, where every second counts, autonomous sourcing has enabled global life sciences companies to scale their procurement operations and uncover new opportunities. For instance, one leading life sciences organization increased the amount of spend competitively sourced by an astonishing 500%—and did so without needing to add a single new member to its procurement team. By leveraging AI, it optimized workflows, reduced manual effort, and focused on what matters most: delivering innovative solutions to patients worldwide.

Helping Families Through the Cost-of-Living Crisis

Closer to home, AI-driven sourcing is also helping consumers at a more immediate level. As the cost-of-living crisis continues to impact families across Europe, Globality’s platform has enabled one of the continent’s leading grocery retailers to maintain low prices for essential goods. By optimizing supplier selection and negotiating better terms through AI, the company ensured that families could afford a little more during the holidays—whether it’s a special meal or gifts for loved ones.

This highlights how procurement, when powered by advanced technology, extends far beyond the back office. By reducing costs and increasing efficiency, businesses can reinvest savings into keeping products affordable, supporting their customers, and delivering greater value when it’s needed most.

Transforming Procurement Operating Models

While these individual successes are powerful, the broader trend is equally transformative. Leading global enterprises have used autonomous sourcing to revolutionize their procurement operating models, empowering teams to do more with less. These organizations have proven that AI can help drive enterprise-wide growth while allowing procurement to evolve into a more strategic, value-adding function.

Consider the following examples of how top organizations are redefining procurement:

• A global life sciences company increased competitively sourced spend by 500% using the same team, demonstrating the power of AI to enhance efficiency without adding headcount.
• A Fortune 500 asset management company automated more than $400 million of spend within just two quarters, freeing up resources to focus on strategic initiatives.
• A leading market retailer leveraged autonomous negotiations to achieve $100 million in savings, unlocking value that could be reinvested elsewhere in the business.
• A top healthcare company empowered its business stakeholders to run over 1,500 sourcing projects autonomously, enabling procurement talent to focus on developing category strategies and strengthening business partnerships.

These examples underscore a critical shift: procurement is no longer just about cost-cutting. It’s about driving measurable business outcomes, increasing speed-to-market, and enhancing collaboration across the enterprise.

A Fair and Transparent Ecosystem

At Globality, our mission has always been clear: to build a fair, transparent, and innovative ecosystem for how companies do business together. In today’s fast-paced and uncertain world, this mission has never been more relevant. AI-driven autonomous sourcing is leveling the playing field, enabling organizations to operate efficiently, unlock savings, and reinvest resources into growth and innovation.

Looking ahead to 2025, we’re excited to build on this momentum and continue supporting businesses in achieving their goals. Whether it’s improving healthcare access, supporting families during economic challenges, or helping enterprises transform their operating models, the potential of autonomous sourcing is limitless.

Join Us on This Mission

The results speak for themselves: AI-driven autonomous sourcing isn’t just a technology upgrade—it’s a strategic enabler that empowers organizations to achieve more with less.

If you’d like to learn more about how Globality can help your business drive savings, streamline procurement, and make a meaningful impact, we invite you to join us on this journey.

Click here to book a demo or here to explore more customer stories and see firsthand how AI-driven sourcing is shaping the Future of Procurement.

Procurement teams, like all business functions, are looking to utilize the power of AI to deliver savings, efficiency, and strategic value — while navigating increasingly complex data and processes. We are meeting these challenges head-on with Glo, the most advanced AI Agent in procurement, and have introduced groundbreaking pricing analysis capabilities that empower Fortune 500 and Global 2000 companies to make faster, better buying decisions across all their enterprise spend.

Revolutionizing Procurement with Next-Gen Analytics
As part of the journey from scope to savings, procurement and business teams can simply ask Glo for detailed project pricing information in natural language. Our award-winning platform, which manages all spend from tail through to complex service categories, instantly creates detailed, actionable, and easily shareable data in the form of AI-powered summaries and dynamic charts and visualizations.

Leading Global 2000 enterprises, including Fidelity Investments, HP, Invesco, Santander, Tesco, and T. Rowe Price, have turned to Globality to better manage company spend. By synthesizing internal and third-party data, Glo enables these organizations to evaluate proposals with confidence and deliver measurable business outcomes.

Delivering New Business Value Amid Complexity
The need for advanced analytics in procurement is more urgent than ever. Gartner’s Procurement Predicts 2025 Report reveals that 63% of procurement organizations fear losing their competitive edge unless they improve their use of data and analytics. While AI promises increased productivity, reduced costs, and faster decision-making, these benefits can only be achieved if procurement teams trust the outputs of their technology.

This is where Glo excels. By providing real-time pricing insights and autonomous analysis capabilities, Glo enables procurement teams to focus on what matters most — strategic decision-making and driving sustainable, bottom-line value.

How Glo’s Capabilities Are Redefining Procurement
Glo’s enhanced analytic tools represent a major leap forward in the way procurement teams approach pricing and proposal evaluation. These features work together to solve critical challenges:

Analytics: Glo provides a foundational layer of intuitive, real-time analytics. Using Agentic AI, procurement professionals can create interactive dashboards, visualizations, and trend reports in seconds, ensuring smarter decisions at every step of the buying journey.

Next-Gen Proposal Insights: This feature builds on Glo’s analytic foundation, offering a deeper dive into pricing data. By using Agentic AI to perform analysis and make recommendations based on industry standards and historical data, Globality gives users a competitive edge in pricing strategies. With this tool, users gain a strategic edge in proposal evaluation and pricing strategies.

Glo Pricing Insights: This feature brings everything together by leveraging Globality's agentic architecture to provide immediate, actionable answers to pricing inquiries from procurement or business users. 

Driving Competitive Advantage with AI
These capabilities don’t just streamline processes—they revolutionize them. With Glo, procurement leaders can better manage risk, enhance productivity, and unlock new opportunities for value creation.

Our mission at Globality has always been ambitious: to transform enterprise spending into a smarter, more inclusive process. With nearly a decade of innovation behind it, we continue to lead the charge in AI-driven procurement solutions. By offering actionable insights, seamless automation, and a human-like approach to decision-making, Glo empowers businesses to thrive in an era of unprecedented complexity.

For procurement leaders looking to stay ahead, Glo’s status as the most advanced AI Agent in the industry, represents a vital competitive advantage enabling them to better manage risk, boost efficiency and productivity, and create new value that goes straight to the bottom line. Through Glo’s next-gen AI-powered analytics, companies gain the tools to drive better business outcomes in the face of unprecedented market and internal complexity.

Historically hard-to-procure spend areas that are complex and high-value, like indirect spend or sourcing of services, have proven an enduring bottleneck for technology. Machines struggled to comprehend nuanced requirements, leaving such tasks firmly in the hands of human expertise. However, the landscape is rapidly shifting. By 2025 and beyond, advancements in AI promise a revolutionary change. Procurement professionals could shed the burden of crafting exhaustive briefs. Instead, they could collaborate with AI systems honed by years of experience and vast datasets of procurement workflows.

These AI partners would provide immediate access to tailored insights and resources. This could enable procurement teams to launch efficient and highly customised sourcing strategies. This evolution not only optimises time and resources but also positions the CPO as a strategic leader within the organisation.

Synthesise in seconds to give accurate and data-driven answers

Customers describe this transformation as nothing short of game-changing.  A recent independent assessment into the benefits of the technology, conducted by analysts at global research and analysis firm HFS,  highlights just how transformative this technology is. It introduces an unprecedented level of transparency to procurement—something the industry has long strived for. Historically, bidding for complex services has meant painstakingly crafting lengthy, intricate RFPs. This has often made it challenging to fairly and thoroughly evaluate vendors’ solutions, pricing, and alignment with requirements like diversity, sustainability, and cultural fit.

Doing this repeatedly at scale is a Herculean task. But imagine a future where you can discuss every aspect of a sourcing brief with an AI partner. THat partner would be a tireless, infinitely patient system with flawless recall and meticulous attention to detail. These emerging AI systems are not just tools; they act as informed colleagues. They process and synthesise vast amounts of data in seconds, offering precise, data-driven insights.

Better still, they simplify the process of generating market-ready responses, ensuring alignment with your organisation’s policies on compliance, governance, and ethical sourcing. The result? Procurement teams are freed from 99% of the drudgery, empowered to focus on strategy and innovation, while achieving fairness and transparency at levels previously unattainable.

AI agents

By partnering with this new class of AI agents, buyers not only work smarter, they also enhance their reputation across the enterprise. After all, introducing new, data-driven processes that identify the best providers, proposals, and outcomes will lead to improved business metrics, enabling procurement to add more strategic value and help drive new growth. As the effectiveness of these processes becomes more visible, increasing numbers of stakeholders—including those typically a little suspicious of what they see as over-rigid procurement strictures—become engaged, bringing more spending under management.

However, it’s important to emphasise that, unlike other areas, in complex sourcing we’re not yet talking about machine-to-machine-only transactions. Complex purchasing decisions always involve relationships; buyers and sellers need to feel confident and trust each other. 

Developing synergistic partnerships with AI

To be honest, I can’t stress enough that relationships, both internal and external, will always be crucial in procurement. These will continue to be managed and led by people, while AI agents do the background work and the heavy-lifting and manual work that at the moment slows you down. In fact, a large part of B2B procurement will increasingly be driven and orchestrated by human experts working in productive and synergistic partnerships with AI.

My advice is to approach AI as you would any trusted colleague—by investing time and effort into building a strong partnership. The potential value lies in the prompts, strategies, and guidance you provide to unlock the power of machine learning, predictive analytics, and Agentic AI within your organisation. For example, you might ask, “Scan my spend portfolio to identify the top three categories with the most supplier fragmentation. Recommend which 15% of my supply base could manage the majority of my spend.”

Autonomous sourcing can then pinpoint categories like employee learning and development, cleaning services, and marketing events, which collectively involve over 2,000 suppliers. With sufficient data, it can instantly identify the 14 suppliers in each category capable of managing the majority of the volume. From there, it outlines three-panel sourcing projects, selecting the most suitable suppliers for each event and providing a clear, actionable framework for execution.

Applying lessons for your team’s benefit

Similarly, you could ask, “Who in my company has run a penetration testing services project in the last 18 months? What were the results, which suppliers bid, and what were the planned contract durations? Provide any insights I could benefit from.” The AI identifies three projects conducted in Europe, LATAM, and the US. Remarkably, it uncovers that one supplier was awarded two of these projects at different price points—something no one had realised.

Within seconds, the system also highlights that one project is performing better financially than any of the previous bids. It recommends consolidating all three projects into a single sourcing event to capitalise on efficiencies before the contracts expire. The AI then prompts the user: “Would you like to combine these volumes and create a new project?” The response? A resounding yes.

By the way, these are all queries you can make today, not tomorrow. So, my advice to the forward-thinking Chief Procurement Officer as we close out 2024 is to start thinking ahead. These are the kinds of prompts you should be exploring with your new AI partners in 2025. Now is the time to get familiar with how AI can reshape your procurement strategy and unlock unprecedented value.

Click here to book a demo of our award-winning AI-driven souring platform and see how you can super-charge your team's impact across the enterprise.

The statistics tell an alarming story: according to IBM's 2024 Cost of a Data Breach Report [1], the average cost of a data breach has now reached $4.88 million. The report highlights an emerging concern: attackers are increasingly leveraging AI and automation tools, adding complexity to the threat landscape and extending breach lifecycles by weeks or months.

But beyond these numbers lies a more nuanced reality; as AI transforms business processes, it creates novel attack surfaces and security challenges that traditional frameworks struggle to address. The rapid adoption of AI technologies across industries has fundamentally changed the security landscape, introducing complexities that weren't even contemplated in traditional security frameworks. Organizations now face the dual challenge of protecting not just their data, but also the AI models that process it, the training data that shapes these models, and the outputs they generate.

At Globality, we've learned that robust security isn't just about checking boxes; it's about building a comprehensive shield around customer data that evolves with emerging threats. While we're proud of our ISO 27001 certification and SOC2 compliance (both recently reaffirmed through rigorous audits), we see these as foundations rather than finish lines. These certifications represent our commitment to maintaining rigorous security standards, but our actual security practices go well beyond what these frameworks require. We've built our security infrastructure with the understanding that in today's landscape, compliance is merely the starting point.

Beyond Standards: Security in Practice

The rise of AI has introduced new complexities in data protection that few organizations are fully prepared to address. Traditional security models often fall short when confronted with the unique challenges of AI systems.

Data residency requirements have become increasingly complex, varying not just by country, but by data type and processing stage. Organizations must now track and control data flows across multiple jurisdictions, ensuring compliance with a patchwork of regulations while maintaining system performance. This becomes particularly challenging when AI models need to process data across borders or when training data comes from multiple jurisdictions.

The protection of both training data and model outputs presents another layer of complexity. At Globality, we've developed sophisticated approaches to this challenge. Our AI models fall into two main categories; classifiers for categorizing projects based on natural language descriptions, and clustering/ranking models for provider matching. Each requires its own careful security consideration. For classifier training, we've implemented a rigorous four-step anonymization and sanitization pipeline:

• We systematically remove client-specific identifiers and codes
• We employ Named Entity Recognition (NER) to identify and remove personal and company proprietary information
• We use sophisticated techniques like Normalized Google Distance (NGD) calculations to identify and redact client-specific entities
• We implement reproducible name mangling for sensitive terms

This process, performed with heavy involvement by Globality InfoSec personnel, ensures that while our models can learn from aggregate patterns, they never retain or expose sensitive client information. We're particularly stringent about personal identifiable information (PII); it's completely excluded from our training pipeline.

For our clustering and ranking models, which process a wide array of supplier data, we've implemented strict controls on data sourcing and usage. While we collect data from public, private, and proprietary sources, we maintain clear boundaries around what data can be used for model training. We've explicitly designed our systems to exclude sensitive proprietary data from model training without explicit customer consultation and approval.

Ensuring AI systems maintain security during both training and inference requires a comprehensive approach that traditional security frameworks don't fully address. During training, we must protect against data poisoning attacks and unauthorized access to training data. During inference, we need to guard against model extraction attacks and ensure that model outputs don't leak sensitive information.

To address these challenges, we've implemented advanced security measures that go beyond standard practices. For example, our Hold Your Own Key (HYOK) encryption capability gives customers complete control over their data while maintaining functionality, and our AI Governance Committee provides oversight of all AI changes from model improvements to prompt evaluation.

IP allowlisting is another critical component of our security infrastructure that we've implemented as part of a broader zero-trust architecture to protect every point of ingress (including both web application and REST API requests). This means every access attempt, whether from an allowed IP or not, undergoes rigorous authentication and authorization checks. We've also adopted the ISO 42001 framework for AI systems, ensuring security is built into our AI operations from the ground up, not bolted on as an afterthought.

Practical Security Advice for the AI Era

Based on our experience protecting sensitive enterprise data, we've developed a comprehensive set of security principles that organizations should consider as they build out their AI infrastructure:

• Treat AI model inputs and outputs with the same rigorous security controls as your raw data. Many organizations focus on securing their databases but leave model interactions inadequately protected. This creates vulnerabilities that sophisticated attackers can exploit. Recent research has demonstrated how seemingly innocuous model outputs can potentially be used to reconstruct training data [2]. That's why we've implemented end-to-end encryption for all model interactions, ensuring that data remains protected at every stage of processing.
• Implement granular access controls as part of a comprehensive zero-trust architecture. IP allowlisting is just the beginning; every access request should be validated regardless of its source. This means implementing strong authentication mechanisms, regular access reviews, and detailed audit logging. At Globality, we maintain comprehensive audit trails of all system access, enabling us to detect and respond to potential security incidents quickly.
• Encryption should be end-to-end, with customer control over keys. Our HYOK implementation demonstrates that robust encryption doesn't have to come at the cost of functionality. We've developed sophisticated key management systems that allow customers to maintain complete control over their data while still leveraging our AI capabilities. This includes automatic key rotation, secure key storage, and immediate revocation capabilities.
• Implement comprehensive data anonymization pipelines for AI training. This isn't just about removing obvious identifiers; it requires a sophisticated multi-layer approach that considers context and relationships between data points. Our experience shows that effective anonymization must combine rule-based systems, machine learning techniques, and careful human oversight.
• Embrace data minimization as a fundamental security principle. At Globality, we rigorously define what data we need and only use the data required. We also carefully select our models and vendors, and we don't allow third parties to use any data we send through their APIs for AI training. This approach follows a core security principle: the best way to mitigate risk is to minimize the data you hold in the first place. By being intentional about data collection and processing, organizations can significantly reduce their attack surface and potential exposure.

The Path Forward

The reality is that security in the AI era requires a fundamental shift in how we think about data protection. It's not enough to secure static data; we need to protect information as it flows through increasingly complex AI systems. This means implementing security controls that understand and account for the unique characteristics of AI workflows, from data ingestion through model training and inference.

Our commitment to security at Globality goes beyond maintaining certifications and implementing standard security measures. We're continuously evolving our security infrastructure to address emerging threats and protect our customers' data in an increasingly complex technological landscape. By sharing our experiences and insights, we hope to contribute to the development of more robust security practices across the industry.

This approach has not only helped us maintain the trust of our enterprise customers but has also positioned us to adapt to emerging security challenges as AI technology continues to evolve. The future of AI security will require even more sophisticated approaches to data protection, and we're committed to staying at the forefront of these developments.

Click here to book a demo of our award-winning AI-driven sourcing platform.

References

[1] IBM Security. "Cost of a Data Breach Report 2024." IBM Security, 2024. https://www.ibm.com/reports/data-breach

[2] Haim, N., Vardi, G., Yehudai, G., Shamir, O., & Irani, M. "Reconstructing Training Data From Trained Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) 2022. https://openreview.net/forum?id=Sxk8Bse3RKO