Executive Summary. Kevin Frechette explains how agentic AI is transforming procurement into an autonomous, compliant, and scalable operating system.
In this insightful interview, Kevin Frechette, Co-Founder and CEO of Fairmarkit, shares his journey from working at IBM and Dell to pioneering an AI-powered sourcing platform. He discusses the challenges of AI adoption in procurement, the evolution of agentic AI, and how it is reshaping efficiency and compliance in the industry. Kevin also highlights Fairmarkit's success in transforming procurement processes for clients like Sonoco, offering valuable advice for entrepreneurs looking to leverage AI in solving complex business problems. Read on to explore the future of AI in procurement and how businesses can scale smarter and faster.
Your journey from working at IBM and Dell to co-founding Fairmarkit is inspiring. What pivotal experiences or lessons during that period shaped your approach to building an AI-powered sourcing platform?
My time at IBM and Dell introduced me to the world of enterprise procurement—particularly the untapped potential of tail spending. I observed how legacy systems and manual processes created friction, delayed transactions, and limited access to diverse suppliers. These experiences made it clear that procurement needed a transformation, not just incremental improvements.
Automation alone wasn’t enough—procurement needed intelligence. AI had to move beyond simple automation and into augmentation, where it could drive decision-making, compliance, and efficiency at scale. That realization led to the creation of Fairmarkit, where we leverage AI to automate sourcing while keeping procurement teams in control, enabling them to focus on strategy rather than administrative tasks.
Fairmarkit has gained recognition as a leader in autonomous sourcing. How do you define “agentic AI,” and why do you believe 2025 is the tipping point for its adoption in procurement?
Agentic AI represents the next evolution of AI in procurement—it moves beyond task automation to fully autonomous decision-making and execution. Unlike traditional AI, which reacts to prompts, agentic AI acts proactively. It can negotiate terms, verify compliance, and execute workflows without human intervention while collaborating with other AI agents.
2025 is the tipping point because the barriers to entry have significantly lowered. GenAI introduced AI-powered assistance, but agentic AI will fundamentally reshape workflows. Enterprises are shifting from pilot programs to full-scale deployments as they realize the ROI—faster procurement cycles, reduced costs, and improved supplier relationships. With procurement teams increasingly stretched thin, agentic AI is becoming a necessity, not a luxury.
What challenges have procurement teams faced historically when trying to adopt AI solutions, and how does Fairmarkit address these pain points?
Historically, procurement AI adoption has been hindered by these challenges:
Complex Implementation – Legacy AI solutions require significant IT and engineering resources, making adoption slow and costly.
User Resistance – Procurement professionals feared AI would replace them rather than augment their capabilities.
Data Silos & Bias – AI models struggled with fragmented data sources and potential bias, impacting accuracy and trust.
Fairmarkit tackles these issues by providing low-code/no-code AI solutions, making adoption seamless for enterprises without deep technical expertise. Our AI is designed to be an extension of procurement teams, not a replacement, ensuring human oversight remains integral. We also emphasize data diversity and transparency to mitigate bias, ensuring fair and ethical AI-driven sourcing.
In industries with heavy regulation, how do you ensure that AI agents can act autonomously while maintaining compliance and ethical standards?
Regulated industries require AI that is autonomous yet auditable. At Fairmarkit, we ensure compliance by prioritizing transparency and explainability in our AI models, allowing procurement teams to understand and justify AI-driven decisions. While AI autonomously executes workflows, human oversight remains essential, ensuring compliance in high-stakes scenarios.
AI governance is embedded directly into our sourcing processes, aligning with regulatory frameworks like the EU AI Act to ensure fairness, privacy, and accountability. By designing AI to operate within clear ethical boundaries, we enable procurement teams to scale AI adoption without regulatory risk.
What are some of the most exciting, high-stakes applications of agentic AI that you believe will unlock unprecedented efficiency and scalability?
Agentic AI is set to redefine procurement efficiency by revolutionizing supplier negotiations, risk mitigation, and procurement transparency. AI agents will be able to negotiate terms, pricing, and contract conditions in real-time, significantly reducing cycle times and improving outcomes. AI-driven risk mitigation will allow companies to proactively analyze supply chain disruptions and adjust sourcing strategies based on global market conditions.
Beyond efficiency, agentic AI will also create a more inclusive procurement ecosystem by enabling minority-owned and emerging suppliers to compete effectively through automated qualification and transparent decision-making. The potential for scaling procurement operations while improving both cost efficiency and supplier diversity is what makes this technology so transformative.
As the CEO of a rapidly growing company, how do you balance the need for innovation with the operational demands of scaling a business?
Scaling a company requires striking the right balance between agility and discipline. At Fairmarkit, innovation is at the core of everything we do, but we ensure sustainable growth by staying laser-focused on solving real procurement challenges. Instead of chasing hype, we prioritize AI solutions that drive measurable impact for our customers.
Scalability is also embedded in our infrastructure, with flexible, cloud-native AI solutions that can adapt as enterprise needs evolve. But none of this would be possible without a strong company culture and a team that embraces both innovation and execution. Keeping the right people in place is just as critical as the technology itself.
How do you see the role of procurement professionals evolving as AI continues to automate and streamline sourcing processes?
As AI takes over repetitive procurement tasks, professionals will shift their focus from transactional execution to strategic enablement. Procurement teams will have deeper insights into supplier performance, allowing them to make smarter, more data-driven decisions. With AI handling operational tasks, professionals will have more time to build stronger supplier relationships and drive long-term value.
Another key evolution will be in governance. Procurement professionals will oversee AI-driven sourcing strategies, ensuring compliance, ethical sourcing, and optimal decision-making. Instead of replacing procurement teams, AI will empower them to operate at a much higher level, turning procurement into a true business driver.
What metrics or indicators do you use to measure the success of Fairmarkit’s AI implementations in delivering value to your clients?
Success in AI-driven procurement is measured by tangible business outcomes. Cost savings, reduced sourcing cycle times, and increased supplier participation are all critical indicators of AI effectiveness. Procurement teams should also look at compliance improvements and risk reduction, as AI can enhance governance and reduce exposure to regulatory issues.
Beyond efficiency, user adoption and satisfaction are key. The most advanced AI in the world is ineffective if procurement teams don’t embrace it. Ensuring that AI solutions are intuitive, transparent, and easy to integrate into existing workflows is a major part of how we measure success.
Can you share a case study or real-world example of how Fairmarkit has transformed procurement processes for one of your clients?
A great example is our work with Sonoco, a global packaging leader, in transforming procurement across their Latin America (LATAM) operations. Their decentralized procurement model allowed for regional autonomy, but highly manual processes led to inefficiencies, prolonged cycle times, and limited visibility. Buyers relied on phone calls and emails to request and evaluate quotes, making sourcing inconsistent and time-consuming. By integrating Fairmarkit with Coupa, Sonoco standardized purchasing workflows, automated RFQs, and created a centralized system for procurement teams to manage sourcing requests efficiently. In just two weeks, they went live, processing 100+ sourcing requests daily and achieving 60 sourcing events in a single day, reducing cycle times while strengthening policy compliance and unlocking cost savings.
The implementation expanded Sonoco’s supplier base, surfacing competitive suppliers from their database and improving cost savings. Teams gained greater visibility and control, tracking sourcing behavior to prevent single sourcing and optimize decision-making. Automating the RFQ process eliminated manual errors, reduced PO rework, and streamlined approvals, ensuring the buying process remained uninterrupted—even when key team members were unavailable. Sonoco’s rapid success led to expansion into multiple categories and sites, demonstrating how AI-driven procurement can transform efficiency, compliance, and scalability at a global level.
What advice would you give to other entrepreneurs looking to leverage AI and automation to solve complex business problems?
The key to building AI-driven solutions is to start with the problem, not the technology. AI should be used to solve real business pain points rather than being an end in itself. Adoption should be seamless—great AI is useless if it’s not user-friendly. Transparency and ethics must also be prioritized to build trust and ensure AI decisions are fair, explainable, and aligned with business goals.
Most importantly, AI innovation requires an experimental mindset. The field evolves rapidly, and successful entrepreneurs must stay agile, iterate based on real-world feedback, and continuously refine their solutions. The companies that embrace AI now will lead the next wave of transformation across industries.
