From Spreadsheets to AI Agents: Reinventing Hardware Procurement for the Next Generation of OEMs

Hardware procurement has remained largely manual and fragmented for decades. Despite advances in software and automation, sourcing critical components, evaluating suppliers, managing costs, and documenting procurement decisions often still rely on spreadsheets, email chains, and disconnected systems.

What changes when procurement becomes AI-native?

To explore that question, KASPA sat down with Aftab Farooqi, Ph.D., founder and chief executive officer of ChipHub, to discuss the vision behind the company and how artificial intelligence is poised to transform the hardware procurement process.

ChipHub has built an AI-native procurement intelligence platform designed specifically for hardware OEMs. The platform brings together sourcing, supplier intelligence, alternate-part analysis, decision documentation, cost tracking, and procurement workflows into a unified system. By reducing reliance on manual processes and fragmented data sources, ChipHub aims to help organizations make faster, more informed, and more resilient procurement decisions in an increasingly complex global supply chain.

In this exclusive interview, Dr. Farooqi shares his perspective on the challenges facing today's procurement teams, the role of AI in modernizing sourcing operations, and how intelligent procurement platforms may reshape the future of hardware manufacturing.

Q: Procurement has traditionally relied on spreadsheets, emails, ERP systems, and distributor portals. What are the biggest inefficiencies you see in today's hardware procurement process?

A: Procurement today is still very fragmented. OEMs have to work across spreadsheets, emails, ERP systems, and distributor portals at different stages of the product lifecycle, which makes it hard to keep visibility, communication, and decisions aligned.

The challenges also change by phase. During NPI, limited component visibility and slow sourcing cycles can delay product launches and time-to-market. In production, shortages and reactive buying can disrupt schedules and hurt revenue and margins. In the sustaining phase, part obsolescence and a lack of forward-looking supply chain insight can lead to expensive redesigns and lifecycle issues.

Q. ChipHub describes itself as an "AI-native procurement intelligence platform." What does AI-native mean in practical terms, and how is it different from simply adding AI features to existing procurement software?

A: AI-native means AI is built into the core of the platform, not added later as an extra feature. In practice, things like component discovery, risk analysis, alternate part recommendations, and supply chain insights all rely on AI to work.

Traditional procurement software usually runs on fixed workflows, with AI added on top for things like chat or basic automation. An AI-native platform, on the other hand, uses AI continuously in the background to analyze data, surface risks, and support decisions in real time.

Q. During the semiconductor shortages of recent years, many OEMs struggled with component availability and alternative sourcing. How can AI help procurement teams anticipate and respond to supply-chain disruptions more effectively?

A: AI helps procurement teams shift from reacting to problems to getting ahead of them. Customers can use our component availability agents to continuously monitor market conditions and spot potential shortages early.

When disruptions do happen, procurement teams can quickly use the agents to find Form-Fit-Function (FFF) compatible alternatives and evaluate sourcing options in real time.

What used to take days or weeks can now be done in minutes. On top of that, suppliers and brokers can share real-time inventory through the platform, giving OEMs faster visibility into available supply and helping reduce production risks.

Q. Engineers and procurement teams often operate in separate silos. How does ChipHub bridge the gap between engineering decisions and sourcing decisions throughout the product lifecycle?

A: We bridge that gap by getting engineering and procurement onto the same platform so they can look at the same component data in real time. Instead of switching between different tools and trading emails or spreadsheets, both teams can review parts together, compare options, and capture decisions in one place.

This creates a single source of truth for each component, including requirements, availability, and sourcing constraints. As a result, engineering and procurement stay aligned throughout the product lifecycle, which reduces rework and speeds up decisions.

Q. One of ChipHub's capabilities is identifying alternate components and interchangeability options. How much of today's sourcing effort is spent manually evaluating alternatives, and how can AI accelerate that process while maintaining engineering confidence?

A: A big part of sourcing today is still manual, especially when engineers are looking at alternative components. Each part can have hundreds of attributes to check for compatibility, which makes the process slow and hard to scale.

On our platform, customers can focus on the key attributes that matter to them and use AI agents to evaluate multiple alternatives in parallel. This brings what usually takes hours or days per component down to under 20 minutes, while also producing clear comparison reports that help engineers trust the recommendations.

Q. Procurement decisions increasingly involve more than just price. Factors such as lifecycle status, supplier risk, lead times, quality history, and total cost of ownership all matter. How does ChipHub help organizations make more informed procurement decisions?

A: We help organizations move from price-based buying to more data-driven decisions by adding over 100 structured attributes to each component, including lifecycle status, supplier risk, lead times, quality history, and total cost of ownership.

Instead of pulling this information from multiple systems, teams can see all the key signals in one place and also add custom attributes based on what matters to them. This makes it easier for procurement and engineering teams to compare options more holistically and make faster, better-informed decisions with more confidence.

Q. Many companies are exploring agentic AI. What role do AI agents play within ChipHub, and which procurement tasks do you believe will eventually become fully autonomous?

A: AI agents in ChipHub are built to handle specific procurement tasks like part search, Form-Fit-Function matching, pricing insights, and tracking stock availability. Each agent focuses on one area, but together they support the full sourcing process from start to finish.

On autonomy, we expect agents to take over more of the high-volume, repetitive work like continuous monitoring, finding alternatives, and real-time supply updates. But for critical decisions like final supplier selection or production qualification, we still believe a human-in-the-loop approach will be important to ensure accuracy, accountability, and engineering confidence.

Q. Trust and traceability are critical when making sourcing decisions worth millions of dollars. How do you ensure that AI-generated recommendations remain transparent, explainable, and auditable?

A: We ensure transparency and traceability by grounding every recommendation in verifiable data sources, with clear citations for things like supplier data, lifecycle status, and availability.

Each recommendation also includes simple, structured reasoning that shows why a component was suggested, including key attribute matches and trade-offs. On top of that, all decision paths are logged in the platform, so teams can review how a recommendation was made and reproduce it if needed.

Q. You have extensive experience in semiconductor sourcing at companies such as Google and Amazon. What lessons from those environments influenced the vision behind ChipHub?

A: The key lesson was recognizing the need to digitize many of the traditionally manual procurement processes and create a more efficient sourcing workflow.

Today, ChipHub enables customers to discover components, evaluate alternatives, collaborate with suppliers, negotiate pricing, and track spending and savings metrics, all within a single platform. By bringing these capabilities together, organizations can eliminate fragmented tools and streamline the entire procurement process.

Q. Looking ahead five years, how do you envision hardware procurement evolving? Will procurement teams work alongside AI copilots, or are we moving toward fully autonomous sourcing ecosystems?

A: Over the next five years, I see procurement teams increasingly working alongside AI copilots that continuously monitor supply markets, surface risks, and recommend sourcing options in real time.

Instead of manually searching and comparing parts, teams will shift toward reviewing AI-generated insights, validating recommendations, and making final decisions based on business and engineering constraints. Full autonomy may emerge in narrow, low-risk categories, but for most critical hardware decisions, it will remain a collaborative model between humans and AI systems.

Q. Korea and the United States are both investing heavily in semiconductors, AI infrastructure, advanced packaging, and next-generation electronics. How can AI-native procurement platforms help strengthen collaboration between global OEMs, suppliers, and technology ecosystems across these two markets?

B: AI-native procurement platforms help connect OEMs and suppliers in Korea and the U.S. by giving everyone a shared, data-driven view of the supply chain.

Instead of working in separate regional systems, teams can find components, evaluate suppliers, and share real-time availability and pricing in one place. This improves visibility across markets, reduces friction, and makes it easier for companies in different regions to work together quickly.

Over time, this helps make cross-border procurement more transparent, consistent, and responsive to changes in supply and demand.

Closing Statement

The next wave of innovation in semiconductors, artificial intelligence, and advanced hardware will depend not only on breakthroughs in technology, but also on the ability to efficiently source, manage, and scale the components that power them. As supply chains become more global and increasingly data-driven, AI-native procurement platforms may play an important role in helping organizations build greater resilience and agility.

KASPA thanks Aftab Farooqi, Ph.D., for sharing his vision for the future of hardware procurement and the role AI can play in transforming sourcing and supply-chain decision-making. We look forward to continuing the conversation around technologies and innovations that strengthen collaboration across the Korea-United States semiconductor and AI ecosystems.