As Procurement Director at Emerson, Samer Haddad brings a powerful combination of analytical precision, digital foresight, and pragmatic leadership to the evolving world of procurement. In this Executive Insight interview, he explores how AI, advanced analytics, and predictive modelling are redefining procurement’s role, from driving smarter supplier performance to enabling proactive risk mitigation. With a keen focus on data integrity, cross-functional collaboration, and strategic alignment, Samer shares how procurement leaders can balance emerging technologies with human innovation to create resilient, agile, and forward-looking procurement functions.
How are you leveraging AI and advanced analytics to improve procurement decisions?
I leverage AI and advanced analytics extensively to enhance procurement decisions. These technologies provide a bird’s eye view, sometimes even feeling like superpowers, turning complex data into clear charts and actionable trends. Instead of manually sifting through vast datasets, I now spend my time strategically interpreting these insights.
While AI tools are not yet universally prevalent in procurement, I personally use AI driven analytics extensively in areas such as evaluating the content I create, further confirming their potential and future importance. AI rapidly processes and analyses large volumes of data, directly informing procurement strategies through realistic predictive analytics. This enables anticipation of market trends, supplier behavior, and price fluctuations, significantly improving negotiation outcomes and business agility.
AI’s forecasting capabilities also empower real time inventory management, optimise ordering schedules, and proactively mitigate risks. Ultimately, these advanced tools shift the focus from operational execution to strategic management, enhancing efficiency, reducing costs, and allowing those who leverage AI to focus on human innovation. All of this is aligned with long term organisational and cross departmental objectives.
What data do you prioritise when evaluating supplier performance?
Before evaluating supplier performance, I always advise businesses to first understand how their data is collected, whether it’s generated by people, systems, or a combination of both, and to be clear about its intended use. Assuming that clarity is established, I prioritise data that reflects supplier reliability, quality, cost efficiency, and, where available, sustainability practices.
Key performance metrics include fundamental KPIs such as on time delivery rates, product defect rates, returns, and adherence to contractual terms. I also assess the effectiveness of suppliers’ communication, an area that is critical but often less data driven.
Sustainability indicators, though sometimes difficult to obtain at an individual supplier level, are increasingly important and should not be overlooked. These include environmental compliance, carbon footprint, and social responsibility scores.
In addition, applying advanced analytics to historical performance data is essential for proactively identifying and addressing potential risks or reliability issues. This is also where AI plays a growing role. By leveraging AI, we can further expand our evaluation capacity, using it to extract insights from reports, audits, and correspondence. While these are primarily text based today, AI may soon be able to quantify this data into meaningful metrics, such as sentiment scores, enabling even more comprehensive and consistent supplier assessments.
How has AI helped you automate and optimise certain parts of your procurement, such as risk mitigation in your supply chain?
AI has not yet extensively enhanced my procurement processes, and I believe that, globally, adoption is still limited. However, the potential is truly transformative. Despite current challenges such as corporate AI readiness and security concerns, we are only at the early stages of AI adoption.
The future impact of AI on procurement, especially in automating and optimising risk mitigation across the supply chain, is revolutionary. AI driven tools will soon be able to systematically monitor vast streams of global market data, geopolitical events, material movements, existing contracts, audits, and even potentially email communications. Combined with historical supplier performance, these tools will proactively identify risks in ways we’ve never been able to before.
AI powered analytics will also help pinpoint vulnerabilities, suggest contingency plans, and enable rapid responses. By automating assessments that once took weeks or months, AI enables proactive, rather than reactive, risk management, significantly reducing the impact of supply chain disruptions.
Furthermore, by freeing up resources, AI allows procurement teams to focus on more strategic initiatives, such as building supplier relationships and driving innovation. In this way, AI will transform procurement into a more resilient, responsive, and strategically agile function, capable of managing risks more effectively across the entire supply chain.
How do you ensure data accuracy and reliability in your procurement process?
As I hinted when discussing supplier performance analysis, ensuring data accuracy and reliability in procurement is critical. It often involves addressing a common corporate dilemma: needing accurate data to justify system upgrades, while those very upgrades are required to improve data accuracy, the classic chicken and egg problem.
I approach this systematically, combining technology, structured processes, and consistent verification methods, including both system driven and manual checkpoints, especially when working with legacy systems. Ideally, automated data validation and cleansing tools should swiftly identify and correct inaccuracies, helping to maintain data integrity.
Standardised data governance practices, clear documentation, and well defined roles and responsibilities further reinforce accuracy. Regular audits and comprehensive training promote accountability, encourage meticulous data management, and support continuous improvement.
Even with automated systems, teams must maintain critical thinking and continue to question the data. Cross functional reviews and stakeholder validations ensure alignment and reinforce confidence in procurement analytics. This rigorous approach significantly reduces risk, streamlines operations, and enhances the strategic impact of procurement decisions, keeping them closely aligned with broader organisational goals.
Can you share examples of how predictive analytics has improved procurement outcomes?
Predictive analytics significantly improves procurement outcomes through proactive decision making, precise demand forecasting, and efficient risk management. Even basic predictive models, analysing backlogs, historical trends, confirmed orders, and high probability workloads, enable accurate forecasting of team readiness and resource requirements.
This approach optimises operational readiness, maintains cost efficiency throughout the source to pay cycle, and ensures consistent organisational preparedness year after year. Predictive analytics also facilitates early detection of supplier performance issues, allowing for preemptive interventions that mitigate disruptions effectively.
Additionally, straightforward predictive analyses help forecast commodity pricing and annual inflation rates, supporting more robust cost estimations and strengthening supplier negotiations, ultimately reducing procurement costs.
By enhancing efficiency, cost management, and strategic preparedness, predictive analytics, at all stages of maturity, demonstrably strengthens procurement resilience and delivers measurable improvements in performance and strategic alignment.
