Bristol Myers Squibb has taken an unusual approach to artificial intelligence in procurement: it chose to start before its data was fully polished.
Rhonda Griscti, the company’s executive director of digital strategy and global process lead, said the pharmaceuticals group decided against waiting for a flawless data environment. Instead, it began building while cleaning information in parallel, a move she says has already delivered a major reduction in the time needed to run s...
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upplier tenders.
According to Griscti, the company has cut its average request-for-proposal cycle from six to nine months to less than 30 days. She said the faster process is part of a broader overhaul aimed at improving supplier selection, modernising workflows and stripping out the email-driven habits that had long slowed procurement.
The shift comes as many companies remain hesitant about deploying AI without stronger data foundations. A December 2025 survey by global professional services firm RGP found that only 10% of chief financial officers fully trust the quality of their data, while more than a third said data trust was their biggest obstacle to getting returns from AI.
Scott Rottmann, president of consulting services at RGP, argued for a more measured middle ground. He said organisations do not need perfect data to move ahead, provided the information is good enough to support sound decisions, adding that firms should aim for progress rather than indefinite preparation.
That debate sits at the heart of enterprise AI adoption. Critics of rapid rollout warn that incomplete or inconsistent data can create governance and operational risks when automated systems are scaled too quickly. Supporters counter that delaying implementation in pursuit of perfection can leave companies waiting too long for real value.
John Sviokla, an AI strategist and co-founder of GAI Insights, made a similar point in an April Forbes article, arguing that companies often overdo data cleaning at the expense of actual deployment. His view was that organisations should build the processing capability first and let it show where cleaning is most needed.
At Bristol Myers, the procurement transformation began in June 2024 with an effort to standardise processes, improve capability and replace fragmented manual work. Griscti joined the company in November 2021 as a senior director in R&D procurement and later moved into her current role. She said momentum accelerated after a new chief procurement officer arrived with a mandate to modernise the function.
The next stage came in November 2024, when the company carried out a soft launch with Globality, a Palo Alto-based platform that uses AI to automate supplier discovery and selection. Bristol Myers then expanded the system across the organisation in February 2025.
The results, Griscti said, have exceeded expectations. More than $1bn passed through the platform in its first year, while the company was able to handle roughly ten times more RFPs and bring work back in-house with about half the resources previously used. The savings from reducing reliance on external providers were then redirected into technology investment.
Griscti’s message to other executives is blunt: waiting for perfect data can mean waiting forever. Her view reflects a growing belief among some enterprise leaders that the speed of AI deployment matters almost as much as the cleanliness of the underlying information, so long as firms are willing to keep improving the data as they go.
Source: Noah Wire Services
