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What Good Looks Like
How to define the right requirements, choose the right partner, and build the conditions for success.
Where AI Buying Decisions Should Start
AI buying decisions in product development should not start with feature lists. They should start with a clear picture of what success looks like. Define the technical and business decisions that matter most, then evaluate platforms against those priorities.
For most innovation leaders, that means choosing a platform that can:
Show Value Quickly
Deliver results without a prolonged setup phase
Work With Real Technical Data
Handle the complexity of actual product development environments
Scale Across Teams
Grow beyond a single project or business unit
Earn Trust Through Proven Results
Demonstrate credibility with documented outcomes
Start With Outcomes, Not Software
Good requirements gathering begins with the outcomes you want to improve first. The goal is to define the technical and business decisions that matter most, then evaluate platforms against those priorities.
Reduce Formulation Time
Accelerate the path from concept to viable formulation by eliminating manual bottlenecks.
Cut Experimental Cycles
Reduce the number of iterations needed to reach a technically sound result.
Improve Customer Responsiveness
Respond faster to customer needs and market signals with data-driven insight.
Respond to Regulatory Changes
Adapt quickly to supply and regulatory shifts without disrupting development workflows.
Look for Quick Time to Value
A strong platform should show value early. In practice, that means helping teams move from data to model to decision without a long setup phase. Good looks like a platform that gets to first model quickly and delivers technical wins on a realistic timeframe. Those numbers matter because they show whether AI can become part of how teams work, rather than staying stuck in evaluation mode.
(* These values are median values from our customers.)
DAYS TO FIRST EXPERIMENT
From data ingestion to actionable experiment suggestions
WEEKS TO FIRST WIN
Realistic benchmark for a first meaningful technical outcome
Choose a Platform That Can Scale
Early wins matter, but they are not enough on their own. Good looks like a platform that can scale across projects, teams, and business units without rebuilding the workflow each time.
Reusable Models
Build once, apply broadly. Models developed for one project should transfer to related challenges without starting from scratch.
Graphical Data Model
Preserve the nuance of product development data while making it usable and accessible across the entire organization.
Cross-Team Workflows
Scale should not depend on starting over for every new project. Workflows must carry forward as the program grows.
Early Win
Achieve initial success and key milestones.
Team Expansion
Grow the team to increase capacity.
Enterprise Scale
Expand operations for broader market reach.
Video: Upscaling AI Company Wide
Upscaling AI Company Wide Using AI to Increase R&D Efficiency – Saint-Gobain x Citrine: How does a global manufacturer turn AI into day-to-day gains?
Look for Proof, Not Just Promise
Why Proof Matters
The strongest AI partners can point to results in real chemicals and materials environments, with messy data, real constraints, and a range of use cases.
Proof in real chemicals and materials environments, not just demos or pilots, is the gold standard for AI platform evaluation.
DOCUMENTED CUSTOMER VALUE
Hundreds of millions in verified outcomes across real deployments
TOP 20 CHEMICAL COMPANIES
Trust from industry leaders operating at global scale
CITRINE CUSTOMERS WITH 3+ YEAR CONTRACTS
Secure relationships
Make Sure It Works the Way Scientists Work
Successful adoption depends on more than model quality. Scientists need to be able to use the platform naturally, trust the outputs, and connect recommendations to real technical decisions.
Support Expert Judgment
“You have these two worlds, data science and chemistry and they don’t often talk to each other, without layers of translation between them, and that is a nut that you have cracked.”
— Vijay Narasimhan, Director R&D Collaborations, EMD Electronics
Shorten the Path to the Next Experiment
“We have been measuring how long it takes to achieve a successful product development so you are able to see the improvements for the projects that are actually employing AI as opposed to those who didn’t.”
— David Thomas – Director of Research Development at Syensqo
Help Teams Learn as They Go
“You’re essentially institutionalizing the knowledge that you are generating in a way that can be easily accessed and used by your next generation of scientists or formulators coming in to the team.”
— David Cranfill, Technology & Innovation Director, Huntsman Polyurethane
What to Look For
When evaluating AI platforms for product development, good looks like a platform that meets all five criteria, not just one or two.
Scale Quickly
Across teams and projects without rebuilding workflows
Get to Value Quickly
With realistic, benchmarked timelines for first results
Show Real Proof
In real product development environments, not just demos
Support Scientists
The way they actually work, with trust and expert judgment
Turn Wins Into Adoption
Convert early successes into broader organizational momentum
