The Challenge: When Building Models Takes Longer Than Market Windows
Commercial strategy teams face relentless pressure to model costs for new products, regional expansions, and competitive scenarios. A single cost model, essential for pricing decisions, bid responses, and market entry strategies, requires synthesizing hundreds of data points across raw materials, labor markets, capital requirements, and operational overheads.
For one specialty chemicals company, the reality was sobering: 29 days on average to build a single cost model from scratch.
The breakdown revealed where time disappeared:
- 8 days on desk research - Manually scanning documents, websites, and databases to extract material costs, labor rates, and market data across geographies.
- 9 days on calculations and analysis - Building cost breakdowns, applying formulas, running scenarios.
- 2 days on data modeling and dashboarding - Structuring models and creating visualization frameworks.
- 2 days on user story creation and testing - Documenting assumptions and validating outputs.
- 3 days on primary research - Expert consultations to validate estimates.
- Additional time for feasibility assessment, feedback incorporation, and project coordination.
The problem wasn't just speed—it was opportunity cost. While analysts spent weeks gathering commodity prices and calculating labor costs, strategic questions went unanswered:
Should we enter this market?
Can we compete at this price point?
What happens if raw material costs shift 15%?
By the time models were complete, market conditions had often shifted, requiring updates before the analysis could inform decisions.
One commercial director described the frustration: "We're spending expert-level resources doing tasks a well-prompted AI could handle in hours. Meanwhile, the actual strategic thinking—the 'so what' for our business—gets rushed at the end."
The Solution: AI Agents Handling the Tedious, Humans Handling the Strategic
Evalueserve developed an AI-agent approach to automate the most time-intensive, repeatable components of cost model development while keeping human expertise where it mattered most.
The solution deployed targeted AI capabilities across four critical phases:
Desk Research Automation (80-90% efficiency gain)
AI agents instantly scanned, summarized, and extracted insights from large volumes of documents, websites, and databases – pulling material costs, labor rates, and market data that previously required days of manual work.
Calculations and Analysis (60-70% efficiency gain)
With targeted prompts, AI quickly performed calculations, built cost breakdowns, and analyzed market data – handling formula application and scenario modeling that consumed analyst time.
Data Modeling & Dashboarding (80-90% efficiency gain)
AI generated model templates, applied logic structures, and simulated scenarios rapidly – creating frameworks that analysts previously built manually over days.
User Story Creation & Testing (80-90% efficiency gain)
AI auto-generated summaries, visualizations, and reports based on model outputs – documenting assumptions and creating clear narratives from complex analyses.
The approach was built on a modular tool architecture. Teams created individual cost component tools (raw materials, labor, capital, overheads) with defined inputs, AI processing steps, and structured outputs. These tools could be used individually or integrated into a comprehensive automated cost model.
Critically, primary research and strategic recommendations remained human-led. Evalueserve’s domain experts focused on validating insights, consulting with industry specialists, and translating model outputs into business strategy.
The Impact: 29 Days Compressed to Under 9, Strategic Focus Reclaimed
The AI-enabled approach delivered 60-70% overall efficiency gains in cost model development, reducing the typical 29-day cycle to approximately 8-9 days.
Specific time savings by activity:
- Desk research: 8 days → 1-2 days (80-85% reduction)
- Calculations & analysis: 9 days → 3 days (67% reduction)
- Data modeling: 2 days → <0.5 days (75-80% reduction)
- User stories & testing: 2 days → <0.5 days (75-80% reduction)
But the transformation extended beyond speed:
One analyst captured the shift: "We used to be the team that took weeks to tell leadership what something might cost. Now we're the team that pressure-tests strategy with live scenarios in real-time conversations."
The modular architecture meant teams could continuously refine individual components, improving raw material cost tools, labor analysis, or capital projections, with each improvement compounding across all future models.
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Strategic Capacity Unlocked
Analysts shifted from data gatherers to strategic advisors. Now spending their time on insight generation, scenario planning, and business recommendations rather than commodity price lookups and spreadsheet formatting.
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Increased Model Volume
The same team could now produce 3-4x more cost models in the same timeframe, enabling rapid evaluation of multiple market scenarios, competitive responses, and pricing strategies simultaneously.
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Real-Time Market Responsiveness
When raw material costs shifted or new competitors entered a market, teams could update models in days rather than weeks. This helped them keep pace with market volatility instead of lagging behind it.
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Accuracy and Consistency
Automated data extraction reduced manual errors from copy-paste mistakes and unit conversion problems, while standardized templates ensured consistent methodology across models.
"We used to be the team that took weeks to tell leadership what something might cost. Now we're the team that pressure-tests strategy with live scenarios in real-time conversations."
The modular architecture meant teams could continuously refine individual components, improving raw material cost tools, labor analysis, or capital projections, with each improvement compounding across all future models.
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Get in touch today to find out about how Evalueserve can help you improve your processes, making you better, faster and more efficient.
Overview & Impact
AI-powered cost modeling transformed commercial strategy for a specialty chemicals company, cutting development time from 29 to under 9 days while elevating teams from data gathering to strategic impact.
