Ensuring AI Readiness with Data-Driven Process Intelligence
About this Project
- Client: The world’s leading semiconductor IP designer, powering 95% of smartphones and driving innovation in AI, automotive, and cloud.
- Industry: Semiconductor & Technology
- Focus Area: AI Readiness for Procure-to-Pay (P2P)
The Challenge
Our client, a global leader in semiconductor IP, sought to accelerate its digital transformation by launching ambitious AI and automation initiatives. The company recognized that the success of these projects depended on the health and quality of their underlying data and processes. They needed a clear, objective assessment of their existing P2P workflow before committing to large-scale AI investments.
Leadership faced critical questions:
- Was the current P2P process consistent enough to be a reliable input for AI?
- Where did data quality issues and process bottlenecks exist?
- Which areas were the most promising for automation, and which would just automate existing inefficiencies?
A wrong decision could lead to failed projects, wasted resources, and skepticism around future AI investments. Leadership needed facts and a clear readiness roadmap, not assumptions.
Why FUTUROOT?
The client needed data-driven clarity to ensure AI and automation success. FUTUROOT delivered by:
- Replacing Assumptions with Facts — Revealed inefficiencies and data quality issues.
- Measuring Readiness — Provided an objective Automation Readiness Score.
- Prioritizing Opportunities — Highlighted tasks with the highest automation ROI.
- Enabling Evidence-Based Decisions — Gave leadership confidence to invest in AI initiatives.
This approach turned AI readiness into a fact-based, actionable strategy, ensuring higher success rates for upcoming automation projects.
Our Approach: Anchored in Data, Not Assumptions
FUTUROOT’s approach combined process mining, KPI analysis, and automation readiness scoring to provide a clear, data-driven roadmap for AI and automation success.
Bottleneck Analysis
FUTUROOT mapped the full end-to-end process, revealing bottlenecks like manual approvals and duplicated data entry, quantifying a 40% reduction in cross-region execution differences, and providing a clear list of fix-first priorities before AI implementation.
KPI Management
FUTUROOT was used to define and monitor key performance indicators essential for successful automation, including invoice processing cycle times, touchless invoice rates, and error rates. This included improvements in vendor master accuracy (82% → 96%), conformance to standard process paths (68% → 88%), and reduction of AP exception rates (22% → 10%). This established a baseline allowing leadership to measure improvement with evidence, rather than opinion.
FR Anaytics
FUTUROOT analyzed the client’s P2P process and data to generate a comprehensive Automation Readiness Score. This quantitative metric provided a clear, objective benchmark that leadership could use to assess the viability of their AI plans and communicate progress.
Business Case Management
Findings were integrated into a comprehensive roadmap. FUTUROOT helped the client identify and prioritize the most impactful automation opportunities, quantifying the potential time and cost savings. This included identifying that 35% of AP tasks were automation-ready, representing up to 9,000 hours in annual savings. The result was a sequenced, fact-based AI strategy, not a leap of faith.
Business Outcomes Delivered
FUTUROOT transformed what could have been a fragmented, assumption-driven process into a clear, evidence-backed set of actionable outcomes:
Improvement in
Master Data Quality
Vendor records reached 96% accuracy, improving overall data reliability.
Increase in
Process Conformance
Workflow standardisation strengthened, ensuring more consistent operations.
Reduction in
AP Exceptions
Exception rates dropped from 22% to 10%, reducing manual interventions and errors.
Increase in
Automation Potential
AP activities flagged for automation unlocked ~9,000 hours of potential annual savings.
Increase in
AI Readiness Score
The improvement from 62 to 85, boosted leadership confidence in upcoming automation initiatives.
““FUTUROOT gave us the objective data we needed to kickstart our AI journey. We now have a clear roadmap, built on facts, that helps us invest in automation with confidence.”
Client Leadership Feedback