Operational costs hide in plain sight, such as excess inventory, energy waste, or inefficient workflows. Policy& embeds AI into organizations' operational DNA, turning factories, warehouses, and offices into self-optimizing ecosystems. Imagine a manufacturing plant where IoT sensors predict equipment failures days in advance, slashing downtime, or a logistics network where AI reroutes shipments in real time to avoid port strikes and fuel price spikes. Our tools don’t just cut costs, they reengineer processes for the digital age.

Key Innovations:

Self-Healing Supply Chains: AI algorithms adjust procurement, production, and logistics in real time, balancing cost, speed, and carbon impact.

Predictive Maintenance: Machine learning analyzes sensor data to forecast machinery breakdowns, reducing repair costs and extending asset lifecycles.

Hyperautomation at Scale: Robotic process automation (RPA) handles repetitive tasks, from invoice processing to HR onboarding, freeing teams for strategic work.

Procurement is rife with hidden risks, overreliance on single suppliers, ESG non-compliance, or contractual loopholes. Policy& transforms procurement into a strategic lever with AI that balances cost, risk, and sustainability. Machine learning evaluates suppliers across 100+ risk factors, from financial stability to carbon footprint, while NLP extracts key terms from contracts to automate compliance checks. For instance, an AI model might identify a supplier in a high-risk geopolitical zone and recommend diversifying to a cost-comparable, ESG-certified alternative, saving while future-proofing the supply chain.

Key Innovations:

Supplier Risk AI: Scores suppliers on financial health, ethical practices, and geopolitical exposure, enabling data-driven negotiations.

Carbon-Aware Sourcing: Algorithms prioritize suppliers that align with net-zero goals, reducing Scope 3 emissions while trimming costs.

Dynamic Contract Management: AI flags auto-renewal clauses or price escalation terms, enabling proactive renegotiation.

Labor costs consume over half of corporate budgets, but attrition, skill gaps, and rigid structures erode ROI. Policy& reimagines workforce economics with AI that aligns talent investments with strategic goals. Machine learning maps employee skills to high-impact projects, reducing overstaffing, while predictive analytics forecast attrition risks, enabling targeted retention programs. For example, AI might identify that upskilling of the workforce in AI governance could reduce reliance on external consultants, or that integrating gig workers for peak demand periods could cut labor costs without sacrificing output.

Key Innovations:

Skills-Based Workforce Design: AI audits employee capabilities, matching hidden talents to projects and closing reskilling gaps.

Attrition Prediction Engines: Analyze engagement surveys, performance metrics, and market trends to predict and preempt turnover.

Gig Economy Optimization: Dynamically blend full-time, contract, and AI-augmented roles based on workflow demands.