AI Strategy Delivers 90% Touchless Processing for AP Automation
Client
A global Procure-to-Pay automation platform serving enterprise clients across energy, renewables and other industries, processing millions of invoices for finance teams worldwide.
About
Industry
Financial Services
Company size
100-250
Client Market
Global
Location
Ireland
Services Offered
Challenge
Untapped AI Potential, No Roadmap
Millions of invoices flowed through the platform each year, and the opportunity for intelligent matching, automated coding and natural language insight was clear to the business. What was missing was direction: internal teams had no clear view of where to invest or how to implement AI capabilities in a way that would deliver measurable return.
The team understood its product and its customers, but needed structured, outside expertise to turn scattered AI ideas into a prioritised, fundable roadmap the business could commit to.
Solution
From use cases to a working roadmap
Identifying where AI would earn its investment
Zartis AI experts ran strategic assessment workshops to surface high-impact AI use cases across the AP platform, working from real invoice volumes and existing pain points rather than a generic capability list.
Testing feasibility before committing budget
Each candidate use case was evaluated for technical feasibility against the platform’s existing data and infrastructure, so the roadmap that followed reflected what could genuinely be built, not just what sounded promising.
Building a roadmap the business could fund
The workshops closed with a phased implementation roadmap sequencing investment against business priority, giving the client a route from strategy to production rather than a static report.
The Zartis Approach
What we delivered
Quick wins, then scale
Momentum for a company-wide AI programme is easier to build once the organisation has seen a result, so we sequenced the roadmap fast, and delivered credible wins before larger asks.
Challenge: No prior AI delivery to point to when requesting further investment.
Recommendation: Deliver three initial use cases over the next 3 to 6 months using existing data, then scale adoption using a dedicated resourcing and skills roadmap for the full rollout.
- Prioritised Copilot, matching and coding first
- Delivered value with data already on hand
- Built organisational buy-in before bigger asks
- Planned resourcing and skills for full adoption
- Created a phased roadmap rather than a waterfall approach
Pragmatic technology choices
Custom AI infrastructure takes time to build and maintain, time that is better spent proving use cases than engineering platforms.
Challenge: Risk of over-investing in bespoke infrastructure before value was proven.
Recommendation: Favour cloud-based ML platforms and managed services over custom builds.
- Managed services chosen over custom infrastructure
- Time-to-value prioritised over platform ownership
- Engineering effort redirected to use cases
- Faster path from workshop to production
Cross-functional alignment
A roadmap owned by one department invites resistance from the others it depends on, so ownership was built jointly from the outset.
Challenge: Siloed ownership risked "not invented here" resistance across departments.
Recommendation: Run the assessment as cross-functional workshops rather than a single-team exercise.
- Workshops spanned departments, not one function
- Shared ownership built into the process
- Reduced resistance to the resulting roadmap
Balanced prioritisation framework
Without a structured framework, AI roadmaps tend to chase the most exciting idea rather than the most valuable one.
Challenge: Risk of prioritising novelty over business value.
Recommendation: Score every use case against business impact, technical feasibility and strategic alignment.
- Three-factor scoring applied to every use case
- Business impact weighed alongside feasibility
- Strategic alignment checked before commitment
Honest readiness assessment
A roadmap built on optimistic assumptions about data, infrastructure or skills tends to stall in delivery, so the gaps were surfaced early rather than discovered mid-project.
Challenge: Data quality, infrastructure and skill gaps that would otherwise block delivery.
Recommendation: Document these gaps explicitly in the Current State Assessment and fold remediation into the roadmap.
- Data quality issues identified upfront
- Infrastructure gaps documented in the AS-IS report
- Skill shortages addressed as part of the plan
Business Impact
Strengthened AI credibility in enterprise sales
Reduced support load through self-service insight
New revenue from AI-enabled feature upsells
Faster onboarding for new platform users
Improved AP team productivity
Real AI capabilities in production
Strategy translated into three shipped capabilities within the client’s AP platform.
90% touchless processing
Intelligent Smart Matching now applies 250+ learned patterns and confidence-based matching to PO invoices, routing only genuine exceptions to a human reviewer, up from a roughly 70% baseline before the engagement.
89% faster non-PO processing
Smart Coding & Routing now codes non-PO invoices to the correct GL account and routes them to the right approver automatically, learning from user corrections, cutting processing time for what was previously a manual workflow.
Reduction in time-to-insight
The AI-powered Copilot gives AP managers a natural language interface to invoice data, letting them identify top suppliers, retrieve invoice histories and surface trends without building manual reports or filters.
