Practical Implementation: Making AI Real in Finance

Randy Werder,
June 5, 2025

Approximate read time: 5-6 minutes

All articles in this series:

Why do nearly 70% of AI initiatives in finance fail to deliver expected value? After guiding dozens of organizations through successful finance transformations, I can tell you it’s rarely about technology limitations. It’s almost always about organizational and methodological shortcomings.

If you’re serious about implementing AI in your finance organization, you need a proven roadmap – not theoretical frameworks that fall apart in the real world.

The Six-Phase Implementation Roadmap That Actually Works

Over the years, I’ve developed and refined a six-phase approach that consistently delivers results. This isn’t academic theory, it’s battle-tested in real finance organizations facing real challenges.

Phase 1: Capability Assessment (4-6 Weeks)

Before selecting technology or defining use cases, you must conduct an honest assessment of your current capabilities across three dimensions:

Data Readiness: I recently worked with a global manufacturer who discovered they had three different calculation methodologies for forecast accuracy across business units – all supposedly reporting the same metric. Without harmonizing these definitions first, any AI forecasting implementation would have been built on quicksand.

Process Maturity: A healthcare provider client realized their budgeting process had 27 unnecessary approval steps. We streamlined these manual processes before applying any AI, yielding a 40% cycle time reduction with zero technology changes.

Skill Availability: Don’t let resource gaps become roadblocks. A consumer goods company I advised created a cross-functional skills matrix that became their targeted upskilling roadmap, focusing resources on the most critical needs.

Remember: Resource gaps shouldn’t halt your AI momentum. Build capability development directly into your implementation plan.

Phase 2: Value Targeting (3-4 Weeks)

With a solid understanding of your current state, identify specific use cases that will deliver meaningful business value. The most successful implementations I’ve led start with a few high-impact use cases rather than broad transformation.

Smart implementation isn’t about isolated victories – it’s about creating a strategic cascade where each success builds capabilities for the next. A financial services firm I worked with engineered a progressive improvement path:

Anomaly detection in financial reporting (60% reduction in manual reviews)

Driver-based revenue forecasting (35% accuracy improvement)

Automated variance analysis (70% time reduction)

Each use case built on the previous one, creating a logical progression of capabilities and value.

Phase 3: Architecture Design (6-8 Weeks)

With clear value targets identified, design the technical and process architecture that will support your AI initiatives. Remember the platform approach I discussed previously – focus on creating an integrated architecture, not disconnected point solutions.

A manufacturing client developed a target architecture that interposed a data and analytics layer between their ERP system and planning tools. This allowed them to implement AI capabilities without disrupting their core systems while ensuring consistent data throughout.

Phase 4: Pilot Implementation (6-12 Weeks)

Rather than a big bang approach, implement your highest priority use case in a controlled environment. The goal is to prove the concept, refine the approach, and build organizational confidence.

A retail client of mine piloted their AI-driven merchandise planning capabilities in a single category across 50 stores. This controlled scope allowed them to measure results against a control group, demonstrating a 22% inventory reduction and 18% sales increase within 10 weeks.

Phase 5: Measured Expansion (Ongoing)

With a successful pilot completed, expand to additional areas in a measured, controlled manner. Don’t rush to enterprise-wide implementation before validating scalability.

A consumer products company I guided expanded their AI forecasting capabilities sequentially: first to their top 3 markets (representing 60% of revenue), then to their top 5 product categories, and finally enterprise-wide. This measured approach allowed them to refine the implementation at each stage.

Phase 6: Continuous Evolution (Ongoing)

AI implementation is never “done.” The most successful organizations establish a continuous evolution model that constantly refines and expands capabilities based on business needs and emerging technologies.

Avoiding the Pitfalls That Derail Most Implementations

Even with a solid roadmap, certain pitfalls consistently trip up finance AI implementations. Here’s how my clients have avoided the most common ones:

The Perfect Data Trap: A manufacturing client built their implementation plan around the data they actually had, not the data they wished they had. They focused initial use cases on areas with sufficient data quality, while using the value created to fund data improvement initiatives for future capabilities.

The Customization Quagmire: A financial services firm I advised adopted a “standard unless proven otherwise” approach, requiring business justification for any customization request. This reduced their implementation time by 40% compared to previous projects.

The Security Afterthought: I watched a healthcare provider initially deploy an AI forecasting capability without adequate security provisions for patient financial data. The resulting remediation delayed full implementation by six months and cost three times what proactive security design would have required.

The Big Bang Delusion: A retailer initially planned a comprehensive finance transformation. After our recalibration discussion, they instead implemented capabilities in three phases over 18 months. The measured approach delivered value sooner and ultimately achieved greater adoption.

The Technology-First Mindset: A pharmaceutical company I worked with flipped their traditional approach, defining specific business outcomes and process requirements before engaging vendors. This clarity allowed them to evaluate solutions based on business impact rather than feature lists.

Measuring Real Success

Implementation isn’t successful when the technology works – it’s successful when it delivers measurable business value. Establish clear success metrics across three dimensions:

Efficiency Gains: Reduced cycle times, increased automation rates, decreased manual effort

Quality Improvements: Increased accuracy, reduced errors, improved compliance

Business Impacts: Revenue growth, cost reduction, working capital improvements, improved decision quality

The Journey Never Ends

Successfully implementing AI in finance isn’t about following a linear project plan. It’s about orchestrating a business transformation journey that delivers increasing value over time.

The organizations that consistently succeed approach implementation with balanced attention to technology, process, people, and security. They start with clear business outcomes, build on their actual capabilities (not aspirational ones), and expand methodically as they demonstrate value.

Remember that although AI technology will be implemented, the ultimate goal is transforming how finance creates value for your organization. Keep that focus throughout your journey, measure success through business outcomes rather than technical milestones, and recognize that implementation is never truly “done” but rather continues to evolve as both technology and your organization mature.