Why you need P.I before you invest in A.I
The importance of process foundations for A.I success
Before implementing AI solutions, organisations must thoroughly understand their existing processes and evaluate whether these processes genuinely create customer value. This article explores why Process Insight is crucial for AI success and how organisations can avoid automating ineffective processes.
Introduction
The allure of artificial intelligence often leads organisations to rush toward implementation without first understanding their existing processes. Yet automating a flawed process merely makes inefficiency faster and more consistent. As organisations invest in AI capabilities, they must first address a fundamental question: do our current processes actually deliver value to customers?
The Process Understanding Gap
Many organisations have limited visibility into how their processes actually function end-to-end. While individual teams may understand their part of a process, the connections between departments often remain unclear. This creates particular challenges for AI implementation, as automated systems need clear rules and boundaries to operate effectively.
Consider a mortgage application process. The sales team may have one view of the process, the underwriting team another, and the customer service team yet another. When organisations implement AI without reconciling these different perspectives, they risk creating new inefficiencies rather than solving existing ones.
The Value Delivery Question
Understanding processes is only the first step. The more critical question is whether these processes actually create value for customers. Many organisational processes have evolved over time through accumulation rather than design, leading to unnecessary complexity that serves the organisation ' s internal needs rather than customer outcomes.
For example, a telecommunications company might have elaborate processes for handling customer moving requests, involving multiple handoffs between departments. But from the customer ' s perspective, the desired outcome is simple: "I want my service to work at my new address. " Any process steps that don 't directly contribute to this outcome represent potential waste.
Identifying Demand created by Service Waste
Before implementing AI, organisations must distinguish between processes that handle Value Demand (what customers actually want) and those that manage Service Waste (dealing with problems caused by poor service delivery). This analysis often reveals surprising insights:
Process Archaeology
Many processes exist solely to handle problems created by other processes. These " compensation mechanisms " often become so embedded in organisational operations that they ' re seen as necessary rather than symptomatic of underlying problems.
Hidden Costs
The true cost of poor processes extends beyond obvious metrics. When processes fail to deliver value, they generate additional work through customer callbacks, complaints handling, and rework - all of which appear as separate processes but stem from the same root cause.
Customer Impact
Ineffective processes don 't just create internal costs; they damage customer relationships and trust. Each process failure generates not just immediate dissatisfaction but also reduces customers ' willingness to use self-service channels in the future.
Avoiding The AI Implementation Trap
Without proper process understanding and evaluation, organisations risk falling into several common traps:
Automating Complexity
Instead of simplifying complex processes, organisations risk automating them, making them harder to change in the future and further embedding inefficiency into their digital operations.
Fragmenting Customer Journeys
AI implementations often focus on individual or clusters of local process steps rather than end-to- end customer journeys, creating new silos and handoff points.
Missing Improvement Opportunities
By focusing on automation rather than process effectiveness, organisations miss opportunities to eliminate unnecessary work and significant associated operating costs entirely.
Building AI on Solid Process Foundations
Alongside appropriate governance, a risk-based approach to data security, reliability and provenance to avoid these traps, organisations should follow a structured approach:
Process Discovery
Begin with a thorough mapping of current processes, focusing not just on documented procedures but on how work actually flows through the organisation. This includes identifying informal workarounds and understanding why they exist.
Value Analysis
Evaluate each process step against a simple criterion: does it directly contribute to delivering what customers actually want? This analysis often reveals substantial opportunities for simplification before any automation occurs.
Root Cause Understanding
For processes handling Service Waste, investigate the root causes. Often, the most valuable AI implementation opportunity lies in preventing these issues rather than handling them more efficiently or automating them.
Customer Journey Integration
Ensure process understanding extends to complete customer journeys rather than stopping at departmental boundaries. This broader perspective often reveals opportunities for more fundamental process redesign, improved Customer Experience and significant cost savings.
Moving Forward with A.I. informed by Process Intelligence (P.I.)
Once your organisation has this foundation of process understanding, you can implement AI more effectively by:
Prioritising Prevention
Using AI first to prevent process failures rather than just handling their consequences more efficiently.
Supporting Natural Workflows
Designing AI systems that support how work naturally flows through the organisation - designed around customer needs and flow - instead of legacy ‘inside-out’ approaches or enforcing rigid process adherence through now automated (and apparently ‘cheaper’) but flawed processes.
Enabling Adaptation
Creating AI implementations that can evolve as processes improve and inevitably evolve rather than hard coding current practices.
Conclusion
The path to successful AI implementation begins with understanding and optimising existing processes. If you invest in this foundation work you will find your AI initiatives deliver substantially better results than just trying to automate current practices.
The key is to remember that AI should serve process effectiveness, not just efficiency. By understanding your processes and ensuring they create genuine customer value, you can use AI to transform service delivery rather than simply making currently practices faster. As well as improving the overall customer and employee experience this approach will unlock cost savings far greater than can ever be achieved by efficiency-based AI projects.
