Feasibility Factors in AI Prioritization

Feasibility factors tell you how realistic and straightforward it will be to transition a workstream into an AI-driven process. Understanding feasibility ensures you prioritize initiatives your firm can genuinely implement without unnecessary complexity or delays.

1. Systems Involved

How many systems does this workstream touch, and how complex are the integrations?

Fewer, simpler systems mean higher feasibility. Complex integrations complicate everything.

Examples:

High Feasibility: Automating simple expense reporting using one or two standard applications

Low Feasibility: Integrating multiple legacy systems for complex audit processes

2. Technical Complexity

How much human judgment or interpretation does this work require?

Processes needing minimal judgment are easier to automate. Work that requires nuance, context, or decision-making is harder.

Examples:

High Feasibility: Automating straightforward categorization of routine financial transactions

Low Feasibility: Complex advisory tasks requiring nuanced human decision-making

3. Data Complexity

How accessible, structured, and clean is the data you need?

Clean, structured data simplifies automation significantly. Messy, unstructured data makes it much harder.

Examples:

High Feasibility: Standardized data formats for invoice processing

Low Feasibility: Analyzing handwritten notes or complex, unstructured client emails

4. Time to Implement

How long will it realistically take to complete this automation?

Shorter implementation periods mean higher feasibility.

Examples:

High Feasibility: Automating straightforward internal workflows within weeks

Low Feasibility: Comprehensive firm-wide system upgrades taking months or longer

5. Resources Required

How much time, money, and human resources does this demand?

Less resource-intensive workstreams naturally rank higher in feasibility.

Examples:

High Feasibility: Small-scale improvements manageable by an individual or small team

Low Feasibility: Major initiatives needing significant cross-firm investment and effort

Feasibility is Relative

Just like impact, feasibility depends on your firm's specific context. What seems straightforward in isolation may become complicated within larger processes or systems.

For example, scheduling meetings individually appears easy. But integrate it across numerous departmental calendars, and implementation gets significantly more complex.

Enhancing Feasibility with Granularity

If a high-impact workstream has low feasibility, consider increasing granularity by breaking it down into smaller, more manageable sub-streams. This can significantly improve feasibility and make it easier to identify segments ready for AI.

Practical Tip: Increase granularity by splitting complex processes like month-end close into smaller tasks such as expense management, payroll, and inventory management.