The Power of Pragmatic AI: Focusing on High-Impact Applications
The buzz around artificial intelligence (AI) is undeniable, but translating that hype into tangible business value requires a shift in strategy. Many organizations are captivated by the promise of fully autonomous AI agents, envisioning sweeping transformations. However,a more effective approach – particularly as agentic capabilities continue to evolve – lies in identifying and automating specific,high-value processes. This isn’t about abandoning ambitious goals; it’s about prioritizing projects that deliver demonstrable results now, building momentum and paving the way for more complex implementations later. Consider the mortgage industry: a recent study by J.D. Power (November 2023) revealed a 35% abandonment rate for online mortgage applications, largely due to complexity and user frustration. This presents a prime chance for targeted AI solutions.
Understanding the Limitations of “Moonshot” AI Projects
The allure of a single, all-encompassing AI agent is strong. The idea of a system that can handle all customer interactions, automate entire departments, or revolutionize core business functions is tempting. However, these “moonshot” projects often suffer from several critical flaws:
* Vague Scope: Overly broad objectives lead to feature creep and difficulty in defining success metrics.
* High Cost: Developing and deploying complex AI systems requires significant investment in infrastructure, talent, and ongoing maintenance.
* Slow Time to Value: Large-scale projects typically take years to complete, delaying the realization of benefits.
* Resource Drain: These initiatives can consume valuable resources that could be better allocated to more achievable goals.
Instead of aiming for the unfeasible, a more strategic approach focuses on incremental improvements. This involves identifying specific pain points and deploying AI solutions to address them directly. This is where the concept of applied AI truly shines.
The “Singles and Doubles” Strategy: Identifying Yoru AI Sweet Spot
So, how do you identify those “singles and doubles”? Here’s a step-by-step process I use with enterprise leaders:
- Process Inventory: List 10 processes currently performed manually that are repetitive and well-documented. Think about tasks that employees find tedious or time-consuming. Examples include invoice processing, customer onboarding, or data entry.
- Impact Assessment: for each process, score its potential impact on a scale of 1-10 if automated (1 = minimal impact, 10 = significant impact). Consider factors like cost savings, increased efficiency, and improved customer satisfaction.
- Risk Evaluation: assess the risk associated with automating each process, also on a scale of 1-10 (1 = low risk, 10 = catastrophic risk). What are the potential consequences if the project fails?
- Complexity Analysis: Evaluate the complexity of building and deploying an AI solution for each process,again using a 1-10 scale (1 = simple,10 = extremely complex). Consider factors like data availability, integration requirements, and the need for specialized expertise.
- Prioritization: Focus on projects that score high on impact, low on risk, and low on complexity.This is your “AI sweet spot.”
here’s a quick comparison table to illustrate the concept:
| Process | Impact (1-10) | Risk (1-10) | Complexity (1-10) | Priority |
|---|---|---|---|---|
| Invoice Processing | 8 | 2 |
