FEATURE: ARTIFICIAL INTELLIGENCE
a 30 % success into a win. But smaller companies need a success rate closer to 80-90 % to justify their investments – they don’ t have the budget to waste. Without a robust discovery process, hitting all of these goals is nearly impossible.
Rushing to implementation
The pressure from boards and executives to demonstrate AI capabilities often compresses the discovery timeline. This rushed approach leads to force-fitting AI into situations where simpler, less expensive alternatives might actually work better. As I often remind my team, sometimes hiring‘ a couple of kids out of college’ to solve a problem manually costs far less than a sophisticated AI implementation – and might deliver results faster with less risk.
A framework for AI evaluation
To bridge the discovery gap, technology leaders need a structured framework for evaluating potential AI applications:
1. Start with the problem Document specific industry challenges and customer pain points before considering technological solutions. Consider what problems keep your customers up at night and which inefficiencies cost your organization the most. 2. Identify blind spots Look beyond obvious challenges to discover problems your customers don’ t yet recognize. As Steve Jobs famously noted,“ a lot of times, people don’ t know what they want until you show it to them.” So think about what customers will want next, not what they’ re asking for today. 3. Assess disruption risk Consider how competitors might use emerging technologies to gain advantages in your market. Identify which processes in your industry are most vulnerable to AI-driven disruption, and how automation could streamline a process significantly. Often, this may be something‘ boring’ like invoice processing or managing international trade logistics, but automating these mundane processes can actually deliver significant savings. 4. Evaluate data assets AI effectiveness depends heavily on data quality and quantity. Assess whether you have sufficient data for your proposed application and whether that data is clean enough to yield reliable insights. Often, we find organizations have abundant data but haven’ t structured it in ways that make it usable for AI applications. 5. Match solutions to problems Only after completing the previous steps should you assess whether AI is the right solution. Sometimes traditional software or even process changes deliver better results without the complexity and expense of AI. 6. Calculate ROI realistically Include all costs – development, deployment, maintenance and organizational change management – when calculating potential returns from AI initiatives. This framework helps ensure that AI implementations solve real problems with appropriate technology and generate tangible business value rather than just creating technological showcases.
Real-World Applications Worth Exploring
At Verra Mobility, we’ ve identified several highpotential AI applications by focusing on specific industry problems:
• Predictive intersection management: Using our extensive database of traffic violations and environmental factors, we’ re exploring how AI can predict when violations are likely to occur. Instead of simply issuing citations after incidents happen,
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