Designing AI for Strategic and Operational Impact
Once you’ve defined the scope of your AI initiative—identifying the user, problem, and core value proposition—it’s time to dive into the second stage of the AI design process. This is where strategic vision meets operational clarity, shaping how artificial intelligence will contribute not just to your product, but to your long-term business advantage.
In this stage, we tackle two major questions:
1. Strategic AI Choices: What Role Will AI Play?
At this point, it’s critical to align your AI investment with your broader business strategy. The question isn’t just “What can AI do?” but rather “How should AI support our unique value proposition and competitive advantage?”
There are typically two paths to consider:
Best Product Player: This approach positions your AI to be a market leader in a core capability. Imagine you're building an AI-based medical imaging tool that detects early-stage cancer. Your competitive edge lies in having the most accurate detection algorithm on the market—trained on a massive, diverse dataset and validated across global clinical trials. Healthcare providers might choose your product over others because it delivers the best raw diagnostic performance, even if it requires additional integration effort.
Full Customer Solution Player: End-to-End Identity Intelligence: This is about creating a full solution strategy that focuses on solving the entire customer problem, even if the AI itself isn’t top-tier. For example, your company might offer an AI-based identity verification platform that includes fingerprinting (not the most advanced), facial recognition, document verification, compliance workflows, and seamless integration with existing enterprise systems. Your AI may not outperform competitors in any single category—but the platform’s all-in-one functionality, intuitive interface, and cloud-ready deployment make it incredibly attractive to mid-size businesses seeking speed, ease, and lower total cost of ownership.
Choosing between these strategic paths is essential. Are you aiming to win on technical performance, or on delivering a complete, valuable experience?
2. Operational Design: Where Will AI Intervene?
With your strategy defined, the next step is to get specific about the business processes that AI will impact.
Ask yourself:
What exact process will AI improve?
What performance targets are realistic and meaningful?
What non-AI components must also evolve to make this work?
This is the “AI without the AI” moment. We must first describe the process we hope to optimize—clearly, and with measurable outcomes—so our engineering teams know what success looks like.
Here are a few examples:
Threat Detection (SOC Optimization): AI models trained on global threat intelligence and real-time endpoint telemetry could be deployed to detect zero-day threats and anomalies with high precision. The operational target might be to reduce mean time to detect (MTTD) threats by 70%—while also minimizing false positives to prevent analyst burnout.
Phishing Prevention (Email Security): AI can analyze email language patterns, sender reputation, and behavioral context to automatically flag or quarantine phishing attempts. The business goal could be to cut successful phishing incidents by 90% without increasing the burden on end users or IT help desks.
Access Anomaly Detection (IAM Integration): By continuously learning user behavior, AI can flag unusual login times, location access, or privilege escalations. A clear process metric might be to reduce unauthorized access events by 60% and automate incident escalation workflows, improving SOC efficiency without adding headcount.
Importantly, the AI solution must be integrated into a broader system of human processes, UX flows, and infrastructure. AI isn’t a magic wand; it’s a tool. And like any tool, it needs the right environment to make an impact.
Stage Two of the AI design process is about thoughtful alignment. Strategically, decide whether AI is your product’s hero feature or part of a broader solution. Operationally, identify where and how AI will drive improvement—backed by clear metrics and support systems.
Next up? Stage Three, where we’ll dive into translating these strategic and operational plans into a technical architecture and data pipeline. Stay tuned!
