Five Traits of Future-Ready AI

The race is on to start building with AI, but that pounding urgency often causes teams to focus on building fast, not smart. Remember, the goal isn’t simply to cross the finish line and deploy your groundbreaking models in production. In many ways, that’s the easy part. The challenge is to build something that provides durable value with minimal overhead.

“No business plan survives first contact with customers" - Steve Blank

You may have heard Steve Blank’s quote, “No business plan survives first contact with customers,” and the same is true for AI. Whether you’re building a custom solution or looking to leverage a third-party offering, consider the five traits listed below to avoid the trap of launching brittle AI solutions.

1. Measurable Returns: Quantifiable Impact

AI solutions, no matter how technologically advanced, must deliver demonstrable value. This means that any AI initiative should be accompanied by metrics that showcase tangible results. With budgets under constant scrutiny, the ability to correlate every dollar spent with a measurable outcome becomes indispensable.

Moreover, understanding the cost of incremental performance is crucial. For instance, if achieving 95% accuracy costs exponentially more than 90%, is that additional 5% truly essential? This nuanced understanding allows you to optimize your future investments.

2. Robust Pipelines: Beyond Algorithms

While algorithms often steal the limelight, a long-term view forces you to consider all technical components in your AI solution. That includes the often neglected data sources required to feed your models long-term. The most common challenge companies face when deploying AI is having access to high-quality data. Think of it as both the foundation for your

For those aiming to transition an AI solution from the drawing board to production, automated data pipelines are non-negotiable. These pipelines must be flexible, seamlessly integrating with diverse systems and platforms. Additionally, as AI becomes more entrenched in business operations, integrated data governance and model version control are essential to ensure auditability and compliance.

3. Managed Resiliency: Eyes on Flexibility

The adage "what got you here, won’t get you there" holds true in the AI realm. Data is dynamic, and AI solutions must be resilient enough to adapt. Resiliency as a feature ensures that models remain relevant and effective as real-world conditions shift.

As part of your KPI dashboard (including your value tracker from #1 above), be sure to have a method to track model performance and "drift" – the phenomenon where a model's predictions gradually become less accurate. Having a dashboard isn’t enough though. Define a plan for how you’ll be alerted to unexpected trends (e.g. automated reporting) and if/when retraining is required. To make that a meaningful exercise, ensure you’re subject matter experts are able to understand (and approve) the model's decision-making approach.

4. High Performance: Scalability Meets Speed

In the age of instant gratification, speed and efficiency are paramount. This is especially true for sectors like fraud detection, where real-time responses can make or break outcomes. AI solutions must be architected to deliver rapid results without compromising on accuracy.

Moreover, as businesses grow and evolve, AI solutions must scale dynamically — as data volumes and user demands surge, the AI system should stay responsive. This is a key aspect for both custom builds and third-party solutions. Ask vendors (or your engineering team) for evidence that supports how increased workloads won’t lead to diminished performance.

5. User-Centric Design: The bridge between AI and Users

No matter how much we can automate and generate with machine learning, at its core, AI is a tool for people. So, its design should prioritize the end-user. This means intuitive interfaces, clear outputs, and a user experience that feels seamless. Moreover, feedback mechanisms should be embedded, allowing for continuous learning based on user insights.

Future-Ready AI The urge to quickly deploy AI solutions is understandable. There is so much attention, across industries, paid to the advancements and power of consumer tools like ChatGPT and Midjourney. And for good reason! They not only found product-market fit, but they did it while building solutions that have the five traits above. That doesn’t mean you shouldn’t build anything if you can’t perfect these elements. Simply keep in mind what ‘great’ will look like when your solution is successful too. Balancing your investment between the model performance and these traits will ensure you’re able to drive a meaningful impact while avoiding technical debt that’ll eat away at your margins long term.