I am deeply passionate and excited about the potential for AI across all areas of daily business operations. Whether you are at a startup, scale up, enterprise org, financial institution or a SaaS provider, someone who is always thinking about how to incorporate AI into the overall business strategy.

That said, it’s very important to recognize that not every use case requires AI. Just like any other tool, AI is JUST that — a tool in the toolbox, not a silver bullet. If the processes were stable, business rules are well defined and variability is low, “traditional” automation, business rule engines maybe more appropriate and cost-saving solutions. Platforms that blend automation, analytics and AI aim to blur these boundaries by allowing organizations to adopt artificial intelligence incrementally rather than all at once.

One of the main barriers to AI adoption is the level of investment already made in existing operational models, workflows, and systems. Dashboards, reports, and KPIs built on structured data remain central to how most organizations run their businesses. Even when AI could deliver superior insights or efficiencies, leaders must evaluate whether the gap in skills, tooling, governance, and organizational readiness justifies the investment. Investment can both be quantified in fudiciary or human capital terms.

An example

Sendlify* is a logistics company managing last mile deliveries across multiple cities. Think of route planning, driver assignment, and delivery tracking. These operations should ideally be handled using deterministic rules and simple optimizations based on distance and “time-to-delivery-location” metrics.

But as the business scales, variability across all the metrics increases — traffic patterns, time-to-delivery-location and demand become less predictable along with a shift in customer expectations. At this stage, investing in AI integrations could deliver outcomes by forecasting delivery volumes, dynamically optimizing routes and detecting operational anomalies or predicting delays before they happen.

It’s important to note that the main role of AI integration is to augment the operations, and not, replace the existing operational tools such as the dispatch dashboards, notifications and alerts to, e.g the drivers and the customers.

For many startups or enterprises, the benefits of AI justify adding additional layers to the operational pipeline — such as feature engineering, model inference, or decision intelligence — while still translating outputs back into familiar formats like reports, alerts, or workflow triggers. Whether this approach is viable depends on the business context, team maturity, risk tolerance, and regulatory environment.

That said, I strongly believe new technologies are worth exploring because:

• You will learn something (as a company).
• You may uncover a high-impact operational or strategic advantage over your competition.

In this era of snake oil merchants disguised as consultants and prompt engineers, it is critical not to lose sight of the underlying business problems.

Always ask whether AI is genuinely the right solution — or whether it is being applied simply because it is fashionable, rather than because it meaningfully improves the business outcomes.

• Not in anyway related to any company with the same brand name.