Smart software powered by AI has been the promised land for software developers and ISVs for many years. We’ve all heard the vision: encode enough smart logic into your apps and watch them do your job for you. Workloads can be managed faster and more efficiently than humans ever could, leading to better outcomes for your customers.
But as we move from using AI agents for rote task automation into an era of true autonomous reasoning, one question is becoming increasingly hard to ignore: “Can your customer actually explain what your AI is doing and why?”
If the answer is no, your customers are going to have a problem.
The “Black Box” – Liability for Sales and Customer Retention
AI agents that act without direct human supervision are nothing new. What is new is the complexity of the decisions being automated. Rule-based workflows were easy. Things broke, and you knew where because everything was baked into a few well-defined scripts. Automated decisions with AI agents are different. They integrate signals from across systems, reason about context, and then act.
This vastly increases the “surface area” of your application. It also increases the danger when those agents make a bad decision. Let’s focus on those business implications for developers first.
Enterprise customers aren’t just adopting AI agents into their workflows. They’re demanding to know how the AI reached its conclusions. Security and compliance teams are mandating auditability questions long before deals close. And most important, end users – the operators who interface with these apps daily – simply won’t use tools they don’t understand.
If your customer can’t explain to their stakeholders how an AI made a decision, they may stop using it altogether. At that point, opaque AI isn’t just a technical risk, it’s a customer success issue.
Understanding What’s at Stake When AI Decisions Can’t Be Explained
Picture the workflows your customers are automating with AI. Infrastructure management, capacity planning, event response, cost optimization. All these activities touch financial, productivity, and reliability goals. Miss a service level agreement (SLA) or fail to stay within budget because an AI made an inexplicable decision, and it won’t just hurt your customer – it’ll hurt your bottom line.
What if an AI algorithm suppressing false positives decided to hide anomaly alerts altogether? What if your customer can’t tell the difference between an intentionally silent AI and a bug? Or a capacity planning tool that automatically tears down instances to “save money” while traffic is peaking?
Regulators Are Starting to Take Notice
It’s not just unhappy customers you need to worry about. Regulators are taking notice as well, and they’re starting to crack down on opaque systems. That should come as no surprise. But failing to have a plan for auditability and explainability is going to put ISVs looking to sell into compliance-sensitive industries like healthcare and financial services at a competitive disadvantage. If you’re not already, get ahead of this requirement.
Opportunities For Differentiation with Explainable AI (xAI)
So where does that leave software developers who want AI agents in their apps? Simply put, explainability will become a key point of differentiation. Effective xAI provides evidence that backs up a recommendation, validates system knowledge around constraints and dependencies, and then clearly communicates its reasoning using the same terminology your operators already use to think about their work. Mapping automated decisions back to prior incidents, highlighting similar outcomes, and explicitly stating where data came from will allow operators to validate what an agent did (and why) far more quickly. As a result, they’ll trust it with more authority.
Which means happier customers who see value faster. Lower support costs. Less churn. Customers love your AI? They’ll renew. Customers don’t understand how it works? They won’t.
Explainable AI also creates a data-driven record of how decisions were made. The inputs, logic applied, actions recommended, and actual outcomes. While beneficial from a compliance perspective, this level of visibility also creates a feedback loop that continuously improves your system (with proof points to boot).
Build Transparency into Everything You Do
Developers who embrace explainability as a core product feature will leave the competition in the dust. Those who treat it as a documentation problem to be solved later will fall behind. Transparent AI agents that can explain their reasoning in familiar terms allow operators to trust them with greater autonomy. Agents that can’t will be babysat or sidelined indefinitely.
Opaque models may work for internal projects with plenty of hands-on supervision. But if you want to sell into the kinds of markets that matter most to ISVs, chances are explainability is going to be table stakes. Are you prepared to build it into your apps from day one?
