Q&A: Balancing Enterprise AI Deployments & Human Expertise

As enterprise organizations continue to leverage AI technologies to improve efficiency and address operational needs, identifying appropriate use cases and establishing clear initiatives is more important than ever.

Dr. Ryan Ries is the chief AI and data scientist at Mission, an AWS Services Partner. He shared his insights on the blend of technological and human capabilities with Channel Insider.

The Q&A below has been lightly edited for grammar and clarity.

From AI testing to enterprise deployment: use cases define 2026 priorities

2025 seemed to be a year of testing and determining ROI—are enterprises closer to identifying where to use AI most efficiently?

Absolutely. Over the past year, we’ve seen enterprises shift from experimenting with AI for its own sake to evaluating where it can actually produce measurable business impact. Many organizations have run pilots, proof-of-concepts, and generative AI experiments—but not all of them delivered real ROI. What’s becoming clear is that success comes from focusing on workflows where AI either improves efficiency or drives revenue, rather than chasing flashy demos. In 2026, the trend will accelerate: enterprises will carefully prioritize use cases where value is tangible, and discard initiatives that don’t move the needle. These initiatives are often found in automating time-consuming tasks. We’ve seen that by automating these tasks, businesses can significantly increase both profit and productivity.

Read more about how enterprises are measuring real ROI from AI.

How has the increasing prevalence of agentic AI impacted those experiments?

Agentic AI has undoubtedly reshaped the landscape, but it’s important to stay realistic about what these systems can actually do. Many so-called AI agents aren’t truly autonomous; they’re probabilistic models that perform best when guided by human expertise. In our experiments, we’ve seen that agentic AI can accelerate workflows, surface insights, and manage routine tasks effectively. However, without human oversight, their outputs can become inconsistent or even counterproductive. The rapid proliferation of these tools has prompted enterprises to be more intentional about AI integration, focusing on where human judgment and context remain essential. 

We’re also noticing a trend where everyone wants to ‘go agentic,’ but in many cases, these frameworks can be overkill for simple use cases—where a well-crafted prompt might be faster, more cost-effective, and yield better results. Ultimately, it’s critical to evaluate whether an agent is truly needed for the task at hand.

Read more about how channel partners are preparing enterprise infrastructure for the AI era.

Why enterprise AI still depends on human expertise and oversight

In the AI era, where is human involvement still a necessity?

Humans remain critical wherever context, judgment, or domain expertise is required. That includes strategic decisions, complex customer interactions, and any situation where AI outputs need interpretation or validation. Even highly capable AI systems operate on probability and patterns—they don’t understand the full business context the way humans do. For example, an AI agent might recommend a next step based on historical trends, but only a human can weigh that recommendation against regulatory, ethical, or organizational factors. Humans also play a key role in shaping AI itself: designing prompts, selecting training data, and iteratively tuning models so outputs are reliable and aligned with business objectives. In essence, AI augments human capability, but human insight still determines the quality, relevance, and ultimate value of any outcome.

Recently, there’s been a growing debate over whether AI agents truly create new answers or simply uncover overlooked insights buried within existing data. This makes it even more important to critically evaluate AI-generated outputs to ensure they serve your business objectives effectively.

How can enterprises build workloads for the future that scale AI adoption and human involvement?

Building scalable AI workloads is about designing systems where AI and humans complement each other seamlessly. That starts with identifying tasks where AI can add real value (like automating repetitive, data-intensive, or time-sensitive processes), while leaving judgment-heavy, strategic, or nuanced tasks to humans. From there, organizations need infrastructure that supports monitoring, retraining, and iterative improvement, ensuring models remain accurate and aligned with business goals. Governance and security are essential: sensitive data must be protected, and humans need to maintain oversight to catch unexpected outputs or errors.

Ultimately, the future-ready enterprise does not maximize AI adoption for its own sake; it designs workflows to let AI amplify human expertise, enabling teams to act faster, make smarter decisions, and scale impact without losing control or context. This approach creates sustainable adoption and ensures AI delivers measurable ROI in the long term.

Read more about how channel partners are enabling AI adoption worldwide.