Creating AI That Works for People
AI Engineering Experts turn AI capabilities into Actual Performance
Background (current state)
Despite the massive investments in AI Gold Rush II, the massive advances in processing power and algorithmic techniques, wide scale Human-AI Teaming success continues to struggle. Gartner reports “…85% of AI projects fail to deploy…”. A recent MIT report cites “…95% of AI projects fail to deliver on their business objectives…” Trust in AI remains a major research area. Even as AI is hyped as “mature” and widely adopted, it continues to fail in the messy, unpredictable realities of real-world use.
Gartner labeled speech recognition “fully mature” back in 2017—yet we still get garbled text messages every day. Imagine relying on that “mature” tech for a life-critical command. By 2023, speech recognition is considered routine engineering, but the mistakes persist. Now computer vision is the latest technology declared “ready for prime time,” yet we’ve already been brought into multiple projects where these systems were rejected by users as unreliable—often because of both the limitations of the AI and the way the overall system was designed.
Why are decades of human factors insights into human machine teams and how to deliver true affordances being overlooked…while AI is stuck in this struggle to deliver operational value?
What does the Human Factors/Cognitive Systems Engineering community have to offer to the *massive* AI development community? (i.e. what are the AI folks missing/overlooking)
The Cognitive Systems Engineering community has the perfect opportunity to deliver value, to directly improve the delivered success rate of this newest class of Human-Technology system that we’ve been studying and building for decades. There is the value proposition for our contribution – we already have the missing piece of the Human-AI Teaming puzzle – and that’s the way to deliver AI value at scale.
RCS’s approach to addressing this issue.
At RCS our AI Engineering Experts turn AI capabilities into actual performance. We design AI solutions that are transparent, resilient, and aligned with real user workflows, so operators can clearly understand system insights and behaviors. This makes our solutions accessible to both experts and non-experts, reducing workload and increasing trust in the automation. We place equal importance on advanced algorithm integration and high-quality interface design, ensuring that mission-critical information is presented in ways that support rapid, confident decision-making.
We do this by combining state-of-the-art AI/ML with proven principles from Cognitive Systems Engineering, RCS delivers AI solutions that perform reliably in real-world complexity—not just in the lab. AI Engineering Experts design systems that respect how humans think and how their business operates. They are ergonomics specialists for the brain. By inserting this expertise into your AI initiative, you ensure your workflow reaches a revolutionary new operating point.
The result is stronger human-AI teams, more resilient decision-support capabilities, and safer, more effective outcomes where it matters most. This means faster decisions, fewer errors, and significantly reduced cognitive burden on your workforce. With CSE-shaped AI, your people aren’t overwhelmed by data, they’re empowered by it.
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