AI Cost Estimation
AI Project Scheduling
AI Risk Assessment & Mitigation
AI Labor Estimation
Work Breakdown Structure
Design for Manufacturing (DFM)
Should Cost Analysis
Price to Win (PtW)
Total Cost of Ownership
Design to Cost
RFI, RFP, RFQ
Mastering Cost Risk with the CRED Model: A New Approach to Managing Uncertainty
There are phrases you never expect to say in a boardroom. “Our digital agent is hallucinating” is one of them. Yet this is the reality for leaders who are adopting artificial intelligence across strategy, planning, and execution.
AI is not just a set of tools. It has introduced an entirely new vocabulary for leaders. We now budget for fine-tuning, evaluate confidence ranges, and debate explainability as a business requirement. What was once the language of technologists is now a critical part of boardroom conversations. And with the rise of generative and agentic AI, these terms are becoming as familiar as revenue forecasts and market share.
Hallucinations Are Not Just Science Fiction
When an AI system “hallucinates,” it does not mean the software has failed. It means the output looks plausible but is not anchored in data. In cost estimation, this could mean pulling the wrong supplier rate, recommending a process that does not exist on the shop floor, or suggesting an unrealistic project timeline.
The issue is not that AI is inherently unreliable. It is that leaders need governance structures that separate useful insights from noise. Every recommendation should be traceable. Which data sources were used? What assumptions were applied? How does the output compare to actual results? Without transparency, you are making strategic decisions on unstable ground.
This echoes one of the biggest current AI debates: accuracy versus speed. Generative models can produce outputs faster than any analyst, but without proper validation, leaders risk optimizing for efficiency at the cost of credibility.
From Black Box to Glass Box
Executives do not need to understand the algorithms or data science inside every model. But they do need to know whether the system can withstand scrutiny. Regulators, investors, and customers will expect explainability, and leaders should demand the same.
Moving from a black-box approach to a glass-box standard means insisting on auditable logic. If your production manager or cost engineer cannot explain how a number was generated, your leadership team should not be using it to make multimillion-dollar decisions. This is not just about technology. It is about governance and accountability.
Globally, regulators are already moving in this direction. The EU AI Act, U.S. executive orders, and sector-specific guidelines in finance and healthcare all signal a future where explainability is not optional. Strategic leaders who adopt “glass box” standards early will be ahead of both compliance demands and customer expectations.
Culture Shifts Faster Than Technology
One of the most surprising challenges with AI adoption is cultural, not technical. Teams are starting to treat AI systems like colleagues. A cost engineer might ask, “Can I prompt this model like a junior analyst?” A procurement lead might suggest that a digital agent join supplier reviews. These scenarios may sound unusual, but they signal a deeper shift. Work is being restructured around the interaction between humans and AI.
Leaders must create space for experimentation while maintaining discipline. Encourage teams to test but require validation. Celebrate efficiency but measure it against accuracy.
This cultural evolution is at the heart of the “augmented workforce” trend. Analysts at McKinsey and Deloitte note that the future of work will not be humans versus AI, but humans with AI. The organizations that thrive will be those that treat digital agents as partners while reinforcing the boundaries of human accountability.
What Leaders Should Say Next
Here are four phrases that should be part of every leader’s vocabulary as AI adoption accelerates:
“Show me the assumptions.” Trustworthy AI is transparent AI.
“What is the confidence range?” Risk lives in the range, not in a single number.
“Has this been calibrated against our actuals?” Models without feedback loops do not improve.
“Where’s the human-in-the-loop checkpoint?” AI can augment decision-making, but it does not replace accountability.
To these, many forward-looking executives are now adding a fifth: “Is this aligned with our governance framework?” As agentic AI takes on more autonomous decision-making, governance becomes the line between innovation and exposure.
Closing the Gap Between Expectation and Reality
Hallucinations, prompts, and black boxes are not technical jargon. They are strategic challenges. Leaders are responsible for ensuring that AI delivers value without eroding trust.
This requires governance frameworks, short pilot cycles, and transparency across every use case. It also requires leaders to get comfortable with language that once belonged only to data scientists. Saying “our agent is hallucinating” today may feel strange, but it is only the start. The next phrase might be “our AI just helped us secure a contract” or “our planning process is faster and more accurate than ever.”
The broader AI conversation makes one thing clear: the companies that move past hype and focus on measurable outcomes will define the competitive standard. Agentic AI, generative copilots, and predictive models all hold promise, but without leadership discipline, they remain potential rather than progress.
The future of leadership is not only about setting direction. It is about learning a new language, one that blends technology, data, and human judgment into outcomes that matter. Leaders who adapt to this reality will not just be speaking differently in the boardroom; they will be shaping how entire industries evolve.
10-Step Estimation Process Checklist
View our 10 Step Estimating Process Checklist. This checklist should be tuned to the individual company’s needs and suggestions.
Estimating Total Cost of Ownership (TCO)
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Should Cost Analysis
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ROM Estimate: The First Step Towards a Detailed Project Plan
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Software Maintenance Cost
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