2AM: Altamenty™ Advisory Method
AI decisions are rarely just technology decisions. They are business decisions involving uncertainty, investment, risk, people, governance, and long-term consequences.
The Altamenty™ Advisory Method (2AM) is a structured, evidence-driven approach designed to help leaders examine those decisions from every relevant angle before committing significant resources or accepting unnecessary risk.
The method is grounded in three main pillars: philosophy, scientific reasoning and real-world experience.
Five Stages
Understand
We begin by understanding the decision in its full business context: the objective, the underlying need, the stakeholders involved, the operating environment, the constraints, and the consequences of acting—or not acting.
Before discussing solutions, we make sure we are solving the right problem.
Challenge
We examine the assumptions behind the proposed direction. We question whether AI is truly necessary, whether the expected value is realistic, whether the organization is ready, and whether important alternatives, risks, or dependencies have been overlooked.
The goal is not to create resistance. It is to protect the quality of the decision.
Evaluate
We evaluate the available evidence, strategic alignment, organizational readiness, economics, technical feasibility, governance implications, vendor claims, and potential risks.
Rather than relying on isolated opinions or impressive demonstrations, we consider how the decision is likely to perform within the realities of the organization.
Recommend
We translate the analysis into a clear, independent point of view. Leaders receive practical recommendations, the reasoning behind them, the tradeoffs involved, and a realistic path forward.
Altamenty™ does not sell software, represent vendors, or benefit from a particular technology choice. The recommendation is shaped by what best serves the client.
Support
We remain a trusted thought partner as leaders communicate the decision, align stakeholders, evaluate new information, and navigate the questions that follow.
Our role is not to take ownership of the decision away from leadership. It is to strengthen the judgment behind it.
The Philosophical & Scientific Foundations
The 2AM draws from several complementary schools of thought.
Pragmatism
At its philosophical core is the pragmatism of Charles Sanders Peirce: ideas should be examined through their practical consequences.
For AI leaders, this means moving beyond what a technology promises and asking what it will actually change. What business outcome will it produce? What must be true for that outcome to occur? How will we know whether it worked? What unintended consequences could follow?
A strategy has value only when it can withstand contact with reality.
The Scientific Method
The method reflects the discipline of scientific inquiry: observe carefully, define the problem, form hypotheses, test assumptions, examine evidence, and revise conclusions when the facts require it.
AI decisions are often made in environments filled with incomplete information and confident claims. Scientific thinking creates a more disciplined way to separate what is known from what is assumed—and what still needs to be tested.
Socratic Inquiry
The methodology uses thoughtful questioning to uncover assumptions that may otherwise remain invisible.
Instead of beginning with “Which AI solution should we buy?” we may first ask:
- What decision or process are we trying to improve?
- Why is the current approach insufficient?
- What evidence suggests AI is the right answer?
- What would success look like in measurable terms?
- What happens if the underlying assumptions are wrong?
The quality of the recommendation depends heavily on the quality of the questions asked before it.
First-Principles Thinking
Complex decisions are broken down into their most fundamental components rather than being evaluated through prevailing trends, competitor activity, or vendor narratives.
This helps leaders distinguish genuine business requirements from inherited assumptions and reconstruct the decision around what the organization actually needs.
Systems Thinking
AI does not operate in isolation. Every initiative sits within a larger system of people, processes, data, technology, incentives, policies, and organizational behavior.
The Altamenty™ method therefore considers not only whether a solution can work technically, but also whether the surrounding organization can adopt, govern, sustain, and benefit from it.
Decision Science
The methodology recognizes that executive decisions must often be made before perfect information is available.
It brings structure to uncertainty by examining alternatives, probabilities, tradeoffs, opportunity costs, reversibility, and the consequences of both action and inaction. The objective is not to eliminate uncertainty. It is to make uncertainty visible and manageable.
Responsible and Human-Centered AI
Altamenty™ approaches AI as a capability that must ultimately serve people and organizational purpose.
Value, accountability, transparency, fairness, privacy, security, and human oversight are not treated as issues to address after deployment. They are considered as part of the decision itself.
Built Through Real-World Experience
The Altamenty™ Advisory Method was not created as an abstract consulting model. It was shaped through more than 15 years of experience across scientific research, data, analytics, machine learning, AI governance, and executive leadership.
Its scientific discipline reflects Michelle de Medeiros’s background as a physicist and researcher, including work associated with Fermilab and Argonne National Laboratory. That experience developed the habit of testing assumptions, following evidence, working through uncertainty, and remaining willing to revise a conclusion when the facts change.
Its business orientation was built through leadership roles across multiple industries, where data and AI had to move beyond technical possibility and produce meaningful operational and financial outcomes.
Its practical perspective comes from building and leading teams of more than 30 data and AI professionals; guiding enterprise AI strategy; establishing governance and responsible AI practices; evaluating technologies and use cases; and overseeing AI capabilities used in real operating environments.
That experience includes the development of an MLOps platform supporting more than 17 production models, conversational AI reaching more than 1,200 active users, and a portfolio of data and AI initiatives generating more than $6.4 million in measurable business value.
It also revealed why promising AI initiatives struggle: unclear business ownership, weak data foundations, unrealistic expectations, fragmented accountability, insufficient governance, organizational resistance, and decisions shaped more by market pressure than enterprise readiness.
The 2AM was built from those lessons.
It combines the rigor of a scientist, the perspective of an experienced AI executive, and the independence of an advisor who has no technology to sell.
Because the most important question is rarely whether an organization can use AI.
It is whether it should, where it will create meaningful value, what must be true for it to succeed, and which decision the evidence can genuinely support.