Targeted Review for Impact, Alignment & Delivery (TRIAD)
By Dan Koloski, Berkeley Almand-Hunter, and Zach Blattner
Northeastern University, Roux Institute
The enthusiasm surrounding artificial intelligence integration has reached fever pitch across industries, with boards and shareholders demanding immediate action on AI strategies. Yet the line between transformative opportunity and costly experiment remains perilously thin. While leaders face mounting pressure to demonstrate AI adoption, many find themselves making critical strategic decisions before establishing foundational understanding or organizational readiness.
This moment—like previous technology revolutions—is fundamentally about organizational change management and behavioral transformation. The TRIAD Framework emerged from our work with organizations navigating this challenge, providing a structured approach to evaluate AI opportunities through three sequential questions that form a complete value triad: What do we want? What’s our context? How will we do it?
Named for its three-question architecture, TRIAD guides teams through a pragmatic assessment designed to surface both opportunities and obstacles early. Rather than defaulting to unchecked enthusiasm or excessive caution, the framework helps organizations bias toward action while maintaining clear sight of practical constraints, industry requirements, and genuine value potential.
TRIAD’s power lies in its sequential structure, ensuring strategic alignment and value articulation precede implementation planning. Each question builds upon the previous, creating a comprehensive evaluation that transforms AI decision-making from reactive scrambling to strategic value delivery.
Every AI initiative must begin with clarity of purpose. This first question forces teams to articulate specific business goals before technology enthusiasm takes hold. We explore three critical dimensions:
Strategy Alignment: Which business goals does this initiative address? Why are these priorities important now? AI projects should not be a solution in search of a problem. If teams cannot connect an AI proposal to strategic priorities or pressing challenges, the project likely belongs in the “money pit” quadrant of our prioritization matrix.
Value Proposition: What quantifiable value will this deliver? To whom, and by when? Moving beyond vague promises of “efficiency” or “innovation,” teams must identify specific metrics that will validate success or signal failure.
Output Definition: When are we done? How will we know? Clear success criteria and completion milestones prevent endless iteration and scope creep that plague many AI initiatives.
Teams that cannot answer these questions shouldn’t immediately abandon the opportunity—but instead should identify specific steps and stakeholders needed to generate complete responses before proceeding.
Once value and purpose are established, teams must honestly assess their organizational reality. This second question examines the pragmatic factors that determine feasibility:
Data Readiness: Do we have the data? Is it accessible, documented, and trustworthy? Proprietary data represents an organization’s unique competitive advantage, but that doesn’t mean it’s genuinely ready for AI integration nor that it’s clean and accurate. This assessment often reveals the hidden work required before any algorithm can deliver value.
Stakeholder Landscape: Who is accountable for success? Who needs to be engaged? Who will be affected? Understanding the human dimension—from executive sponsors to end users—determines whether an initiative gains momentum or stalls despite technical success.
Risk Assessment: What operational, technical, reputational, regulatory, or compliance risks exist? Different industries face distinct challenges; what works for a startup may be untenable for a regulated financial institution or legacy company with a lot of tech debt. Honest risk evaluation prevents costly surprises.
These dimensions overlap and interact. Data brings inherent risks; stakeholder engagement determines who manages the technical work. This interconnected assessment reveals the true complexity beneath surface-level enthusiasm.
Only after establishing purpose and context should teams address implementation. This final question examines the tactical realities of execution:
Consumption Model: Build versus buy versus partner? Standalone or integrated? These decisions flow naturally from the context assessment, balancing internal capabilities against external expertise and considering how the solution fits existing systems.
Effort Estimation: How hard is deployment really? What about ongoing operations? True cost-benefit analysis requires honest assessment of both launch effort and maintenance burden, measured against the value proposition established earlier.
Talent Readiness: Are technical teams prepared? What about business users? Beyond technical skills, successful AI adoption requires change management capability and user readiness—factors often overlooked in technology-focused planning.
Teams should identify pilot opportunities and specific individuals who would own the work, determining whether this requires new hires or existing staff have capacity.
We deploy TRIAD through experiential workshops where leadership teams work through real opportunities. Starting with Essential AI for Leaders to establish shared understanding, we progress to Identifying AI Opportunities or Evaluating AI Opportunities sessions where participants apply the framework to their specific challenges.
The goal isn’t just about reaching immediate decisions but more importantly about building organizational muscle memory for pragmatic ongoing evaluation. Participants engage in honest debate, stack-rank opportunities on an Impact versus Complexity matrix (inspired by Eisenhower, 1954 and Covey, 1989), and develop comfort with the framework that enables faster, more effective assessment of future possibilities.
We customize TRIAD to align with each organization’s nomenclature and existing governance structures. This ensures the framework becomes embedded in practical decision-making rather than remaining an academic exercise.
TRIAD transforms how organizations approach technology-enabled transformation. By establishing a common language and evaluation discipline, teams move beyond the paralysis of endless possibilities or the chaos of uncoordinated experiments that do not deliver value.
The framework particularly helps organizations avoid common pitfalls: pursuing AI for its own sake, underestimating implementation complexity, or abandoning valuable opportunities due to unclear evaluation criteria. Instead, TRIAD enables the kind of clear-eyed assessment that identifies true opportunities for value, whether they be “quick wins” (low-impact, low-complexity opportunities that are worth pursuing), “big bets” (high-impact, high-complexity” opportunities that demand attention), and maybe one of those rare “unicorns” (high-impact, low-complexity initiatives) — while avoiding costly “money pits.”
Most importantly, TRIAD instills behavioral change across the organization. When employees at all levels can perform quick, back-of-the-napkin evaluations using consistent criteria, the entire organization becomes more adept at recognizing and capturing value from technological disruption.
The TRIAD Framework doesn’t promise to make AI decisions easy—it makes them clear. In a landscape where technology capabilities evolve daily but organizational fundamentals remain constant, TRIAD provides the structured thinking that transforms AI from a source of anxiety to an engine of strategic value.
Organizations that master this evaluation discipline will find themselves better positioned not just for current AI opportunities, but for whatever technological disruption emerges next. Because while the specific technologies will change, the fundamental questions remain: What do we want? What’s our context? How will we do it?
The answers to these questions—pursued honestly and sequentially—chart the path from technological possibility to business value.
The Triad Framework™️ is a proprietary methodology developed by Koloski, Almand-Hunter, and Blattner at Northeastern University’s Roux Institute. For workshop information and implementation support, visit our custom learning webpage.
©️2025 Northeastern University. All rights reserved.
References
Eisenhower, D. D. (1954). Address at the Second Assembly of the World Council of Churches, Evanston, Illinois.
Covey, S. R. (1989). The seven habits of highly effective people: Restoring the character ethic. Simon and Schuster.