AI Raises the Bar
Success with AI depends on how well the technology supports your people and advances your strategy.
In this five-part series, I am sharing how to use the ALIGN framework to troubleshoot friction in AI implementations and turn AI into a genuine strategic advantage.
In Absorb, leaders gather data to understand what is actually happening.
In Legitimize, leaders respond with a clear, prioritized roadmap that reflects what they heard from stakeholders, what the data reinforces, and what they observed firsthand.
In Integrate, that roadmap meets the realities of daily work, where priorities, systems, and roles must begin to line up in practice.
In Grow, we explore ways to strengthen the organization’s capacity to carry the work forward.
Strength the Ecosystem
Congratulations. You have launched your AI initiative. Now the real work begins.
Once priorities have been translated into action during the Integrate phase, the next step is to strengthen the people and systems that will carry the work forward. This is the Grow phase, where leaders, teams, and individuals build the capacity, confidence, and resilience required to sustain progress.
A byproduct of AI’s ability to operate at a much faster pace than humans is that it accelerates decisions, exposes inconsistencies, and makes gaps in clarity, judgment, and leadership visible far more quickly than before. In doing so, AI introduces a new kind of pressure into the organization.
Grow is the phase where organizations pause long enough to understand their current state and identify where strategy, people, and processes must grow in skill and understanding for AI to be effective. Without this work, AI will amplify issues that humans might previously have corrected in the moment, issues that now risk being embedded into automated systems and scaled across the organization.
Reallocation, not Replacement
When people hear “AI” and “efficiency,” they often hear “replacement.” That fear is understandable. No one wants to feel that their judgment, experience, or contribution can be reduced to a machine.
The reality is that people and technology are good at different aspects of work. AI is well suited to speed, scale, and consistency. Humans are well suited to context, judgment, and meaning. Grow is about building the skills and knowledge that enable people to work at their best. In the context of AI implementation, Grow also focuses on maximizing human strengths so the technology is effective, making that division of labor explicit and workable.
While organizations may not explicitly state that they intend to replace people with AI, there is often a quiet hope that once AI is introduced, cost savings will be realized by reducing or stretching human involvement. What is often underestimated is the opposite reality: AI raises the demand for human capability. As systems move faster and decisions scale, organizations need clearer judgment, stronger decision-making, and more disciplined learning than before. When that capacity is not developed alongside the technology, AI exposes gaps that teams are not equipped to manage.
While this gap often shows up first in how leaders think, decide, and learn, it does not stay there. These demands quickly move into execution, shaping how work is paced, how risks are managed, how quality is maintained, and how people experience the culture under pressure. Grow is where strategic intent and cultural norms translate into how work actually gets done across roles and teams.
Strengths and Meaningful Work
As a Gallup Strengths coach, I am very aware that people thrive in different parts of execution. What feels energizing to one person may feel draining to another. Someone with Strategic may want to move quickly and adjust course as new information appears. Someone with Deliberative may want time to think risks through. A person high in Adaptability may shift naturally to meet the moment, while someone strong in Consistency finds meaning in stability and reliability.
What one person finds dull gives another person energy. What one person finds difficult is easy for another. There may be assumptions that certain kinds of work are inherently dull or undesirable while for many people they find deep satisfaction in work that is structured, repeatable, and predictable. They take pride in accuracy, continuity, and getting things right over time. That contribution matters. AI does not eliminate the need for those qualities. It changes where they are applied.
As systems take on more of the mechanical repetition, human work often shifts toward oversight, quality, exception handling, and judgment at the edges. For someone who values consistency or deliberation, this may mean becoming a steward of reliability rather than a processor of volume. For someone who values adaptability or strategy, it may mean focusing more on direction-setting and course correction. The work evolves, but the strengths still matter.
Grow is about helping people see how what they naturally do well continues to be valuable as the shape of the work changes. It is not about forcing everyone into the same kind of role. It is about redirecting strengths so people can contribute with confidence and pride as AI becomes part of how work gets done.
Personal Agency
Admitting where support is needed can be uncomfortable, especially in environments that reward competence and speed. Leaders can lower that barrier by starting with strengths. Naming what someone already does well, and why those strengths matter to the work or the team, establishes respect and context before discussing development needs.
From there, invite the individual to identify where additional support would be most helpful. When people are asked for their perspective first, the conversation shifts from evaluation to collaboration. The perceived risk drops, and participation in learning becomes more likely because it feels self-directed rather than imposed.
This process is more effective when leaders pair the conversation with a defined set of options. Offering a clear menu of development supports signals that help is real, available, and endorsed. People are more willing to name what they need when they know those needs can actually be met.
People at every level, from the front line to the C-suite, benefit from support that helps them calibrate their strengths in an AI-augmented environment.
Growth in Capacity Precedes Growth in Results
Many organizations name growth as a top priority while simultaneously contending with uneven execution, workforce strain, and unresolved questions about identity and direction. These tensions matter because AI does not operate in a vacuum. It operationalizes whatever clarity or confusion already exists in the system.
Organizations often articulate ambitions to use AI to improve efficiency, strengthen operations, or elevate the customer experience. Those outcomes are achievable, but only if the organization grows in parallel in several critical ways.
Grow focuses on building clarity and judgment so that speed does not outpace understanding.
A Critical AI Design Constraint
People routinely navigate ambiguity by applying context and judgment. AI does not have that context. When clarity is missing, it will still produce an output, but that output is based on statistical inference rather than situational understanding.
AI does not understand a situation. It recognizes patterns.
More specifically, statistical inference means that AI:
Looks at large volumes of past data.
Identifies patterns, correlations, and probabilities within that data.
Uses those patterns to predict or generate what is most likely to come next, based on the inputs it receives.
What AI does not do:
Understand why something matters in this moment.
Grasp intent, consequences, or tradeoffs unless they have been explicitly defined.
Sense shifts in tone, trust, pressure, or context the way people do.
Adjust based on lived experience or values unless those are encoded into rules or training data.
When clarity is missing, AI does not pause or ask for meaning. It fills the gap by extending patterns it has seen before. This is why Grow matters.
Strategy (Including Brand)
Strategy sets direction. It establishes what the organization is trying to become and how success will be defined.
What AI can do
Analyze large volumes of data to identify trends, opportunities, and risks.
Model scenarios and tradeoffs based on defined objectives.
Optimize toward stated goals and scale strategic decisions quickly.
What AI needs humans to be clear about
What the organization is trying to become, not just what it is trying to improve.
What differentiates the organization and what should not be optimized away.
Which tradeoffs matter most when priorities conflict.
How success is defined beyond speed, volume, or short-term gain.
Development focuses on strengthening direction-setting.
Examples of effective support include:
Strategy clarification sessions that force explicit tradeoffs.
Leader working sessions on framing useful questions and constraints for AI systems.
Coaching senior leaders to articulate strategy and brand intent in operational terms.
Scenario-based exercises that practice decisions with incomplete or competing data.
Culture
Culture shapes how decisions are made when pressure is high and priorities collide. It is experienced through what people believe will be supported when judgment is required.
What AI can do
Apply policies, rules, and priorities consistently once expectations are clear.
Reinforce patterns of work through scheduling, workflows, and automated interactions.
Reduce variability in routine decisions.
What AI needs humans to be clear about
How values are meant to guide real decisions.
What matters when priorities compete.
Where discretion is expected and where consistency is required.
What people can trust will be supported when judgment is exercised.
Development focuses on shared understanding.
Examples of effective support include:
Facilitated conversations using real decisions to explore how values apply in practice.
Leader coaching on explaining tradeoffs, especially when decisions disappoint someone.
Team forums that normalize naming tension instead of working around it.
Clear escalation paths so individuals are not carrying cultural decisions alone.
Execution
Execution is how strategy becomes real in day-to-day work. It is the set of decisions, handoffs, and follow-through that determine whether priorities are delivered or quietly eroded over time.
What AI can do
Monitor performance continuously and at scale.
Surface patterns, anomalies, and early warning signals.
Automate routine tasks and reporting.
What AI needs humans to be clear about
Which signals matter and which can be ignored.
What constitutes meaningful deviation versus normal variation.
When human intervention is required and who is accountable.
How decisions should be made when data is incomplete or contradictory.
Development focuses on judgment under speed.
Examples of effective support include:
Helping leaders and managers interpret AI outputs in the context of real operating decisions.
Practicing how to respond to early signals using live data, not hypothetical scenarios.
Clarifying decision rights so accountability remains with people, not systems.
Coaching leaders to intervene early and proportionately, rather than waiting for failure or overreacting to variation.
Normalize the Learning Curve
Innovation requires new skills, and proficiency takes time. Leaders play a critical role by modeling curiosity, calibrating expectations to the reality of learning, and creating conditions where learning is expected and supported.
Feedback as Fuel
Growth depends on feedback that is integrated into everyday work. When people see their insights shaping real decisions, trust increases and resistance decreases. Feedback becomes a source of refinement rather than a penalty.
The Result: A Culture of Continuous Learning
AI increases the demand for continuous learning. While there is a temptation to believe technology will reduce the need to build human capacity, this kind of technology does the opposite. It requires more clarity, more judgment, and more learning over time. When people do better, AI does better. In that way, AI becomes a reflection of an organization’s commitment to learning, especially when the pressure to move faster is highest.
UP NEXT: Nurture
In the next post, Nurture, we’ll explore the ongoing commitment leaders must make to protect, reinforce, and sustain the human practices that allow an AI-enhanced organization to realize its full potential.