Unlocking AI’s Potential: Why Your People Are the Key
The scent of stale coffee hung in the air of the open-plan office, a familiar comfort for Priya, a marketing director at a sprawling consumer goods firm.
She stared at the new GenAI dashboard on her screen, its sleek interface promising revolutionary efficiency.
The email from leadership had been clear: Embrace AI, transform work.
Yet, as she watched her team, heads bent over their keyboards, grappling with new prompts and unfamiliar interfaces, she felt a gnawing disconnect.
The tools were here, gleaming and potent, but the change felt distant, clunky.
She knew, deep down, that merely having the keys to a powerful new engine did not automatically mean everyone knew how to drive it, let alone win the race.
This is not just Priya’s struggle; it is a widespread organizational challenge.
AI, particularly generative AI (GenAI), holds revolutionary potential for the world of work, yet its full value remains largely untapped.
While most organizations are experimenting with AI tools, many are lagging in the deep transformation necessary to unlock meaningful changes in how people work.
The gap between AI adoption and tangible business value is significant.
Many companies see this chasm and instinctively double down on adopting more AI tools, rather than building the human capabilities that truly translate adoption into lasting business value.
To truly harness AI’s power, organizations must pivot beyond simply adopting tools to deeply invest in their people.
This involves building comprehensive, enterprise-wide enablement systems that embed AI into how everyone thinks, works, and leads, fostering lasting behavioral change to unlock significant business impact.
The Chasm Between Tools and True Value
We live in an era where AI is ubiquitous, its promise echoing across every industry.
Yet, for many organizations, that promise feels more like a distant mirage than a tangible reality.
Companies acquire cutting-edge AI software, roll out licenses, and encourage experimentation, only to find that the revolutionary shifts they envisioned are not materializing at scale.
The counterintuitive truth is this: the problem is not usually the technology itself.
It is the human element – the failure to adequately prepare, empower, and integrate people into the AI-powered future.
What is often observed is a consistent pattern: organizations invest heavily in technology, then wonder why the desired outcomes—faster innovation, increased productivity, enhanced customer experience—remain elusive.
They mistake tool adoption for true transformation.
But genuine AI value is not extracted from algorithms alone; it is forged in the intersection of intelligent technology and human ingenuity.
It requires a fundamental shift in how people think about their work, how they interact with AI, and how new capabilities are systematically integrated into daily operations.
From Manual Grind to Digital Leap: Rethinking Operations
Consider a financial institution facing the challenge of reimagining its lending operations.
The process was often mired in manual checks, document validation, and data transfers—a time-consuming grind for employees and a source of frustration for customers.
The institution recognized that GenAI offered an opportunity to transform this core function, but it was not just about buying a new piece of software.
They focused on a clear value pool: lending operations, where automation could reduce costs and improve customer experience.
The institution simplified its core lending processes, introducing an Ops AI Agent to automate mundane tasks.
This was not merely plugging AI into the old system; it involved rebuilding workflows, identifying friction points, redesigning handoffs, and codifying new ways of working.
Employees were then able to focus on higher-value activities, moving from data entry to more nuanced problem-solving.
This holistic approach, centered on both technology and a transformed human process, can lead to substantial improvements in efficiency and customer satisfaction.
The critical insight here is that the technology unlocked potential value, but the people’s ability to adapt to new workflows and leverage the AI effectively drove the transformation.
What True Capability Building Looks Like
Effective capability-building approaches show that traditional training programs, while a good start, simply are not enough to meet the scale and urgency of the GenAI moment.
True capability-building goes deeper, aiming for lasting behavior change rather than just knowledge acquisition.
It is a three-part learning progression that turns knowing into doing, and doing into habit.
The foundational stage
The foundational stage is about imparting new knowledge.
It involves key concepts, frameworks, vocabulary, and those crucial a-ha moments that broaden understanding.
It is where people learn what AI is and why it matters.
The practical implication for businesses is ensuring everyone has a baseline understanding, fostering a shared language around AI’s potential and limitations.
The applied stage
The applied stage is where knowing turns into doing.
This stage focuses on on-the-job practice, tied directly to real workflows.
It moves beyond theoretical understanding to practical application, giving people the chance to experiment and integrate AI into their daily tasks.
The practical implication is to provide safe spaces for experimentation and immediate feedback loops, ensuring new skills are directly relevant and useful.
The embedded stage
The embedded stage, and arguably most crucial, is about turning doing into habit.
This is achieved by codifying new practices into standard ways of working, role expectations, support structures, and even incentive programs.
The practical implication is that organizations must redesign their operational models to make AI-enabled work the default, not an optional add-on, fostering a culture of continuous learning and adaptation.
Traditional training often stops at the foundational stage, leaving employees with knowledge but little sustained impact on their actual work.
To truly unlock AI’s full value, organizations must journey through all three stages, strategically prioritizing AI capability building in areas where the potential for return on investment is highest.
A Playbook You Can Use Today
Realizing AI’s full value potential requires a step change in an organization’s capabilities—a mix of technical fluency and human skills supported by behaviors, mindsets, and systems that help people work differently, and more effectively, with AI.
Here is a playbook to guide your enterprise AI enablement:
- Ground AI Enablement in Clear Context: Begin by identifying high-potential value pools and redesigning related workflows.
Understand existing AI capabilities—your starting point—and define a bold future-state vision at the team or workflow level.
This strategic clarity ensures efforts are focused where they will yield the greatest impact.
- Develop Role-Specific Capabilities: AI transformation is an all-hands-on-deck endeavor, but not all hands have the same involvement.
Tailor learning journeys to specific archetypes.
Shapers are executives who set the AI vision.
Leaders are managers who create conditions for AI to scale.
Enablers are experts who support adoption.
Users are frontline employees integrating AI daily.
This role-based training ensures relevance and builds confidence for each individual.
- Equip Leaders to Model a Unified Vision: The most effective AI transformations start at the top.
Leaders must be aligned on the transformation’s goals and process, then visibly practice and reinforce the desired behaviors.
This means providing executives with the fluency to guide their organization coherently, fostering a shared language between business and technology leaders.
Leaders need to practice and model these new behaviors.
- Prioritize Trust and Motivation: AI rollouts can elicit skepticism and anxiety, especially concerning job security or output quality.
Engage with these concerns head-on.
Upskill managers in change management and GenAI fluency, empowering them to address team concerns with empathy and reinforce the business case for change.
Creating a safe environment for experimentation fosters confidence and turns potential threats into sources of empowerment.
The real challenge with AI is often not the technology, but getting people to trust it.
- Measure What Really Matters: Value, Not Just Adoption: While tracking AI tool adoption is a start, true success lies in measuring how new capabilities unlock value pools across the business.
Focus on metrics that show tangible business impact: faster time to market, improved decision-making, cost savings, or better customer outcomes.
A common pitfall in digital transformation is equating adoption with success.
Instead, define desired business outcomes, then trace how changes in skills and behaviors contribute to those results.
This integrated approach moves beyond sporadic training events to establish a robust enterprise AI enablement system, building capabilities that drive lasting business impact.
Risks, Trade-offs, and Ethics
Embarking on an AI transformation journey is not without its challenges.
Over-reliance on AI without critical human oversight can lead to a degradation of essential human skills or propagate biases embedded in the data.
There is also the very real risk of alienating employees who fear job displacement or perceive AI as a threat to their expertise.
To mitigate these, transparency is paramount.
Clearly communicate the why behind AI adoption, emphasizing augmentation rather than replacement.
Establish ethical guidelines for AI use, fostering a culture of responsible AI.
Invest proactively in workforce upskilling and reskilling programs that prepare your workforce for new, AI-enabled roles, ensuring a just transition.
The trade-off for speed and efficiency should never be a loss of human dignity or critical thinking.
A human-first approach ensures AI serves people, not the other way around.
Tools, Metrics, and Cadence
Tools and Platforms
- Look for AI Enablement Platforms that integrate learning content, hands-on labs, and real-time usage data.
Leverage existing Collaboration Platforms (e.g., Microsoft Teams, Slack, internal intranets) to foster peer-to-peer learning, share best practices, and facilitate discussions.
Embed AI Assistants or Copilots directly into workflows to encourage experimentation in the flow of work.
Create a centralized Knowledge Management System repository for AI guidelines, prompt libraries, and success stories.
Key Performance Indicators (KPIs) for Value
- Engagement and Fluency can be measured by the number of new AI use cases generated by employees, frequency and variety of AI tool usage, and observed improvement in prompt engineering quality.
- Productivity and Efficiency include time saved on specific tasks, reduction in errors, acceleration of decision-making processes, and faster project completion times.
- Quality and Innovation metrics include improvement in output quality (e.g., marketing copy, code snippets), increase in innovative solutions developed with AI support, and enhanced customer satisfaction scores.
- Behavioral Change can be seen in documented shifts in ways of working, proactive sharing of AI insights, and employees coaching peers on AI tools.
Review Cadence
- Implement Weekly or Bi-weekly team-level AI sprints for hands-on experimentation, peer reviews, and troubleshooting.
Conduct Monthly departmental leadership reviews focusing on progress against specific value pools, identifying friction points, and real-time course corrections.
Hold Quarterly executive leadership summits to assess overall AI transformation, strategic alignment, and resource allocation, ensuring the AI strategy remains intertwined with business strategy.
Frequently Asked Questions
How do organizations typically fall short in realizing AI’s value?
Many organizations focus too heavily on merely adopting AI tools rather than fundamentally investing in the human capabilities—skills, mindsets, and behaviors—needed to integrate AI effectively into workflows and leadership.
What is the difference between traditional training and effective AI capability building?
Traditional training often stops at imparting knowledge (Foundational stage).
Effective AI capability building moves beyond this to include applied, on-the-job practice and embedded new habits into daily operations and organizational structures.
This aligns with the learning progression framework discussed earlier.
How can leaders effectively champion AI transformation?
Leaders must share a unified vision for AI, visibly model desired AI-enabled behaviors, and create the conditions for AI to scale.
This requires technical fluency paired with strong change management skills.
What is the best way to build employee trust in AI?
Building trust involves engaging head-on with skepticism, upskilling managers to coach with empathy, providing safe environments for experimentation, and clearly communicating the business value and benefits of AI to individuals.
What metrics truly indicate AI transformation success?
Success metrics should extend beyond tool adoption rates to focus on tangible business outcomes, such as time saved, errors reduced, decisions accelerated, and the generation of new AI-driven use cases, indicating a deeper capability shift.
The Human Core of AI Value
Priya now approaches her AI dashboard with a different lens.
She understands it is not just a tool; it is an invitation to a new way of working.
Her team, once tentative, now shares prompts, celebrates small wins, and collectively brainstorms how AI can elevate their creativity, not just automate tasks.
The hum of the office still signifies busy work, but now, there is a different energy—a spark of confidence, a sense of shared purpose.
The full value of AI is not hiding in lines of code or complex algorithms.
It resides, vibrantly and powerfully, in the hands, minds, and hearts of your people.
When you invest in them, truly empower them, you do not just adopt AI; you unlock a future where human ingenuity and artificial intelligence collaborate to achieve the extraordinary.
This is the moment to move beyond tools and invest in transformation.
Let us build that future, together.
References
- World Economic Forum. (2023). Future of Jobs Report 2023.
- McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier.
- Deloitte. (2023). Generative AI in the enterprise: A new era for human-machine collaboration.
- Harvard Business Review. (2023). What It Takes to Build an AI-Driven Organization.
Glossary
Generative AI (GenAI)
A type of artificial intelligence that can create new content, like text, images, or code, often based on patterns learned from vast amounts of existing data.
Value Pools
Specific areas within a business where the application of AI can generate significant and measurable benefits, such as cost savings, increased revenue, or improved customer satisfaction.
Capability-building
A holistic approach to developing skills, knowledge, mindsets, and behaviors within an organization, going beyond basic training to embed new ways of working.
Learning Progression
A structured, multi-stage approach to education, moving from foundational knowledge to applied practice and finally to embedded habits.
Role-Specific Capabilities
Tailoring learning and development programs to the unique needs, responsibilities, and challenges of different roles or archetypes within an organization.
Change Management
The process of guiding individuals, teams, and organizations through transitions, ensuring successful adoption and proficiency of new processes, tools, or strategies.
Ethical AI
The practice of developing and deploying artificial intelligence responsibly, considering fairness, transparency, privacy, and potential societal impacts.
Upskilling/Reskilling
Programs designed to teach employees new skills (upskilling) or entirely new sets of skills (reskilling) to remain relevant and effective in an evolving job market.
Article start from Hers……
Unlocking AI’s Potential: Why Your People Are the Key
The scent of stale coffee hung in the air of the open-plan office, a familiar comfort for Priya, a marketing director at a sprawling consumer goods firm.
She stared at the new GenAI dashboard on her screen, its sleek interface promising revolutionary efficiency.
The email from leadership had been clear: Embrace AI, transform work.
Yet, as she watched her team, heads bent over their keyboards, grappling with new prompts and unfamiliar interfaces, she felt a gnawing disconnect.
The tools were here, gleaming and potent, but the change felt distant, clunky.
She knew, deep down, that merely having the keys to a powerful new engine did not automatically mean everyone knew how to drive it, let alone win the race.
This is not just Priya’s struggle; it is a widespread organizational challenge.
AI, particularly generative AI (GenAI), holds revolutionary potential for the world of work, yet its full value remains largely untapped.
While most organizations are experimenting with AI tools, many are lagging in the deep transformation necessary to unlock meaningful changes in how people work.
The gap between AI adoption and tangible business value is significant.
Many companies see this chasm and instinctively double down on adopting more AI tools, rather than building the human capabilities that truly translate adoption into lasting business value.
To truly harness AI’s power, organizations must pivot beyond simply adopting tools to deeply invest in their people.
This involves building comprehensive, enterprise-wide enablement systems that embed AI into how everyone thinks, works, and leads, fostering lasting behavioral change to unlock significant business impact.
The Chasm Between Tools and True Value
We live in an era where AI is ubiquitous, its promise echoing across every industry.
Yet, for many organizations, that promise feels more like a distant mirage than a tangible reality.
Companies acquire cutting-edge AI software, roll out licenses, and encourage experimentation, only to find that the revolutionary shifts they envisioned are not materializing at scale.
The counterintuitive truth is this: the problem is not usually the technology itself.
It is the human element – the failure to adequately prepare, empower, and integrate people into the AI-powered future.
What is often observed is a consistent pattern: organizations invest heavily in technology, then wonder why the desired outcomes—faster innovation, increased productivity, enhanced customer experience—remain elusive.
They mistake tool adoption for true transformation.
But genuine AI value is not extracted from algorithms alone; it is forged in the intersection of intelligent technology and human ingenuity.
It requires a fundamental shift in how people think about their work, how they interact with AI, and how new capabilities are systematically integrated into daily operations.
From Manual Grind to Digital Leap: Rethinking Operations
Consider a financial institution facing the challenge of reimagining its lending operations.
The process was often mired in manual checks, document validation, and data transfers—a time-consuming grind for employees and a source of frustration for customers.
The institution recognized that GenAI offered an opportunity to transform this core function, but it was not just about buying a new piece of software.
They focused on a clear value pool: lending operations, where automation could reduce costs and improve customer experience.
The institution simplified its core lending processes, introducing an Ops AI Agent to automate mundane tasks.
This was not merely plugging AI into the old system; it involved rebuilding workflows, identifying friction points, redesigning handoffs, and codifying new ways of working.
Employees were then able to focus on higher-value activities, moving from data entry to more nuanced problem-solving.
This holistic approach, centered on both technology and a transformed human process, can lead to substantial improvements in efficiency and customer satisfaction.
The critical insight here is that the technology unlocked potential value, but the people’s ability to adapt to new workflows and leverage the AI effectively drove the transformation.
What True Capability Building Looks Like
Effective capability-building approaches show that traditional training programs, while a good start, simply are not enough to meet the scale and urgency of the GenAI moment.
True capability-building goes deeper, aiming for lasting behavior change rather than just knowledge acquisition.
It is a three-part learning progression that turns knowing into doing, and doing into habit.
The foundational stage
The foundational stage is about imparting new knowledge.
It involves key concepts, frameworks, vocabulary, and those crucial a-ha moments that broaden understanding.
It is where people learn what AI is and why it matters.
The practical implication for businesses is ensuring everyone has a baseline understanding, fostering a shared language around AI’s potential and limitations.
The applied stage
The applied stage is where knowing turns into doing.
This stage focuses on on-the-job practice, tied directly to real workflows.
It moves beyond theoretical understanding to practical application, giving people the chance to experiment and integrate AI into their daily tasks.
The practical implication is to provide safe spaces for experimentation and immediate feedback loops, ensuring new skills are directly relevant and useful.
The embedded stage
The embedded stage, and arguably most crucial, is about turning doing into habit.
This is achieved by codifying new practices into standard ways of working, role expectations, support structures, and even incentive programs.
The practical implication is that organizations must redesign their operational models to make AI-enabled work the default, not an optional add-on, fostering a culture of continuous learning and adaptation.
Traditional training often stops at the foundational stage, leaving employees with knowledge but little sustained impact on their actual work.
To truly unlock AI’s full value, organizations must journey through all three stages, strategically prioritizing AI capability building in areas where the potential for return on investment is highest.
A Playbook You Can Use Today
Realizing AI’s full value potential requires a step change in an organization’s capabilities—a mix of technical fluency and human skills supported by behaviors, mindsets, and systems that help people work differently, and more effectively, with AI.
Here is a playbook to guide your enterprise AI enablement:
- Ground AI Enablement in Clear Context: Begin by identifying high-potential value pools and redesigning related workflows.
Understand existing AI capabilities—your starting point—and define a bold future-state vision at the team or workflow level.
This strategic clarity ensures efforts are focused where they will yield the greatest impact.
- Develop Role-Specific Capabilities: AI transformation is an all-hands-on-deck endeavor, but not all hands have the same involvement.
Tailor learning journeys to specific archetypes.
Shapers are executives who set the AI vision.
Leaders are managers who create conditions for AI to scale.
Enablers are experts who support adoption.
Users are frontline employees integrating AI daily.
This role-based training ensures relevance and builds confidence for each individual.
- Equip Leaders to Model a Unified Vision: The most effective AI transformations start at the top.
Leaders must be aligned on the transformation’s goals and process, then visibly practice and reinforce the desired behaviors.
This means providing executives with the fluency to guide their organization coherently, fostering a shared language between business and technology leaders.
Leaders need to practice and model these new behaviors.
- Prioritize Trust and Motivation: AI rollouts can elicit skepticism and anxiety, especially concerning job security or output quality.
Engage with these concerns head-on.
Upskill managers in change management and GenAI fluency, empowering them to address team concerns with empathy and reinforce the business case for change.
Creating a safe environment for experimentation fosters confidence and turns potential threats into sources of empowerment.
The real challenge with AI is often not the technology, but getting people to trust it.
- Measure What Really Matters: Value, Not Just Adoption: While tracking AI tool adoption is a start, true success lies in measuring how new capabilities unlock value pools across the business.
Focus on metrics that show tangible business impact: faster time to market, improved decision-making, cost savings, or better customer outcomes.
A common pitfall in digital transformation is equating adoption with success.
Instead, define desired business outcomes, then trace how changes in skills and behaviors contribute to those results.
This integrated approach moves beyond sporadic training events to establish a robust enterprise AI enablement system, building capabilities that drive lasting business impact.
Risks, Trade-offs, and Ethics
Embarking on an AI transformation journey is not without its challenges.
Over-reliance on AI without critical human oversight can lead to a degradation of essential human skills or propagate biases embedded in the data.
There is also the very real risk of alienating employees who fear job displacement or perceive AI as a threat to their expertise.
To mitigate these, transparency is paramount.
Clearly communicate the why behind AI adoption, emphasizing augmentation rather than replacement.
Establish ethical guidelines for AI use, fostering a culture of responsible AI.
Invest proactively in workforce upskilling and reskilling programs that prepare your workforce for new, AI-enabled roles, ensuring a just transition.
The trade-off for speed and efficiency should never be a loss of human dignity or critical thinking.
A human-first approach ensures AI serves people, not the other way around.
Tools, Metrics, and Cadence
Tools and Platforms
- Look for AI Enablement Platforms that integrate learning content, hands-on labs, and real-time usage data.
Leverage existing Collaboration Platforms (e.g., Microsoft Teams, Slack, internal intranets) to foster peer-to-peer learning, share best practices, and facilitate discussions.
Embed AI Assistants or Copilots directly into workflows to encourage experimentation in the flow of work.
Create a centralized Knowledge Management System repository for AI guidelines, prompt libraries, and success stories.
Key Performance Indicators (KPIs) for Value
- Engagement and Fluency can be measured by the number of new AI use cases generated by employees, frequency and variety of AI tool usage, and observed improvement in prompt engineering quality.
- Productivity and Efficiency include time saved on specific tasks, reduction in errors, acceleration of decision-making processes, and faster project completion times.
- Quality and Innovation metrics include improvement in output quality (e.g., marketing copy, code snippets), increase in innovative solutions developed with AI support, and enhanced customer satisfaction scores.
- Behavioral Change can be seen in documented shifts in ways of working, proactive sharing of AI insights, and employees coaching peers on AI tools.
Review Cadence
- Implement Weekly or Bi-weekly team-level AI sprints for hands-on experimentation, peer reviews, and troubleshooting.
Conduct Monthly departmental leadership reviews focusing on progress against specific value pools, identifying friction points, and real-time course corrections.
Hold Quarterly executive leadership summits to assess overall AI transformation, strategic alignment, and resource allocation, ensuring the AI strategy remains intertwined with business strategy.
Frequently Asked Questions
How do organizations typically fall short in realizing AI’s value?
Many organizations focus too heavily on merely adopting AI tools rather than fundamentally investing in the human capabilities—skills, mindsets, and behaviors—needed to integrate AI effectively into workflows and leadership.
What is the difference between traditional training and effective AI capability building?
Traditional training often stops at imparting knowledge (Foundational stage).
Effective AI capability building moves beyond this to include applied, on-the-job practice and embedded new habits into daily operations and organizational structures.
This aligns with the learning progression framework discussed earlier.
How can leaders effectively champion AI transformation?
Leaders must share a unified vision for AI, visibly model desired AI-enabled behaviors, and create the conditions for AI to scale.
This requires technical fluency paired with strong change management skills.
What is the best way to build employee trust in AI?
Building trust involves engaging head-on with skepticism, upskilling managers to coach with empathy, providing safe environments for experimentation, and clearly communicating the business value and benefits of AI to individuals.
What metrics truly indicate AI transformation success?
Success metrics should extend beyond tool adoption rates to focus on tangible business outcomes, such as time saved, errors reduced, decisions accelerated, and the generation of new AI-driven use cases, indicating a deeper capability shift.
The Human Core of AI Value
Priya now approaches her AI dashboard with a different lens.
She understands it is not just a tool; it is an invitation to a new way of working.
Her team, once tentative, now shares prompts, celebrates small wins, and collectively brainstorms how AI can elevate their creativity, not just automate tasks.
The hum of the office still signifies busy work, but now, there is a different energy—a spark of confidence, a sense of shared purpose.
The full value of AI is not hiding in lines of code or complex algorithms.
It resides, vibrantly and powerfully, in the hands, minds, and hearts of your people.
When you invest in them, truly empower them, you do not just adopt AI; you unlock a future where human ingenuity and artificial intelligence collaborate to achieve the extraordinary.
This is the moment to move beyond tools and invest in transformation.
Let us build that future, together.
References
- World Economic Forum. (2023). Future of Jobs Report 2023.
- McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier.
- Deloitte. (2023). Generative AI in the enterprise: A new era for human-machine collaboration.
- Harvard Business Review. (2023). What It Takes to Build an AI-Driven Organization.
Glossary
Generative AI (GenAI)
A type of artificial intelligence that can create new content, like text, images, or code, often based on patterns learned from vast amounts of existing data.
Value Pools
Specific areas within a business where the application of AI can generate significant and measurable benefits, such as cost savings, increased revenue, or improved customer satisfaction.
Capability-building
A holistic approach to developing skills, knowledge, mindsets, and behaviors within an organization, going beyond basic training to embed new ways of working.
Learning Progression
A structured, multi-stage approach to education, moving from foundational knowledge to applied practice and finally to embedded habits.
Role-Specific Capabilities
Tailoring learning and development programs to the unique needs, responsibilities, and challenges of different roles or archetypes within an organization.
Change Management
The process of guiding individuals, teams, and organizations through transitions, ensuring successful adoption and proficiency of new processes, tools, or strategies.
Ethical AI
The practice of developing and deploying artificial intelligence responsibly, considering fairness, transparency, privacy, and potential societal impacts.
Upskilling/Reskilling
Programs designed to teach employees new skills (upskilling) or entirely new sets of skills (reskilling) to remain relevant and effective in an evolving job market.
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