Just-in-Time Learning Is Not Enough
BY: Hana Dhanji, Founder & CEO, Cognitrex Inc.
Why AI Is Forcing Companies to Build Workforce Readiness Infrastructure
For more than a decade, “just-in-time learning” has been one of the most widely accepted ideas in corporate training.
The premise is simple and appealing: employees should not be expected to memorize large volumes of information in advance. Instead, organizations should make knowledge accessible exactly when it is needed.
Searchable knowledge bases, digital documentation, microlearning platforms, and now AI assistants have made this approach easier than ever. Employees can retrieve policies, procedures, technical instructions, or regulatory guidance in seconds.
For many everyday tasks, this works remarkably well.
But the rapid adoption of artificial intelligence is exposing a fundamental limitation in this model.
Just-in-time learning solves the problem of information access. It does not solve the problem of organizational capability or workforce readiness.
And as AI becomes embedded in enterprise workflows, that distinction is becoming critically important.
For compliance officers, legal teams, and risk leaders, the reliance on just-in-time knowledge may create an illusion of competence that organizations are not yet equipped to manage.
Increasingly, companies are discovering that access to answers is not the same as being prepared to act responsibly with those answers.
As a result, a new priority is emerging across many industries:
workforce capability and readiness infrastructure.
The Rise of Just-in-Time Learning
Just-in-time learning emerged as organizations began to move away from traditional training models.
For decades, corporate learning relied heavily on scheduled training programs and classroom instruction. Employees attended courses, completed modules, and were expected to remember the material until it was eventually needed.
However, several structural problems made this model increasingly ineffective.
First, employees frequently forgot much of what they learned before they had an opportunity to apply it. Research on the “forgetting curve,” originally developed by psychologist Hermann Ebbinghaus, suggests that individuals may forget a significant portion of newly learned information within days if it is not reinforced through practice.
Second, business environments began to change more rapidly. Training content could become outdated before employees had the chance to apply it.
Just-in-time learning offered a solution.
Instead of front-loading knowledge, organizations could provide employees with tools that allowed them to access information on demand.
Knowledge management platforms, searchable documentation, and microlearning systems enabled workers to retrieve guidance exactly when they needed it.
The rise of generative AI has dramatically accelerated this model.
Employees can now ask AI assistants to summarize policies, generate technical explanations, draft communications, or recommend next steps in real time.
At first glance, this appears to solve the corporate learning problem entirely.
But the reality is more complicated.
AI Has Created the “Answer Economy”
Generative AI has effectively created what might be called an answer economy.
Instead of searching for documents or reviewing training materials, employees can now generate answers directly.
Ask a question.
Receive a response.
Act on the information.
This shift dramatically lowers the barrier to accessing knowledge.
But it also creates new risks.
AI systems are capable of producing highly confident responses that appear authoritative even when they are incomplete or incorrect. In many cases, employees may not have the expertise required to evaluate whether those answers are accurate.
In other words, the availability of answers may increase faster than the capability to evaluate them.
For organizations operating in regulated or high-risk environments, this is a serious governance issue.
Access to Information Is Not Capability
The fundamental limitation of just-in-time learning is that it assumes access to information is sufficient for competent action.
In many professional contexts, that assumption does not hold.
Consider a healthcare setting. A clinician may be able to access a treatment guideline instantly through a digital system. But applying that guideline correctly requires clinical judgment, contextual awareness, and familiarity with the patient’s specific circumstances.
Similarly, in financial services, a compliance officer may retrieve regulatory guidance through a knowledge platform. But interpreting how that guidance applies to a specific transaction requires training and expertise.
In these environments, knowledge retrieval is only one component of effective performance.
True capability involves:
- contextual understanding
- applied judgment
- validated skills
- operational experience
Without these elements, access to information alone may create a dangerous illusion of readiness.
The Illusion of Competence in the AI Era
Psychologists have long studied what is known as the illusion of competence — a cognitive bias in which individuals believe they understand a concept simply because explanations are easily available.
AI systems may amplify this phenomenon.
When employees receive immediate answers from AI tools, they may assume that they understand the subject matter sufficiently to act on those answers.
However, the ability to generate an explanation does not necessarily imply deep understanding.
In environments where decisions have legal, financial, or safety consequences, this gap between perceived and actual competence becomes risky.
Organizations may assume that employees are prepared to perform tasks responsibly simply because they have access to AI-generated guidance.
But access to guidance does not guarantee capability.
Why Compliance and Legal Teams Should Pay Attention
For many compliance and legal departments, AI adoption is already raising difficult questions.
Organizations are increasingly developing policies governing the use of generative AI tools. These policies often address issues such as:
- data privacy
- intellectual property protection
- responsible AI use
- confidentiality risks
However, policy frameworks alone may not be sufficient.
Compliance systems traditionally assume that employees possess the underlying expertise necessary to interpret regulations correctly.
But if employees increasingly rely on AI tools for regulatory interpretation or decision support, organizations must ensure that those employees have the capability to evaluate AI outputs critically.
Otherwise, responsibility for decisions becomes ambiguous.
If an employee follows an AI-generated recommendation that turns out to be incorrect, who is accountable?
The employee?
The manager?
The organization?
Or the technology itself?
Without systems that verify workforce capability, answering these questions becomes difficult.
The Case for Workforce Readiness Infrastructure
To manage these risks, organizations may need to rethink how they approach workforce development.
Rather than focusing primarily on delivering knowledge, companies may need systems designed to ensure workforce readiness.
Workforce readiness infrastructure focuses on verifying that employees are prepared to perform specific tasks safely, responsibly, and in compliance with relevant regulations.
This infrastructure typically includes three key components.
Capability Mapping
Organizations must first define the capabilities required for critical roles.
Capability mapping involves identifying the skills, knowledge, and competencies necessary to perform key tasks effectively.
These capability maps allow organizations to understand where readiness gaps may exist across the workforce.
Skills Validation
Once capabilities are defined, organizations must verify that employees actually possess them.
This may involve structured assessments, certifications, simulation-based training environments, or other validation mechanisms.
Validation ensures that capability is demonstrated rather than assumed.
Readiness Monitoring
Capabilities change over time.
Regulations evolve. Technologies advance. Employees change roles.
Organizations therefore require systems that monitor workforce readiness continuously.
Readiness infrastructure provides visibility into whether employees remain prepared to perform critical tasks.
Why AI Makes This Infrastructure Urgent
Artificial intelligence accelerates the need for workforce readiness infrastructure for several reasons.
First, AI dramatically expands the range of tasks employees can perform. Employees may suddenly have access to analytical tools or decision-support systems that previously required specialized training.
Second, AI-generated outputs can appear authoritative even when they contain errors or incomplete information. Employees must therefore develop strong judgment skills to evaluate those outputs.
Third, organizations must maintain clear accountability for decisions made in AI-enabled workflows.
Without systems that track workforce capability, accountability becomes difficult to assign.
As AI becomes embedded in enterprise operations, these challenges will only intensify.
Enterprise AI Adoption Is an Organizational Challenge
Much of the public conversation around AI adoption focuses on technical infrastructure.
Organizations discuss:
- model performance
- data pipelines
- cloud architecture
- software integration
These technical elements are important.
But the most difficult challenge in enterprise AI adoption may ultimately be organizational.
AI does not simply change technology systems. It changes how work is performed.
Employees must learn how to collaborate with AI tools, evaluate AI outputs, and make decisions in environments where human and machine judgment intersect.
This requires new forms of workforce capability.
The Future of Enterprise Learning
For decades, enterprise learning systems have focused primarily on distributing knowledge.
In the AI era, organizations will increasingly need systems designed to ensure capability and readiness.
These systems will integrate learning, assessment, certification, and operational performance data into a unified framework.
Instead of measuring training activity, organizations will measure workforce readiness.
This shift will move learning from a peripheral HR function to a core component of operational infrastructure.
The Strategic Implication
Just-in-time learning will continue to play an important role in supporting employees with immediate information needs.
But it cannot serve as the sole foundation for workforce capability in AI-enabled organizations.
As artificial intelligence becomes embedded into enterprise workflows, organizations must ensure that employees possess the judgment, expertise, and accountability necessary to apply knowledge responsibly.
That requires infrastructure that goes beyond content delivery.
It requires systems designed to ensure organizational capability and workforce readiness.
The companies that build this infrastructure will be better positioned to manage risk, maintain compliance, and deploy new technologies safely.
Just-in-time learning may help employees access answers.
But in the age of AI, organizations must ensure that their people are ready to use those answers wisely.
Hana Dhanji is the Founder & CEO of Cognitrex, an enterprise LearningOS platform and content design firm that helps organizations modernize learning and development.
Cognitrex works with enterprise teams to design and deliver role-based learning programs, onboarding pathways, and scalable training systems that improve workforce capability and performance. The platform combines LMS, LXP, and content infrastructure into a single system, paired with high-quality, scenario-based course design.
Hana is a former corporate lawyer at Sullivan & Cromwell and Hogan Lovells, having worked across New York, London, Dubai, and Toronto. She now advises organizations on how to move beyond fragmented training toward structured, high-impact learning systems.
She also serves as Treasurer and Chair of the Finance Committee for the UTS Alumni Association Board and as a Committee Member of the Ismaili Economic Planning Board for Toronto.
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