The AI Readiness Gap01
The AI
Readiness
Gap
THE 2026 ENTERPRISE
LEARNING WAKE UP CALL
The AI Readiness Gap02 The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call02
Table of
contents
03 Executive summary
04 Methodology
06 Enterprise learning: The AI Readiness Gap
13 What’s getting in the way of enterprise transformation?
22 How organizations can bridge the AI readiness gap
31 How enterprises succeed with learning and skills intelligence
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call03
Executive summary
When we started this research, we asked a simple question:
how ready are enterprise organizations for what’s coming?
The answer was clear. And uncomfortable.
Most organizations have moved fast on AI. They’ve deployed tools. Announced initiatives.
Signed contracts and stood up systems at a pace that would have seemed impossible five
years ago. On every external measure, they look ready.
They’re not.
What this research makes clear, and what I hear consistently from enterprise leaders, is
that the gap isn’t in technology access. The gap is in human capability. Organizations have
accelerated deployment without building the infrastructure their people need to keep pace.
That’s not a critique. It’s a structural reality. And it’s widening.
The people inside these organizations feel it every day. Shifting skill demands. New tools.
Accelerating change. Learning systems that weren’t built for this moment.
Here’s what I’ve come to believe: AI readiness has nothing to do with how many tools you’ve
deployed. It has everything to do with whether your people can acquire, adapt, and apply
skills as work changes around them. Skills are the real unit of readiness. Not licenses. Not
completion rates. Not headcount trained.
Without that intelligence, AI systems are operating blind. An agent without data is a Ferrari
with no fuel.
Closing this gap requires learning embedded in work, not separated from it. Skills intelligence
that makes capability visible in real time. Experiences that evolve with the individual and the
business, not once, but continuously.
The organizations that lead through this transformation won’t be the ones that moved fastest
on AI. They’ll be the ones who built the infrastructure to use it.
That’s what this research is about.
Alessio Artuffo
CEO, Docebo
The AI Readiness Gap04
01
Methodology
The AI Readiness Gap05 The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call05
Which of the following best describes your job title? (Learning leaders)
Individual Contributor (has no direct reports)
Manager (has direct reports)
Director or Senior Director
Other
%
%
%
%
Learning Program Manager / L&D Manager
Director or Senior Director of People/HR
Director or Senior Director of Learning and Development
Chief People Officer (CPO)
VP or Head of Learning and Development
Partner Enablement Leader (VP, Director, Head of)
Training Administrator / Learning Administrator
Other
VP or Head of People/HR
Sales Enablement Leader (VP, Director, Head of)
Chief Learning Officer (CLO)
Customer Education Leader (VP, Director, Head of)
Chief Human Resources Officer (CHRO)
%
%
%
11%
%
%
%
%
%
%
%
%
%
This industry-wide study surveyed 2,000 respondents at the enterprise level across the US, UK,
Canada, France, Germany, and Italy.
The 1,000 learners surveyed were currently employed in non-L&D or HR roles at or below the VP level.
The 1,000 learning leaders were defined as professionals whose responsibilities involve L&D in some
capacity, including, but not limited to, CPOs, CLOs, HR professionals, L&D admins, and leaders at the
manager level or above.
What is your current level or position? (Learners)
Enterprise learning:
The AI Readiness Gap
02
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call07
Enterprise learning has always operated at scale, supporting employees across roles, regions, and levels,
while also serving customers and partners. Given that complexity, we expected this study to surface
familiar challenges: Scalability, measurement, governance, long-standing issues in the learning industry.
Instead, we uncovered a more urgent problem underpinning them all, one that shows up most clearly in
internal learning.
Despite widespread AI adoption, there is a growing readiness gap. Organizations have ambitious
AI goals and are investing heavily in new tools and automation. Yet learners are still developing the
confidence, clarity, and contextual skills required to truly apply AI in their day-to-day work.
This is the AI Readiness Gap: The disconnect between ambition and capability.
Widespread adoption, limited transformation
AI is everywhere in learning conversations. Nearly 79% of learning leaders say they already leverage AI
for tasks such as content generation, assessments, and recommendations.
Yet adoption has not translated into transformation.
Experimental (Small-scale pilots, individual
testing, or curiosity-driven use)
Integrated (Standardized across the
department and built into daily workflows)
Transformative (Fundamentally redefining
how learning is designed and delivered)
Inactive (Not yet explored or prohibited due
to policy)
%
%
%
8%
Tactical (Use for specific efficiency gains like
content drafting or admin tasks)
%
•
Which word best describes the current maturity of AI within your organization’s
learning strategy?
of learning leaders say their
organizations have not yet
used AI to fundamentally
redefine their workflows.
91%
remain in the
experimental
%
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call08
“The pressure to reach AI fluency exists across every sector and function, but most organizations
are stuck in the ‘Surface Wave’. They have high levels of individual experimentation (people using
AI tools to complete isolated tasks), but then they return to the exact same operating workflows
they already had five years ago.
They are confusing tool access with transformation. To move into the ‘Undercurrent,’ where real,
durable value is created, organizations must move beyond individual skills and fundamentally
redesign their workflows. If you do not have clear decision rights, data transparency, and
governance models in place, your organization is not truly ready for AI.”
Organizations are stuck running pilot programs, use cases that remain isolated, leading to incremental
inefficiencies. Without systemic change, AI adoption alone will not lead to readiness.
Demand for skills, lagging capabilities
In addition to widespread adoption, there is undeniable urgency around AI in enterprise learning. For
many learning and HR leaders, it is not a future initiative, but a present pressure.
The top two challenges leaders face today are AI adoption and fluency, and skills development.
What is the biggest pressure shaping your learning strategy right now?
AI adoption and fluency
Business transformation
Tech modernization
Headcount constraints
Regulatory pressure
Other
%
%
%
%
%
%
%
Skills
Markus Bernhardt
Principal, Endeavor Intelligence
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call09
That urgency is reflected in priorities; leaders rank upskilling and reskilling, and AI fluency in their top
three learning priorities for 2026.
What are your top learning priorities for 2026?
On the surface, this signals alignment with the times. Organizations know skills matter, especially those
tied to AI. They are prioritizing accordingly.
But the data reveals a deeper tension.
Upskilling/reskilling
AI fluency
Manager capability
Onboarding
Customer/partner enablement
Compliance
Other
%
%
%
%
%
%
%
%
Leadership
Shifts in technology adoption
AI integration
Learning delivery models
Workforce transformation
%
%
%
%
%
Becoming a skills-based organization
So while leaders recognize the importance of skills and AI fluency, few anticipate the structural changes
required to truly embed skills into how learning operates.
What is the single biggest shift you expect in corporate learning over the next year?
of learning leaders expect becoming a skills-based
organization to be a major shift in corporate learning
in 2026. Very few leaders also expect AI integration
across their organization to happen anytime soon.
12%
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call10
This could be due in part to an infrastructure issue. Traditional platforms were built to track completions,
not skills. It is no surprise, then, that learning leaders rank skills mapping among the top five capabilities
they expect from their platforms.
So, they struggle to map evolving skills, integrate data from multiple systems, and connect learning to
measurable business performance. More on this later.
Adding to this disconnect between pressures felt by leaders and their expectations, there is a growing
divide between what learners hope to achieve and what their training offers them.
The number one skill prioritized by both learners and leaders in the next 12 to 18 months is AI literacy
and applied skills.
Which skills are the highest priority in the next 12-18 months?
AI literacy and AI applied skills
Critical thinking and problem solving
Leadership and people management
Innovation and creativity
Data literacy and analytics
Digital transformation skills
Sales or commercial skills
Role-specific technical or functional skills
Compliance and risk management
Customer- or partner-facing skills
Change management and adaptability
Communication and collaboration
Learners Leaders
% %
% %
%
% %
% %
%
% %
% %
% %
% %
% %
%%
%
%
of learners say the training they receive
does not help them fully understand or
use AI in their role, with 1 in 5 learners
not having received any AI training at all.
85%
本报告来源于三个皮匠报告站（）,由用户Id:619989下载,文档Id:1185577,下载日期:2026-04-10
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call11
Have you received training that helps you understand or use AI in your role?
This is the AI Readiness Gap: While the demand for AI skills is high, training is not consistently
translating into role-relevant capability.
And it’s not just AI fluency skills that are in demand.
Human skills are needed more than ever
Beyond AI fluency, the top skills currently prioritized by learning leaders are:
• Leadership and people management
• Communication and collaboration
• Critical thinking and problem solving
• Innovation and creativity
The prevalence of these human capabilities alongside AI fluency spells out an important truth: AI may
change the tools of work, but people remain the competitive advantage.
As AI reshapes how work gets done, organizations are doubling down on the human skills that
contextualize, guide, and apply it effectively.
So how do we move from AI awareness to AI readiness? From experimentation to enterprise-wide
transformation?
If urgency is high and adoption is widespread, what’s holding organizations back?
Yes, fully Yes, somewhat No, not enough No training at all
%% % %
30
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call12
“Organizations are talking a lot about AI transformation, but very few have clearly articulated
how work itself is changing or what that means for skills. That creates anxiety and insecurity.
At the same time, organizations are treating AI readiness and skill development as two separate
conversations, when in reality they are one and the same. If you’re reimagining how work gets
done with AI, you’re also redefining the skills your workforce needs.
The challenge is that most companies don’t yet have clear visibility into either side of that
equation: What skills future work requires or what skills they currently have. Ultimately, skills
transformation is data transformation.
Until organizations commit to building trusted, actionable skills data and tying it directly to real
business problems, skills will remain a side initiative instead of the engine driving transformation
throughout the organization.”
Koreen Pagano
CEO, Talent Rewire &
Co-Founder, Rising Tide Cooperative
What’s getting in
the way of enterprise
transformation?
03
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call14
Our research points to five structural barriers standing in the way of transformation:
• Lack of content relevance
• Poor personalization
• Limited integration
• Little time
• Lack of business alignment
These represent systemic friction points that prevent skills-based learning from scaling effectively.
Content without context
Learning today often lacks the targeting, support, and contextual relevance required to translate into
real performance. For starters, 66% of learners say they don’t feel fully supported by their manager or
organization to learn. Nearly 60% feel learning programs are not designed with people like them in mind.
Do you feel supported by your manager or organization when it comes to learning?
Yes Sometimes Not really Not at all
%% % %
30
Do you feel your organization’s learning programs are designed with people like
you in mind?
Yes, fully Yes, somewhat No, not enough Not at all
%% % %
30
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call15
Training is understandable but not useful. While 75% of learners say training is appropriate for their
current knowledge level, relevance to real work remains the bigger problem:
• 57% say training isn’t very relevant to their role (it doesn’t address the actual skills they need
day-to-day)
• 57% aren’t very confident that training will improve their performance, and
• Less than half (44%) very clearly understand how learning contributes to their career progression
How relevant is the learning you receive to your role or goals?
Very relevant Mostly relevant Somewhat relevant Not at all relevant
%% % %
30
Very confident Mostly confident Somewhat confident Not at all confident
%% % %
30
How confident are you that the training you receive helps you perform
better in your work?
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call16
“Most AI training today focuses on general literacy or basic tool overviews, and while that’s
a useful starting point, it isn’t concrete or operational enough to change how people actually
work. What’s missing is role-specific, use-case-driven training informed by skills data.
Unfortunately, most organizations lack a unified way to understand what skills employees
actually have or which ones they need to develop as work continues to evolve. Without that
clarity, delivering learning that’s relevant, motivating, and impactful becomes nearly impossible.”
How clearly do you understand how learning contributes to your career growth?
Very clearly Somewhat clearly Not very clearly Not at all
%% % %
30
What this means is that organizations are missing the mark on practical application and skills
development. In other words, content delivered must go beyond clarity to connecting capability.
Without context, application, and visible skills progression, learning becomes an isolated activity rather
than a driver of AI readiness and sustained performance.
This lack of content relevance goes hand in hand with personalization. When learning isn’t tailored
to a learner’s role, skill level, or real-world challenges, even the clearest content can feel disconnected
from application.
Sandra Loughlin
Chief Learning Scientist,
EPAM Systems
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call17
Personalization remains superficial
Yes, personalization is essential to content relevance, but, for many learners, that promise has yet
to materialize.
Despite widespread discussion of personalization, it remains underdeveloped in practice.
• 79% of learners report their learning experience is not fully personalized.
• 63% of learning leaders say their organization does not fully personalize learning today.
• 49% of leaders cite limited personalization or customization as a technology obstacle.
Do the learning paths you receive feel personalized to your needs?
How would you rate your organization’s ability to personalize learning today?
Somewhat
personalized
Not very
personalized
Fully
personalized
Not personalized
at all
%% % %
30
Partially Fully Not yet No Unsure
%%
%
% %
30
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call18
Learning and HR leaders rank personalized learning at scale among the top four platform capabilities,
yet many systems cannot dynamically adjust based on skills, performance data, or career trajectories.
Without fully personalized learning experiences, AI training raises awareness but fails to build
confidence. And even when content is tailored, it loses impact if it remains disconnected from the
systems and tools where work actually happens.
Learning sits outside the flow of work
Limited integration ranks as the second biggest frustration among learners: 78% report they don’t often
receive learning inside the tools they already use for work. More than a third of learning leaders agree.
What frustrates you most about the learning tools you used?
What obstacles do you face with your learning technology infrastructure?
Limited personalization or customization
Lack of automation
Outdated user experience
Inadequate reporting
Other
Not scalable enough
%
%
%
%
%
%
%
Poor integrations
Not personalized
Outdated content
Hard to navigate
Slow or unreliable
Other
Poor search
%
%
%
%
%
%
%
Limited integration with other tools
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call19
When learning is forced outside the flow of work, it competes with productivity rather than enabling it.
AI capability cannot grow if learning is disconnected from daily workflows.
Both learners and leaders have little time on their hands
Time pressures compound these issues.
• 56% of learners say they do not have enough time during the day to complete learning.
• 64% of leaders struggle to find time to deliver it.
What is the biggest barrier that keeps you from completing learning?
Do you receive training inside the tools you already use?
Sometimes Rarely Often Never
%% % %
30
Not enough time
Not relevant
No support or coaching
Hard to find
Other
Too much information
%
%
%
%
%
%
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call20
Time
Technology limitations
Headcount
Governance
Other
Stakeholder buy-in
Budget
%
%
%
%
%
%
%
What are the biggest obstacles to delivering effective learning at scale?
Beyond a scheduling issue, time represents a design issue. When learning is embedded into work
systems, aligned to real tasks, and personalized to immediate skill gaps, it becomes part of performance
rather than an additional burden.
But for that integration to materialize, leaders must elevate learning from an initiative to a strategic
priority. Without business alignment and executive commitment, learning remains peripheral. Leaders
recognize this as one of the most significant barriers to impact.
Weak business alignment undermines credibility
Less than a quarter of organizations report that learning is fully aligned with business strategy.
As a result, more than one in five learning leaders struggle with stakeholder buy-in.
How aligned is your learning team’s strategy to your organization’s business strategy?
Mostly aligned Somewhat aligned Fully aligned Not at all aligned
%% % %
30
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call21
When learning outcomes are not tied to the skills that matter most to the business, impact becomes
difficult to measure and investment becomes harder to justify.
These constraints keep learning disconnected from real work, slowing skill application and limiting
organizations’ ability to adapt at the pace AI demands.
So how do you begin closing this AI readiness gap?
“Organizations aren’t struggling with AI because they lack data. They’re struggling because
they don’t use it. They already have rich data about people, roles, and business priorities, but
learning is still designed in isolation and measured by activity rather than outcomes. As a result,
personalization remains superficial, learning stays disconnected from real work, and time and
alignment become constant challenges. Until organizations use performance and business data
to continuously inform and improve learning, AI adoption won’t translate into real readiness.”
Derek Mitchell
CEO, Gallus Insight
How organizations
can bridge the AI
readiness gap
04
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call23
Though simpler said than done, bridging the AI Readiness Gap requires structure. Our findings point to
two essentials:
1. A modern learning system, defined by how skill-informed learning is designed, governed, and
aligned to the business
2. The right technology to deliver, measure, personalize, and scale that system
Without both a clear operating model and the tools to execute it, enterprise AI readiness will remain out
of reach.
And critically, the skill-informed learning system must continuously sense, develop, and validate skills.
AI readiness cannot be achieved through static programs. It must be built through feedback loops, real-
world application, and visible skills progression.
This starts by placing learner feedback at the center of learning design.
Listening to learners: Personalization and coaching
at scale
Learner confidence is not built through exposure alone. It grows through feedback, coaching, and
experiences that feel directly relevant to real work.
Yet consistent support remains uneven. Close to half of learners (49%) say feedback or coaching is only
occasionally provided. While 46% of learning leaders already offer coaching or mentoring, scaling that
support equitably across the enterprise is difficult without AI.
How often do you receive feedback or coaching related to your learning?
Occasionally Rarely Frequently Never
%% %
%
30
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call24
Which learning modalities do you currently use to deliver learning programs?
How are you planning to use AI in 2026?
Coaching or mentoring
On-the-job or experiential learning
Live virtual workshops or webinars
Self-paced eLearning
Performance support or knowledge bases
Custom-built learning content
Off-the-shelf learning content
Instructor-led training (virtual)
Community or peer-based learning
Microlearning or short-form content
Instructor-led training (in person)
%
%
%
%
%
%
%
%
%
%
%
Analytics and insights
Chatbots
Personalized recommendations
Content authoring
Reducing manual work
Virtual coaching
%
%
%
%
%
%
That is beginning to change: 45% of leaders plan to use AI to provide virtual coaching in 2026, and 42%
want to use AI for personalized recommendations.
This shift is critical. As we saw earlier, 79% of learners say their experience is not fully personalized,
and 63% of leaders acknowledge the same gap.
When powered by learner data, AI can scale personalization and coaching in ways that were previously
impossible. By analyzing skills profiles, role requirements, performance signals, and engagement
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call25
“Learners have consistently asked for more coaching and more personalized experiences.
The barrier hasn’t been awareness or intent, it’s been scale. Coaching only scaled by adding more
humans, which wasn’t realistic for most teams. Personalization required systems sophisticated
enough to recognize and respond to individual needs. AI now gives us the opportunity to scale
both coaching and personalization in ways that weren’t previously possible. But harnessing
that opportunity requires more than adopting new tools; it demands that L&D rethink its design
mindset and processes to expand its view of what’s possible.”
patterns, AI can tailor recommendations, surface targeted practice opportunities, and provide
contextual guidance in the moment of need. Instead of delivering one-size-fits-all content, learning
adapts dynamically to each individual’s skill gaps and growth goals.
But it’s important to remember that closing the AI Readiness Gap starts with listening to learners and
acting on what they need to build confidence and competence. Without continuous feedback and
scalable coaching, the gap will only widen.
David Kelly
SVP of Strategy & Transformation,
Bluewater
To sustain personalization and continuously respond to learner feedback, organizations must
strengthen how they evaluate and measure learning. Analytics are what close the loop between
listening and action.
Measuring what actually matters
If AI readiness depends on performance, then measurement must extend beyond participation.
Our findings show that many leaders are incorporating performance indicators into their measurement
strategies: 55% report using metrics like sales, quality, or productivity.
However, many organizations continue to track completion rates (44%), behavior change or skill
adoption (51%), and employee engagement or NPS (50%), which spell learning progress but do not
immediately predict business progress.
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call26
Which outcomes do you track to understand the impact of learning?
This might explain why fewer than half of learning leaders feel very confident in connecting
learning to business results. Without that confidence, justifying investment and sustaining
momentum becomes difficult.
How confident are you in your ability to connect learning to business results?
Performance metrics (sales, quality, productivity)
Employee engagement or NPS
Learner completion survey
Customer satisfaction or NPS
Customer churn
Product adoption
Retention or internal mobility
Other
Time to first sale for partners
Behavior change or skill adoption
%
%
%
%
%
%
%
%
%
%
Somewhat confident
Neutral
Very confident
Not very confident
%
%
%
%
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call27
”Learning leaders often struggle to bridge the gap between learning and business impact because
they approach the problem with a learning mindset rather than a performance mindset. Impact
should be measured using business metrics, not learning metrics. In the age of AI, this shift
becomes even more critical. AI will reshape roles, augment performance, and redefine work itself.
L&D must therefore focus on the critical tasks that drive business success, align closely with
stakeholders, and measure how learning changes performance at scale. Without tying learning
directly to organizational objectives and using meaningful business data to demonstrate impact,
AI readiness will remain an aspiration rather than an outcome.”
And as we saw before, leaders don’t feel that learning is fully aligned with the business, and 22%
experience challenges getting approval from stakeholders.
This data problem is a skills problem too. When organizations lack visibility into skills progression
and performance impact, learning remains disconnected from business outcomes.
But when learning is tied directly to retention, mobility, productivity, and performance, it earns
credibility. Stakeholder buy-in increases. Learning shifts from a support function to a strategic driver.
Measuring what matters transforms learning into an engine for AI readiness by linking skills
development to tangible outcomes.
Leaning on skills intelligence
AI fluency is both a pressure and priority for learning leaders, but urgency alone does not create agility.
Many organizations still rely on static frameworks to catalog skills at a moment in time. These models
struggle to keep pace with the speed at which roles, tools, and technologies are evolving.
Skills intelligence offers a fundamentally different approach.
Charles Jennings
Director, Duntroon Consultants
& Co-founder, 70:20:10 Institute
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call28
This is important because we’ve found that learners are driven more by opportunity than obligation:
Rather than codifying today’s skills, it creates a dynamic, data-driven system that continuously:
• Detects emerging capabilities
• Identifies skill gaps
• Delivers targeted development
• Validates progress
• Refines and repeats as needs evolve
And in this model, tools are not separate from the system. They work together. AI-driven platforms
make skills visible, measurable, and actionable at scale. They transform learning from a static catalog
into an adaptive engine that continuously aligns workforce capability with business strategy.
This transforms learning from a one-time intervention into a continuous improvement engine, one that
enables visibility into skills progression and makes development purposeful and motivating.
are motivated by job
%
cite career growth as
their main motivation
for completing training,
compared to
40%
• Standardize architecture
• Assess continuously
• Unlock career growth
• Personalize at scale
• Deliver anywhere
• Coach, certify & adapt
• Validate skills & impact
• Measure to outcomes
• Recommend action
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call29
What motivates you to complete learning?
Industry research validates this: 94%* of employees say they would stay longer at a company that
invests in their learning.
When skills intelligence makes growth pathways transparent, development feels less like compliance
and more like progress.
For organizations, the benefits are equally strategic. Skills intelligence enables more relevant
personalization, better workforce planning, and more fluid talent mobility. Gartner reports that
companies using skills intelligence platforms see a 30% increase in internal moves, reducing external
hiring costs and accelerating workforce agility.
Meaning, skills intelligence does more than improve learning. It strengthens retention, mobility, and
organizational resilience.
Ultimately, skills intelligence shifts learning from reactive programming to proactive AI readiness-
building, a continuous system that adapts as fast as the future demands.
Career growth
Required for my job
Staying up-to-date
Recognition, badges, or incentives
Other
%
%
%
%
%
%
Improving performance
“Without a dynamic view of skills, organizations are flying blind. Skills intelligence gives you
that visibility. It allows you to detect change as it happens, align learning to real business needs,
and validate progress over time. When AI powers that system, learning stops being a catalog
of courses and becomes a living engine for capability.”
Loïc Michel
Co-Founder & CEO, 365Talents
*LinkedIn Learning, 2024.
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call30
One connected learning ecosystem drives
workforce readiness
It is important to note that while the AI Readiness Gap identified in this study appears most clearly
among internal audiences, the underlying challenges (poor personalization, limited integration, weak
business alignment) affect external audiences as well.
Customers, partners, sellers, and members feel the friction when learning is generic, disconnected from
daily work, or difficult to access.
Enterprise learning cannot be approached in silos.
Organizations that succeed think about learning as one connected ecosystem. A system that supports
different audiences in different ways, but is built on a shared foundation of personalization, seamless
access in the flow of work, and measurable capability development.
The examples that follow demonstrate how enterprises are doing exactly that: Leveraging a unified
learning environment to deliver integrated, personalized experiences across audiences, and using skills
intelligence internally to drive personalization, talent development, and AI readiness at scale.
How enterprises
succeed with
learning and skills
intelligence
05
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call32
By no means an easy endeavor, the path forward is clear: AI readiness requires personalization at scale,
integration into the flow of work, meaningful measurement, and skills intelligence that continuously
adapts to change.
But what does that look like in practice?
The following organizations, Insurity, , and SNCF, demonstrate what happens when
learning systems are intentionally designed for continuous change, not just to deliver content, but to
build capability across internal and external audiences alike. By solving for personalization, integration,
and data, they moved beyond fragmented learning programs toward unified ecosystems that support
employees, customers, and partners simultaneously.
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call33
Insurity: Scaling employee, customer, and
partner education with a unified platform
Insurity, a leading provider of insurance software solutions for the property and casualty (P&C)
industry, supports employees, customers, and partners across the entire policy lifecycle. As the
company grew, so did the complexity of its learning needs. Training was costly, time-intensive,
and heavily reliant on in-person delivery, limiting scalability.
To modernize its approach, Insurity adopted a single learning platform to centralize learning
across audiences. This unified model enabled the organization to:
• Personalize the learning experience through role-based learning paths with branching logic
to guide employees, customers, and partners to relevant content,
• Automate compliance and certification tracking by region to meet regulatory
requirements, and
• Enable learning in the flow of work via integration of third-party content to expand offerings
like product onboarding for customers, and product certifications for partners, without
increasing development costs.
The results were significant:
• A 50+ point increase in employee NPS
• 90% of employees reporting access to learning and development opportunities
• A 30× increase in active users, growing from 50 to 1,500
• Improved customer and partner readiness through standardized training
• New revenue streams through paid certifications
• Reduced training costs with improved scalability
Insurity’s success underscores a key insight: When learning is centralized, personalized,
and integrated, it becomes a growth engine for the entire extended enterprise.
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call34
: Reclaiming time to build
a culture of learning
, one of the world’s largest travel platforms, had a learning operations team
stuck in the wrong job. Their legacy LMS was so limited that new ideas never made it off the
whiteboard. Instead of designing learning, the team spent their days writing manual emails,
creating Google Calendar invites, and duplicating work across systems for a workforce of over
15,000 employees.
They rebuilt their learning operation from the ground up. Their approach included:
• Personalized homepages and role-specific course catalogs to surface relevant
content immediately,
• Automated notifications for learners and managers, eliminating manual outreach at scale,
• Google Calendar integration to auto-generate event invites for all courses and workshops,
• HRIS integration to automate user creation, deactivation, and profile updates as employees
join or leave, and
• Enrollment rules to trigger onboarding automatically for every new hire from day one.
The impact was measurable:
• 800+ hours of learning admin time saved annually
• Complex program administration reduced by up to 80%
• Learning programs increased by 30% with no added operational burden
• Subject matter expert-led training scaled by 40%
• Instructor-led training offerings grew from 20 to 120
The biggest unlock wasn’t efficiency, it was ambition. With administrative work off their
plates, the learning team moved from order-takers to strategic partners, consulting on
leadership conversations and designing innovative learning solutions they previously
couldn’t imagine pursuing.
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call35
SNCF: Building a skills-driven HR ecosystem
SNCF illustrates how skills intelligence can transform internal talent strategy.
Historically, SNCF operated within a statutory employment model with fixed career paths. But
as the organization transitioned to a more market-driven structure, flexibility became essential.
Career mobility increased, roles evolved, and leadership needed visibility into workforce skills at
scale.
They began by asking themselves, how could they manage talent without a clear picture
of existing capabilities?
Their solution was systems integration. SNCF unified recruitment, learning, mobility, and
performance processes within a single HR ecosystem, powered by AI-driven skills intelligence
through 365Talents.
This enabled them to:
• Map workforce skills dynamically
• Surface personalized recommendations for roles, projects, and training
• Provide employees with transparent career pathways
Within six months, they achieved the following:
• 83% of 155,000 employees adopted the platform
• Over 30,000 positive feedback entries were recorded
• 2,000+ inter-company transfers occurred annually
• €100 million in savings were achieved through improved internal mobility
SNCF’s experience illustrates the full realization of skills intelligence: Not just personalized
learning, but a connected talent ecosystem where skills inform workforce strategy in real time.
And while the AI Readiness Gap is most visible internally, the same barriers of weak
personalization, limited integration, and poor alignment affect external audiences as well. The
organizations featured here demonstrate that when learning systems are unified, measurable,
and skills-driven, they strengthen both workforce capability and customer impact.
The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call36
The AI Readiness Gap: Where do you stand?
Across both learners and leaders, the message from this research is clear: Skills and AI readiness aren’t
separate conversations. Skills sit at the center of AI readiness, workforce growth, and organizational
performance. But prioritizing skills is not enough.
They must be visible, measurable, and connected to the work that drives business outcomes.
Organizations that succeed with AI will redesign learning systems for continuous change, systems that
listen to learners, personalize development at scale, integrate learning into the flow of work, and use
skills intelligence to guide workforce decisions in real time.
But how do you get there? And where do you stand right now?
Recently, we developed the Learning Performance Index, a tool to help you assess your organization’s
learning maturity and readiness for the future, with recommended steps to take.
Discover your organization’s readiness for the AI-driven future with the Learning Performance Index,
and take the first step toward turning AI ambition into measurable capability.
_l2myehz3ti3w
_toq2fpx3hsmd
_nv08rz2kr0bo
_db0gsu7r6xj2
_unglbhkmvj82
_i8vqjo1xerep
_yjbcoafx8kly
_w4rnjnf7exyj
_sc7sl8qwzd56
_u5jkjkmwar2b
_ji31rvaqbagm
_ttur2ieosdal
_r35dr14k9n0i
_5ycfzdmm96rt
_r69kovrp4oz8
_q0t3psgmk0fc
