To keep up with a quickly changing world, more employers are focused on upskilling and reskilling their employees. But according to Raman K. Attri, founder of GetThereFaster Learning & Leadership Labs, executive coach to senior learning officers and a Fortune 500 technical learning leader, simply increasing the volume of training programs is just putting a band-aid on a larger issue.
“To compress time to proficiency, leaders must stop viewing training as the first line of defense,” he says. Instead, training needs to be treated as a “holistic ecosystem designed to nurture employee proficiency.”
In conversation with StrategicCHRO360, Attri shares the three actionable pillars that this ecosystem is built upon, and what leaders can do today to build a agile workforce.
Despite the availability of powerful AI-driven analytics, what critical performance metrics are most L&D leaders overlooking that have a direct impact on business performance?
Despite the proliferation of AI-driven analytics, most L&D leaders remain focused on traditional metrics that overlook the most critical driver of business performance. In an era defined by velocity, an organization’s market positioning and time-to-market are dictated by how effectively leadership can accelerate employee development.
Traditional HR metrics—time-to hire, talent distribution and compensation benchmarking—measure process efficiency rather than strategic impact. Similarly, L&D often defaults to “time to training” as a proxy for effectiveness, while managers track “time to activity” to gauge contribution. While these metrics populate operational dashboards, they are insufficient for the modern enterprise. They lack the strategic depth required to accelerate workforce readiness.
The most critical, yet frequently overlooked, metric is time to proficiency. This indicator directly impacts competitive positioning by measuring the duration it takes for a new hire or upskilled employee to perform consistently without supervision. If an employee is on the payroll for 12 months but only reaches full productivity by month nine, the organization is burdened by nine months of “proficiency lag.”
AI analytics should be recalibrated from tracking engagement or test scores to monitoring proficiency milestones. By leveraging these insights, leaders can baseline and compress time to proficiency, directly increasing the organization’s capacity and revenue per employee—the ultimate indicators of business health.
How does the pace of business in the current market impact the way organizations should fundamentally structure and deliver their learning programs and human resource development initiatives?
I have been involved in research across 60 best-in-class organizations which reveals a fundamental misalignment: a speed paradox. The half-life of learned skills shrunk to six months during the pandemic and has since dropped to less than three months in the wake of the AI revolution. Conversely, the time required for an employee to reach full proficiency often spans 12 to 18 months. By the time an individual is fully proficient, the skill itself may already be obsolete.
The traditional model of event-based training programs is incapable of addressing this lag. Relying on content-heavy approaches to build skills is simply too slow for today’s market. Organizations must cease structuring L&D as a content-delivery factory. Instead, the AI era demands a shift toward context-heavy approaches that build skills at speed and scale.
L&D functions must pivot from being training providers to becoming performance architects who build speed-enabling ecosystems. This requires a structural transition from siloed training departments to integrated performance labs where the objective is not learning, but workforce readiness.
We must move away from course-centric designs toward agile, granular and context-aware performance support systems that allow employees to learn while producing. The metric of success is no longer “Did they learn?” but “How fast can they perform independently?”
Amidst unprecedented pressures of high skill obsolescence and squeezed time-to-market, what approaches can L&D leaders take to accelerate the development of workforce at the speed of business?
To compress time to proficiency, leaders must stop viewing training as the first line of defense. Insights from 85 world-class leaders suggest that true acceleration requires moving beyond task-based training toward a holistic ecosystem designed to nurture employee proficiency.
This ecosystem is built upon three actionable pillars:
Define measurable proficiency. Leaders must establish verifiable metrics for every critical role. You must define exactly what “independent, reliable and consistent performance” looks like. Time to proficiency should only be recorded once an employee demonstrates the attainment of these specific metrics.
Establish a proficiency baseline. By aggregating time to proficiency data across employees, roles and departments, leaders can create a baseline to measure future development gains. AI-driven analytics can automate this tracking to provide real-time strategic visibility.
Build a speed-enabling ecosystem. The final and most strategic step is to de-emphasize formal training in favor of social connectivity among peers, structured coaching and just-in-time, on-demand performance support. By involving managers as accountable stakeholders and utilizing AI to guide tasks in real-time, organizations can ensure the workforce evolves at the same velocity as the business pivots.
With heavy cross-over of specialization across people-related functions, how should CXOs reorganize their L&D functions to accelerate the implementation of AI-driven workforce learning and performance?
The success of AI-driven learning depends on how effectively L&D leverage platforms and analytics to accelerate capability. This requires a unified ecosystem where HR policy, L&D strategy and AI tech stacks are fully aligned to eliminate friction in employee development.
To create this speed-enabling environment, CXOs must dismantle the legacy walls between HR, L&D and IT. These functions can no longer operate in isolation if the goal is a frictionless AI implementation. Future reorganizations must place technology at the center, evaluating every tool based on how it contributes to learning speed.
I recommend that leaders elevate the scope of L&D by establishing the role of a chief learning technology strategist. Positioned at the intersection of human performance science and AI infrastructure, this role is essential for driving and justifying the ROI of capital-intensive AI investments. This strategic leader ensures that technology does not just exist, but actively drives the speed of the business.
