AI is no longer simply automating tasks. It is reshaping the landscape of the workforce. The key divide is not between humans and machines, but between workers skilled in using AI and those who are not.
The exponential growth of AI has created a sharp mismatch between demand and supply of talent. With only a limited pool of AI experts, this is a period of collective learning — enterprises are still evaluating ROI from GenAI investments, tech-native companies are showcasing use cases to other industries, and existing resources using AI to become more productive.
In such a landscape, it becomes critical that enterprises allocate a significant investment towards building AI literacy within the organization. LatentView Analytics, a leading analytics and AI service provider, believes that the need of the hour is to take a proactive role in building a collaborative workforce where human ingenuity and AI capabilities reinforce each other.
“In the future, you’re not going to be replaced by AI, but by someone who knows how to use AI,” said LatentView Analytics CEO, Rajan Sethuraman.
The Skills Gap
For the company’s CHRO, Remadevi Thottathil, the shift is already visible in hiring. As routine tasks are getting automated, companies are now valuing skills like critical thinking, problem solving, and AI literacy more than ever. “As much as we discuss technology, what is more needed is creative thinking,” she said.
In her view, the future employee will need to cut through data with analytical skills while also using imagination to prompt AI effectively. Humanities, psychology, and behavioural sciences are regaining importance. Thottathil explained that to train a black-and-white system to think like a human, people must coach it toward more human-like behaviour.
The market is already rewarding the combination of technical and human skills needed to succeed in the AI-powered workforce, shows data from Lightcast, a labour market analytics firm.
Roles requiring high-end AI skills pay nearly $18,000 more than similar jobs without them, and demand has spread far beyond IT. Since 2022, non-tech sectors have seen an 800% rise in generative AI roles, according to Lightcast.
India’s Advantage
Despite global pressure on services firms to move beyond headcount-driven growth, Thottathil sees India holding an edge. “We are still the most adaptive workforce ever,” she said, comparing it to the typist-to-computer shift. “Typists who refused to learn computers went extinct. That’s how it will play out again.”
But adaptability alone isn’t enough. Hiring and learning systems are changing to keep pace with AI’s rapid evolution. Screening resumes, for instance, now risks errors if candidates don’t frame their applications in AI-readable ways.
“If your resume is not AI-readable, you may miss out,” Thottathil warned. Recruiters, too, need prompt engineering skills to prevent AI screening tools from making costly mistakes.
Rethinking Learning and Development
The bigger challenge is training existing employees. Traditional annual training calendars are obsolete. Learning has to be hyper-personalised and embedded into workflows, with AI prompting employees in real time to take advanced courses as they work.
LatentView Analytics has already redesigned its learning strategy around this. Its Analytics Bootcamp’s AI-powered onboarding module first conducts a diagnostic test in SQL, Python, Excel, and statistics. AI adapts questions based on responses, maps proficiency levels, and builds personalised curricula.
Specialised roles get even deeper pre-assessments to evaluate readiness for targeted training programs—only those who clear advanced SQL, for example, move forward, while others are given tailored recommendations to close gaps, ensuring they are prepared for future opportunities.
Beyond onboarding, LatentView Analytics uses AI-driven platforms to design individualised learning paths based on skill levels, interests, and career goals. Employees learn at their own pace, focusing only on what matters to them.
AI-Powered Simulations
For first-time managers, LatentView Analytics runs a blended program called Rise. It combines virtual and in-person training with AI-powered simulations, enabling managers to practice real-world scenarios such as giving feedback or handling conflicts. AI provides instant feedback, letting them build confidence before applying the skills on the job.
The company also uses AI simulations to train employees in leadership and communication. These gamified environments let them experiment and fail safely while receiving real-time feedback.
In technical areas, LatentView Analytics reached Databricks Elite Status by upskilling over 200 employees in just three months, with nearly a third of them leading major projects that boosted client acquisition by 15%.
Building GCCs with AI Sensitivity
The company also helps Fortune 500 firms set up global capability centres (GCCs), where the challenge extends beyond technical skills to cultural nuance. “Different countries, different cultural backgrounds, different sensitivities. These are more than technology. They are human nuances,” Thottathil said, underscoring how AI literacy now cuts across HR, finance, IT, and even admin. “Without AI literacy, it’s going to be tough for any department to function.”
Sethuraman frames this as LatentView’s edge. “If a Fortune 500 company needs to build a complete AI-literate team, it has to start from scratch. We are a few steps ahead,” he said.
Sethuraman observes clear productivity improvements already. “If it took 100 units of effort three years ago, today it’s happening with only 60 or 70,” he said. Coding, in particular, has experienced a significant impact.
But the CEO also warns against overreliance on probabilistic models. “Not all real-world problems are probabilistic. Enterprises are often worried that you are bringing a probabilistic approach to a deterministic problem.”
To address this, companies are adding contextual layers like RAG models and smaller reasoning systems to reduce hallucinations.
The Three Layers of Training
LatentView Analytics structures its AI upskilling into three buckets.
First, boosting personal productivity with tools like LLMs. Second, training on client-facing problem-solving and AI architecture. Third, navigating the flood of AI options across data layers, models, and agents.
Much of this experimentation and R&D feeds into its AI Centre of Excellence, which the company set up to consolidate learnings. Both leaders are wary of certifications that don’t match real-world work.
Thottathil calls most of them “acknowledgements” rather than proof of capability, while Sethuraman points out that certifications are often tied to specific platforms. LatentView instead focuses on curated, relevant programmes that empower its own teams to help clients optimise returns on their AI investments
The message is blunt. Companies that don’t invest in AI literacy risk being left behind. The workforce divide will not be between humans and machines, but between those who adapt and those who don’t.
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