Mayank Kumar of BorderPlus discusses how workforce mobility is moving beyond traditional recruitment and how AI is accelerating frontline workforce readiness at scale.
As aging economies face talent shortages and AI transforms work processes, new companies are emerging to reinvent cross-border workforce mobility.
Mumbai-based BorderPlus is focused on this mission. Beginning with healthcare, the company goes beyond traditional recruitment by preparing, deploying, and supporting skilled professionals, helping global employers address workforce gaps at scale and connecting blue-collar workers with global opportunities.
Mayank Kumar, who also co-founded upGrad, and Ayush Mathur, former President at OYO Europe, started the company in 2025. Right now, the platform mainly helps healthcare workers move to Europe, with a focus on Germany.
In a recent interaction with CIO&Leader, Mayank shares why workforce mobility is now more important than just recruitment, how AI helps frontline workers get job-ready faster, and why future enterprise AI will focus more on services, compliance, and getting things done rather than just products.
CIO&Leader: At a time when recruitment is increasingly commoditized, what organizational inefficiencies are you fundamentally trying to solve?
Mayank Kumar: The core inefficiency is fragmentation. Today’s global workforce mobility ecosystem operates through disconnected intermediaries: sourcing agents, language institutes, recruiters, visa facilitators, and employers, all of whom function independently.
This fragmentation makes it hard to predict and be accountable. For instance, a hospital in Germany might need 15 nurses by a certain date, but if no one manages the entire process, it is hard to reliably meet that goal.
We think the real value in workforce mobility is making sure workers are ready, not just hired. We find candidates, check their credentials, train them in language and culture, get them ready for the job, send them abroad, and help them settle in. This approach is a lot like how Indian IT companies built large talent pools using centralized training programs.
CIO&Leader: How are you using AI to solve the difficulties of scaling German language training and supporting nurses once they’re working in Germany?
Mayank Kumar: AI lets us personalize training and support in ways that were not possible before.
When it comes to language learning, it is hard to find enough good German teachers worldwide, especially for healthcare. Right now, we have only 35 to 40 German teachers, and training new ones takes a lot of time and effort.
To solve this problem, we created an AI-powered learning system with three parts: Learn, Ask, and Do.
Learn
The ‘Learn’ part helps nurses build up their language skills and get ready for real work tasks step by step.
- Words: After each class, nurses practice vocabulary through AI-driven exercises that offer real-time pronunciation feedback, similar to Duolingo.
- Sentences: As proficiency improves, learners move from isolated words to conversational sentence patterns.
- Clinical scenarios: Once they reach advanced levels, nurses take part in 5-to-10-minute practice conversations based on real hospital situations. We created these scenarios after talking to over 100 nurses in German hospitals to learn what a typical shift is like.
The results are impressive. Nurses who go for interviews almost always pass because they have already practiced real-life situations many times.
Even more important, candidates are not just memorizing words. They are learning how to care for dementia patients, handle medication refusals, manage emergencies, fill out insurance paperwork, and talk with patients in the German healthcare system.
Ask: The second layer addresses operational confidence. Migrant nurses commonly struggle not because of their skills and knowledge, but because they lack contextualized familiarity and hesitate to ask questions.
We built an AI support system that they can use both in India and once they arrive in Germany. Nurses can ask practical workflow questions such as:
- “My patient is refusing medication—what should I do?”
- “I noticed a yellow patch on a patient’s skin—what is the protocol?”
- “A patient wants discharge approval, but I do not have the authority to approve it.”
The system answers using rules based on Germany’s SGB social code, MDK guidelines, and local insurance rules. In many ways, it acts like an AI assistant that helps nurses follow the right steps and protocols.
Do: The third layer focuses on documentation automation. Clinical documentation in Germany is highly compliance-intensive, and written German is often more difficult for migrant nurses than spoken communication.
To solve this, we built an AI-powered scribing platform. Nurses speak naturally into the system, which transcribes the interaction into structured clinical documentation. The platform then does the compliance and insurance validation, flagging anything that needs to be added, removed, or rephrased.
This matters because in Germany, every clinical document gets a quality score from regulators. Hospitals are often unsure about hiring new migrant nurses because they worry about mistakes in records and following the rules. But with our system, our nurses regularly get above-average scores for their documentation.
So, AI is not taking jobs away here. Instead, it helps workers get ready faster, reduces problems in daily work, boosts confidence, and makes it easier to be productive on a large scale.
CIO&Leader: Staffing businesses have historically struggled to build sustainable margins. What creates defensibility in your model?
Mayank Kumar: Conventional staffing businesses aggregate supply; they don’t create capability. That’s why they remain transactional. Our defensibility comes from controlling workforce preparation itself. For example, a nurse may already possess a clinical degree, but succeeding in Germany requires language proficiency, understanding of the local workflow, adherence to documentation standards, and cultural adaptation.
We own that transformation layer.
Another advantage is predictability. Across the industry, very few people pass the German B2 certification on their first try. Our success rates are much higher because we have built training systems that focus on real results, not just teaching.
The third advantage is our knowledge of how work actually happens. As we get more involved in operations in other countries, we develop our own understanding of real clinical processes and compliance systems.
CIO&Leader: Enterprise CIOs today are struggling to move their AI initiatives from pilots into production. From your perspective, why do so many AI deployments fail to scale operationally?
Mayank Kumar: Because organizations underestimate workflow complexity.
Most companies still see AI as just another software tool that can be added to what they already have. But real change only happens when the way work is done is redesigned from the ground up.
Most companies still see AI as just another software tool that can be added to what they already have. But real change only happens when the way work is done is redesigned from the ground up.
Implementing AI is really about services and integration, not just about the technology itself.
That is why I think the next ten years will be very good for IT services companies. Businesses will need partners who really understand how their work gets done and can fit AI into those processes.
For us, we send our engineers into German hospitals to observe nurses at work before we set up any AI systems. Without this hands-on experience, using AI stays at a surface level.
CIO&Leader: Your AI systems operate in highly regulated healthcare environments. How do you balance automation with compliance and risk governance?
Mayank Kumar: We are very careful about setting boundaries. Our systems are built so they do not make clinical decisions. Instead, we focus on following protocols, guiding workflows, and helping with documentation.
For example, if a patient refuses medication, the AI does not recommend treatment decisions. Instead, it guides the nurse on what must be documented, how to escalate appropriately, and how to remain compliant with German healthcare regulations.
Most of our engineering work is about building trust into the system. We decide what the AI should not answer, how to check for compliance, and where people still need to be involved.
We also maintain human review across all critical workflows because trust in healthcare cannot be left entirely to AI.
CIO&Leader: Many AI startups today claim proprietary advantages. What unique data moat is BorderPlus building?
Mayank Kumar: At first, a lot of healthcare documentation was available to the public. But our real advantage comes from learning and improving through feedback from actual operations.
We have already processed thousands of real nurse conversations, workflow steps, and documents. Even more important, working nurses keep checking and rating what the system produces.
That human feedback is essential. Without experts checking the results, most AI products end up being just a thin layer over basic models.
Our real advantage is knowing the details of specific workflows and always learning from ongoing feedback.
CIO&Leader: As generative AI becomes more democratized, do you believe product differentiation itself will weaken?
Mayank Kumar: Yes, definitely. Product advantages are disappearing fast. As core AI models get better everywhere, just having access to a language model will not set you apart anymore.
The two lasting advantages will be how well you can reach customers and how deeply you understand their workflows.
Can you connect closely with customers to fit into their daily operations? And do you know their processes better than your competitors?
That is where companies can stand out.
CIO&Leader: AI systems frequently struggle with bias, data inconsistency, and contextual inaccuracies. How do you approach governance in such a sensitive domain?
Mayank Kumar: Human oversight is at the heart of what we do. We never let AI make decisions on its own in interviews, evaluations, or any process where following the rules is critical.
The truth is, many candidates have very different language skills and educational backgrounds. Human evaluators are still needed to interpret and judge each situation in context.
AI makes things more efficient, but the rules and oversight must stay focused on people, especially in regulated work settings.
CIO&Leader: Geopolitical shifts and anti-immigration sentiment continue to create uncertainty globally. How do you future-proof the business?
Mayank Kumar: It is important to diversify. We cannot rely on just one country. That is why we are growing in Germany, the GCC countries, Japan, and possibly in English-speaking places like Australia, Ireland, Canada, and the UK.
But the basic fact remains aging countries need workers from abroad. Healthcare systems cannot keep running without migrant workers.
CIO&Leader: What are the company’s strategic priorities over the next 12–18 months?
Mayank Kumar: Our main goal is to grow our work in Germany. We have bought several companies there to build stronger relationships and improve how we operate locally.
Second, we want to expand into the GCC countries and Japan, and we are also looking at other international opportunities.
In the long run, we want to show that BorderPlus is more than just a recruitment platform. We aim to be a company that provides full workforce infrastructure powered by AI.
Third, we plan to use our AI tools not just for preparing workers, but also in healthcare institutions, especially in care homes and elder-care systems.
In the long run, we want to show that BorderPlus is more than just a recruitment platform. We aim to be a company that provides full workforce infrastructure powered by AI.
CIO&Leader: Finally, if you had to place one long-term strategic bet on AI’s function in global workforce mobility, what would it be?
Mayank Kumar: The biggest impact of AI will be making the time between learning and being productive much shorter.
Global workforce mobility today remains slow, manual, and operationally fragmented. AI can dramatically accelerate language acquisition, workflow adaptation, onboarding, compliance readiness, and contextual integration.
The real opportunity is not just matching talent with jobs. It’s helping workers to become productive in foreign work settings much faster.
Interestingly, I think AI could have a bigger long-term effect on blue-collar and healthcare jobs than on traditional office work.