Artificial intelligence (AI) isn’t just a futuristic idea anymore, but a business imperative. Any organisation that looks forward to building a future-ready workforce is either seeking AI-skills-ready recruits or skilling current employees with the right set of skills to stay competitive
A recent example of this is NTT Data Corporation, which has decided to upskill its entire Indian workforce in AI, rather than lay off
A study conducted by upGrad Rekrut found that the AI exposure for the tech roles is projected to rise to 31.8% from 19.9% by next year. The study further found that organisations with efficient career development plans are likely to become 42% of the AI frontrunners.
All the industries across all levels have been talking about AI over the last few years. But do we have clarity? The study mentioned above also found that trust in AI increases by 15% when organisations provide transparency about how AI will affect work.
This clearly shows that, even though AI has made its way into the workforce, clarity about its uses and impacts on decision-making still lags. This clarity is nothing but the business goals that AI is being explored to achieve.
On this, Vipul Prakash, Founder and CEO of FireAI, said, “When outcomes are established upfront, AI can be architected to reinforce the organisation’s core priorities, whether that is operational efficiency, revenue acceleration, risk mitigation, or decision velocity.”
Achieving clarity
The first step towards achieving clarity is defining the functions for which AI is to be adopted. The upGrad study revealed that 93.4% of the respondents reported that AI improves complex and repetitive tasks, and 68% associated AI with faster task completion. This reflects that even in a single function, AI may have multiple uses.
Determining business goals beforehand improves decision quality and enhances the employee experience, helping ensure AI adoption stays tied to business outcomes rather than creating panic among the workforce.
According to Vipul Prakash, here’s what clarity helps with:
- Reduces cognitive overload by prioritising what truly matters
- Eliminates repetitive analytical effort
- Increases confidence in decision-making
- Encourages adoption through trust, not enforcement
Earlier in an interaction with ThePeoplesBoard, Karan Sandhu, Independent Operating Partner (People, Learning & AI), said, “Many companies and trainers are still beating around the bush. Once you are in a position to figure out what both internal and external stakeholders would like, you will be able to discover which areas can be amplified by AI to at least meet or exceed the business needs. Depending on these needs, learning programmes need to be designed.”
Is your organisation AI-ready?
It is very important to understand an organisation’s readiness after analysing the anticipated business outcomes.
Neha Gupta, VP HR APAC, Material, “Most organisations confuse AI readiness with tool adoption. True readiness shows up when employees stop asking ‘Will AI replace me?’ and start asking ‘How do I work better with it?’ ”AI readiness is really about how many can rightly think of how and where to use AI to support, improve, speed up or enhance their thinking.”
She further added that AI readiness is measured across three dimensions: mindset, skill, and trust. “Are employees curious or fearful? Can they ask the right questions of AI? And do they trust the organisation to use AI ethically? If you don’t map all three, you’re not measuring readiness; you are measuring noise and activity.” she said.
Microsoft suggests that before diving into AI projects, you should evaluate whether your business is prepared to support and sustain these technologies. By addressing gaps (if any), an organisation can lay a solid foundation for AI deployment, reduce the risk of unexpected challenges, and increase the likelihood of achieving long-term positive results.
As per the suggestions, the things to consider are:
- Data security: Given that AI tools work with data and information, organisations must ensure strong firewalls are in place to prevent data breaches. Measures such as data encryption, access restrictions, and frequent security audits are essential to staying compliant with regulations.
- Data availability: Reliable AI outcomes depend on the quality of data feeding the system. Organisations need to assess whether their data is accurate, consistent, and properly structured. And this isn’t restricted to structured data; it also covers unstructured sources such as emails, documents, and feedback, which can offer valuable insights.
- Infrastructure: AI adoption requires a technology backbone that can scale with demand. Businesses must ensure they have adequate cloud computing capabilities, secure data storage, and compatible software platforms. Without this foundation, handling large volumes of data or running AI models efficiently becomes a challenge.
- Talent: The most important step is certainly to ensure that an organisation has enough skilled people to deploy AI tools. Companies need professionals who understand data science, machine learning, and AI systems, or they must invest in upskilling existing teams.
- Integration opportunities: Leveraging platforms that already support AI integration can significantly reduce deployment complexity. Such tools help organisations embed AI into existing workflows more smoothly while offering built-in security, scalability, and performance features, minimising the need for extensive custom development.
Interestingly, often, the suggestions laid down by Microsoft come to organisations as challenges during the first stages of AI deployment.
But for Indian employers, an additional struggle is tackling cultural resistance to AI and the fear of job loss among senior employees. This calls for transparent communication between the management and the workforce.
On this, Neha Gupta further added, “AI adoption fails when leaders delegate it to IT instead of owning it as a people transformation. Also, resistance to AI is less about technology and more about livelihood anxiety. Training won’t solve this issue. People need reassurance backed by action and demonstrated care.”
According to Gupta, leaders must clearly state where AI will augment work, where roles will evolve, and how the organisation will invest in reskilling. If organisations roll out AI without addressing job security, power structures, and decision rights, resistance is inevitable. “You beat fear with honest leadership and visible career pathways. Dashboards don’t cut through cultural issues,” she added.
“A company that fosters trust in AI systems, empowers employees to innovate, and integrates responsible practices into its DNA creates a competitive moat that technology alone cannot provide,” Voice & AI has written in a blog.
Interestingly, Voice & AI has also written that organisations that focus on the human side of AI, rather than only on the technical infrastructure or ROI, are the ones that successfully deploy AI. Culture shapes trust, collaboration, and the willingness to innovate, all qualities that no algorithm can create, but without which no AI initiative can succeed.
Going Ahead
At this point, the conversation circles back to where it all started. AI adoption at work has surpassed the stage of ‘ifs’ or ‘buts’. It is an unwritten mandate for relevance in a competitive market. Experts are of the view that even if AI doesn’t take away jobs, people who don’t skill up with AI may lose in competition against those who do.
Previously, in an interaction with TPB, Shantiprakash Motwani, senior data and AI leader, pointed out that roles such as delivery managers, product managers, data engineers, full-stack developers and test engineers will significantly evolve with AI.
Citing such circumstances, businesses often rush to adopt AI. Also, teams with client-facing roles often feel the pressure to automate processes with AI for faster outcomes. But adopting AI under peer pressure without clarity is often suicidal
Plus, successful AI adoption is as much an organisational exercise as it is a technological one. It requires transparency with employees, strong governance around data and decision-making, and a clear understanding of where human judgment must remain central. Without this foundation, AI risks becoming another layer of complexity, adding cost and confusion rather than value.
