The data generation due to digitalization by the overall business ecosystem has been prominent in the last few years. On the other hand, business leaders starving to get insights even after getting deep into scattered data led my Data & AI journey. The AI vision has evolved over the last few years drastically with generative and agentic AI intervention. I can see the professional and personal life of human beings getting simplified with AI assistants. Certain roles will exist, certain roles will be transformed, certain will not exist - this is a matter of debate. But whoever exists will be AI enabled for sure.

\The future belongs to those who use AI to amplify human judgment, creativity, and ethical decision-making, not to replace it.\

I have been instrumental in leveraging AI in document comprehension using Natural Language Processing (NLP), predictive analysis of manufacturing ecosystems (using Linear and non-linear models). The larger impact of AI will be seen in automation, personalization, content generation, cybersecurity and big data insights.

The rise in the AI ecosystem will also fuel the ethics discussions. Fairness and transparency will be demanded across the AI systems. I feel this problem should be addressed in the design of the AI system itself. The explainability and the comprehensiveness of the training data will make things simple and models unbiased.

The skillsets of the future would be divided in major two parts - skills complementing use of AI and the skills which cannot be adopted by AI. The skills which can scale faster with the help of AI will get transformed, we can see huge throughput scaling using AI. Skills like strategic, ethical thinking will be prominent skillsets of the future and we don’t see AI adopting these skills soon. The skills of mundane nature and prototype work content-based roles will be eliminated in future. Overall mankind will be pushed towards a more intelligent, emotional, ethical, innovative and artistic way of working.

The success of AI systems is in scaling. They look attractive in POC because of the limited dataset. The major shift in scaling the AI system can be achieved by continuously tuning the systems and keeping it relevant to the use case. Apart from technical, business alignment, user buy-in and communication are non-negotiable things for scaling AI systems.

“Fail fast, fail forward” is my major learning in AI and Digital transformation so far. Try, fail and eliminate one unsuccessful path. Try, succeed and find out better ways to do things. One should be always skillful in his own core skillsets like finance, marketing, design etc. and should acquire AI skills in addition. Not a single business will be left unimpacted by AI, hence it is better to be equipped.

The Journey Into Industry

Minanath Dhaske is a accomplished technology leader and continuous learner with a cross-disciplinary career spanning mechanical engineering, supply chain, IT infrastructure, information security, and digital transformation. He currently leads IT for the EV Components business at Tata AutoComp Systems, shaping the digital roadmap for the electric mobility sector. Minanath drives initiatives in Industry 4.0, SAP EWM, MES, MRP, and secure, scalable greenfield manufacturing operations. Previously, he spearheaded advanced IIoT, RPA, AR/VR, and defence-grade cybersecurity programs, delivering measurable business impact and operational excellence. Known for building future-ready teams and resilient IT foundations, he aligns technology strategy with organizational growth and innovation.