The trajectory of enterprises and their operations has fundamentally altered with the rapid advancements in artificial intelligence (AI). Today, we see a significant rise in data-driven decision-making at the core of enterprise operations. The advent of AI algorithms especially has made it simpler to scrutinize myriad datasets that provide evidence-based insights into customer behavior, economic trends, and market dynamics.
With widespread public adoption of generative AI (GenAI) applications today, foundation models like ChatGPT will only get more sophisticated with time, leading to more shifts in creative and business outcomes. Enterprises now and in the future will have to increasingly rely on intelligent automation and decision-making systems to streamline processes, enhance productivity, and optimize resource allocation.
Refining the Human-AI Collaboration within Enterprises
Let’s be clear. A technology as complex and vast as AI cannot stand on its own without humans in the loop. From an enterprise adoption and usage perspective, we will be responsible for AI’s governance, especially in terms of the decisions that are taken by using AI-enabled tools. This would mean refining the decision-making structure within the organization with business experts assessing and improving the data and insights that are generated by these tools.
As we evolve the regulatory and ethical standards of use, AI and its applications will become an essential partner for us, collaborating across various functions and processes. This is a pivotal point in enterprise history, wherein we can communicate with technology using our language, customizing our context and intent differently each time for the applications to independently generate different creative outcomes. These AI abilities are far-reaching and can be tailored across enterprises to elevate workplace culture, customer experiences, business models and strategies, and more.
For example, if we explore the impact of AI and machine learning (ML) in the manufacturing industry, the use of such technologies has led to improved product design and quality, increased worker safety, reduced unplanned downtime, and accelerated transition times. AI tools can perform predictive maintenance to proactively detect problems in production. There’s also been a significant increase in productivity with the automation of repetitive tasks. Similarly, we see measurable progress in the healthcare and life sciences sectors with AI enhancing the drug development process, automating image analysis in radiology that enable faster, accurate diagnosis, enabling remote diagnostics, and redefining access to medical facilities. And these benefits will only broaden as enterprises across industries continue to explore AI’s potential.
Augmenting the Workforce of the Future
There are some misconceptions around how AI and its evolution will impact the current and future workforces in different sectors and industries. Most technological advancements do not lead to the extinction of human labor. Rather, it enables us to expand and create new roles to drive further innovation. AI is positioned to do the same as more enterprises explore and understand the need for new abilities and skillsets to train their workforce. Most new-age tech solutions and use cases will always need human operators to enable the desired outcome.
In the long run, AI and Big Data will drive new job creation with roles that require more data scientists and business intelligence analysts. The focus for the emerging workforce would be more on honing their creative and analytical skills rather than investing time learning manual, repetitive (entry-level) tasks. AI is positioned to enhance and not replace human agents within the workforce, giving us the scope and ability to expand how we apply our intelligence alongside this tech across different business scenarios. Enterprises regularly investing in skilling and up-skilling programs will be able to properly embrace this technology’s full potential—we are already seeing massive improvements in productivity and quality of output.
As AI evolves, we are set to witness further revolution in enterprise operations as data-driven decision-making becomes simplified, and human skills like leadership, management, and creativity are further augmented.
About the Author
Vikram Nair
President - EMEA Business, Tech Mahindra
Vikram Nair, President, EMEA at Tech Mahindra, is an accomplished business leader with over 25 years of global experience in the industry. An astute and inspiring leader, Vikram played a significant role in Tech Mahindra’s growth over the past decade. Based in London, he takes active part in various global leadership communities including the G100, and is a member of the Mahindra Group delegation at the World Economic Forum where he regularly participates in the forum’s strategic partnership initiatives, including the annual meeting in Davos.
