“AI will not replace humans, but those who use AI will replace those who don’t.” – Ginni Rometty, former CEO of IBM.
In the evolution of technology that have changed human lives, AI stands out because it uses human inputs—in the form of digital data—to act intelligently without human intervention. AI uses data to learn, adapt and take independent actions which profoundly impacts the way we live, work and play.
The difference between early automation and now is that earlier automation was rigid and linear, relying on rule-based responses. Now AI has become adaptive and can navigate complex scenarios to take decisions in a dynamic environment, thanks to its self-learning capabilities.
Trained on vast data sets, AI models can perform tasks at speed and scale to drive huge productivity and efficiency gains. AI can summarize, write codes, engage in conversations and reduce the skills barrier to facilitate problem solving. However, harnessing AI effectively is no mean feat. It requires deep understanding of the technological implications, the experience to identify where it can deliver business value; and the expertise to turn it into business-ready solutions with measurable outcomes.
NewVision has partnered with many organizations across the spectrum—large, global organizations to fast growing start-ups—helping them implement AI with real-world outcomes. Through these experiences we have gained critical insights. Here are three key learnings that will help businesses to better achieve AI outcomes with confidence and clarity.
- From Digital-first to AI-first: Organizations approaching AI implementation only as software deployment are facing challenges in extracting meaningful value. AI requires a shift in mindset from digital first to AI first, so that systems are not just AI-enabled but become AI-native. To unlock its true value, AI must be wired deeply into the core of the organization, into the workflows and the decision-making process so that systems are learning continuously and adapting by using its intelligence.
For instance, while working with a large banking customer we re-designed workflows to optimize them. One of the objectives was to decouple different channels to facilitate agentic AI to perform jobs such as updating a customer profile across channels and have a seamless view of customer engagement.
- From bottom line to top line growth: It is time to reframe the AI value proposition as a core lever to accelerate business growth by empowering teams and processes. Focusing on AI as a tool for cost-savings and productivity limits the scope to create value. AI discussions focused on the bottom line also create uncertainty as it leads to the inevitable question of reducing employees. Often there are unrealistic expectations from cost-cutting gains which are hard to realize at scale, while growth-focused use cases tend to deliver more tangible impact.
Our experience find that when AI conversations are about empowering teams and creating possibilities such as hyper-personalized marketing and dynamic pricing mechanisms, AI experiences faster uptake. Initiatives tied to top-line growth—such as customer engagement, marketing automation, or contact center scalability—face fewer barriers as these use cases do not threaten existing jobs. Instead, it expands capacity and reach. For example, AI-powered agents may have lower conversion rates than humans, but they can operate at scale with no limit on outreach volume. Even marginal gains across a larger funnel contribute meaningfully to revenue.
- From Large Language Models to Small Language Models: Organizations that have better navigated the AI curve understand the AI models must be tailored to the needs of the organization, and the accuracy of the outcomes is closely linked to the data it is fed. While it is tempting to rely on off-the-shelf LLMs, it is difficult to create differentiated value through these models. Small language models on the other hand are customized, vertical-specific, and tailored to the needs of the organization deliver impactful outcomes.
Working with a large B2B marketplace, we understood that our client needed a retrieval augmented generated (RAG) model that is built and trained on its platform-specific requirements. This is where the data strategy comes into play, and we helped the customer re-architect to meet AI-specific goals.
In another case, we have helped a banking customer to prepare the data for AI by starting with data classification. We embedded intelligence into the process to classify compliant-bound data and less sensitive data and built a solid foundation for the next steps. Once data was categorized, we could strategize how to harness the data and explore use cases for automation, analytics and AI in a structured and compliant manner. We empowered the customer to harness even non-critical data to identify opportunities for automation and enhance efficiency.
Thriving with an AI DNA
Implementing AI goes beyond layering intelligence on top of existing systems. Organizations must embrace an AI-native mindset and implant AI into the DNA of the organization—to include efforts towards re-designing the architecture, re-engineering workflows, and embedding intelligence into decision making.
Infusing AI into the way organizations think and act creates new possibilities. Forward-looking companies are rolling out AI deployments as a strategic initiative with a long-term vision of how it will shape business outcomes, while building robust data foundations with thoughtful implementations. By empowering employees to work alongside AI, organizations are crafting winning strategies by amplifying human ingenuity with AI.
