Anand Raman, EVP and COO, Newgen Software, says AI will “truly catalyze” digital transformation.
Remember when ‘going digital’ meant launching a mobile app and moving files to the cloud? It then felt bold and visionary. However, today that’s just the bare minimum. We’ve moved from digitizing processes; we are now reimagining entire business models. The transformation is intelligent, adaptive and always on.
Here’s a wake-up call though, by 2027, global spending on digital transformation will hit a US$ 3.9 trillion. That’s not a trend; it’s a tidal shift – and AI is powering the wave. In fact, 64% of firms believe AI will significantly boost their business outcomes.
Across industries, including banking, insurance, healthcare and government, AI is reforming how decisions are made, services are delivered, and operations are managed.
Today, the market isn’t waiting. Customers aren’t waiting. We’re talking machine learning (ML) models, generative AI (GenAI), natural language processing (NLP), AI agents, and data analytics. If organizations still treat AI like an R&D experiment, they’re not just late, they are at the verge of becoming irrelevant.
Why AI is a Catalyst for Change
The numbers tell the story:
- Analysts estimate GenAI could add $2.6–$4.4 trillion annually to economic growth
- In financial services alone, McKinsey projects $200–$340 billion of value (9–15% of operating profit) from AI, mainly from higher productivity
- Gartner says that by 2028, 33% of enterprise software applications will include agentic AI, enabling 15% of day-to-day work decisions to be made autonomously
These are not forecasts for 2030. These shifts are already underway and driving innovative processes.
Modern AI models draft detailed financial reports or summarize medical research in seconds
Advanced analytics and ML sift through terabytes of transactions or patient data, surface hidden patterns and recommend the best next steps
NLP understands text and speech at scale, turning complex regulations, claim forms, or medical notes into structured insights
Conversational interfaces, including chatbots and voice assistants, democratize access with no rigid forms. Employees and customers engage with systems in natural, interact with systems as if talking to a human
But how does this translate into business objectives?
Transforming Businesses Through the AI Lens
The same set of AI technologies are redefining the basics of how businesses operate, compete, and grow. When deployed strategically, AI optimizes outcomes and transforms the enterprise for intelligence at scale.
Let’s explore five core impact zones where AI is already reimagining industries.
- Enabling Smarter Decision-making
AI augments human decisions at scale by turning vast datasets into actionable insights. In banking, for instance, ML models evaluate millions of customer data points to price loans and detect fraud. The models scour a bank’s massive research database, summarize regulatory changes, and draft follow-up client emails. Likewise, healthcare organizations use predictive models to flag hospitalized patients at risk of complications, prompting early interventions. Across industries, AI transforms intuitive guesswork into data-driven strategy.
- Delivering Personalized Experiences at Scale
Today, AI makes hyperpersonalization practical at enterprise scale. In insurance, digital agents analyze a business client’s data (industry, size, history) and instantly generate a customized policy package. In healthcare, AI chatbots handle routine inquiries and triage patient symptoms, providing users with relevant information. These AI systems learn continually and improve their ability to match products to needs. The result is a Netflix-like experience in business services: relevant, customized, and instant. Firms are better equipped to deliver “one-to-one” service at a “one-to-many” scale.
- Driving Operational Efficiency
Routine processes – document review, compliance checks, and claims processing– can be automated with AI and robotic process automation (RPA) tools. For example, banks can leverage AI-driven OCR and NLP to process loan applications. The system extracts data from financial statements, verifies information, and flags anomalies, reducing manual review. Even back-office IT tasks benefit: AI code assistants translate legacy code and debug software, accelerating development.
- Managing Risk Effectively
Risk management is another domain where AI shines. Here, it serves as a radar system, constantly scanning for anomalies that signal danger. In banking, AI models analyze transaction streams in real time. For example, credit risk teams can utilize AI to update risk scores instantly as new data flows in, leading to more accurate lending decisions.
Government agencies can employ AI to combat fraud in benefits programs by detecting suspicious claim patterns. In fact, studies estimate that AI-driven analytics in healthcare payers alone could save billions (for every $10B in revenue, $150–$300M in admin and $380–$970M in medical costs).
- Supporting Growth and Market Expansion
Far from just cutting cost, AI is a launch pad for new products, services, and markets. Enterprises can utilize AI on big data to identify underserved communities and launch digital services specifically targeted where needed. Furthermore, digital insights reveal cross-selling or upselling opportunities. Firms that tap this acceleration will leap ahead of competitors, reaching new markets and customer segments faster.
Challenges in AI Adoption
While AI promises transformation, unlocking its value is not as easy as plug-and-play. For most enterprises, adopting AI is less about models and more about readiness. The real friction lies not in the tech itself but in everything surrounding it. Here’s what usually gets in the way:
- Cultural resistance: Teams fear disruption or don’t understand how AI fits into their roles
- Siloed data: Incomplete or unstructured data leads to poor model performance
- Lack of ethical frameworks: Without explainability and governance, trust in AI breaks down
- Skill gaps: Talent needed to build, scale and manage AI is still limited
- Unclear leadership direction: Without clear ROI, strategy, and endorsement, initiatives lose momentum
- Legacy infrastructure: Aging systems aren’t built for modern, real-time, cloud-based AI solutions
Success requires addressing real-world constraints before the full power of AI flows.
Building for Long-term AI Success
If the barriers to AI adoption feel familiar, it’s because they are systemic and not technical. Culture, clarity, and capability matter more than code. For C-suite leaders, the mandate is clear: approach AI as a marathon, not a sprint. Long-term success demands vision and execution.
- First, set a clear strategy and leadership: tie AI initiatives to specific business goals. Harvard Business School experts advise a measured, step-by-step approach to transformation. In practice, this means piloting use cases that demonstrate quick wins (say, an AI-powered chatbot or an automated report generator) and scaling those successes
- Invest in data and technology foundations. Modernize infrastructure (cloud platforms, data warehouses, analytics pipelines) and break down silos. Firms like GE have shown that deploying IoT and cloud sensors creates a unified platform for real-time analysis and predictive maintenance
- Establish governance. Set clear policies for ethics, data privacy, and accountability. Create AI teams that include IT, compliance, and business units
- Upskill employees, hire specialists, or partner with technology firms to build an AI-ready workforce
Summing Up
AI is not a one-time project but an ongoing capability to be refined. Ultimately, building for long-term AI success involves blending ambition with discipline. With the right foundation—strong leadership, clean data, robust platforms, and an innovation-friendly culture – AI will truly catalyze digital transformation. Once the foundation is solid, the journey ahead will be faster, smoother and much more rewarding.
