By Shivraj Sabale, COO, Xoriant
In today’s AI-first economy, every enterprise is racing to “do AI.” But chasing artificial intelligence for novelty’s sake or applying it narrowly to reduce costs and automate workflows has left many organisations questioning the real value of their technology investments. According to Gartner, nearly 85 percent of AI projects fail to deliver meaningful business outcomes, underscoring the gap between experimentation and measurable impact.
The next leap forward will not come from isolated AI initiatives, but from Applied Intelligence, which is the disciplined integration of AI, data, and technology to drive tangible business results. Applied Intelligence transforms AI from a set of tools into a strategic capability that fuels growth, innovation, and resilience. It’s about embedding intelligence into the very fabric of how a business thinks, operates, and evolves, turning information into foresight, automation into adaptability, and digital transformation into a sustained competitive edge.
Rethinking AI
For years, enterprises have equated AI adoption with progress—layering algorithms, chatbots, and automation across every function. Yet the pursuit of efficiency has often delivered diminishing returns. Many teams have automated outputs, not outcomes, creating more activity but less impact. Researchers describe this phenomenon as “workslop”—work that looks polished but lacks depth or context. Further, the illusion of speed masks the cost of rework, clarification, and lost focus, leading to an efficiency fallacy that views AI as an end rather than an enabler.
Real transformation comes from applying Artificial Intelligence along with data, context, and human judgment to drive meaningful outcomes. This approach of Applied Intelligence shifts the focus from automation to elevation, turning technology into a force for smarter thinking and sustained value.
The three pillars of Applied Intelligence
Applied Intelligence marks a shift from using AI to automate processes to using it intelligently to reinvent and monetise them. It’s not about doing things faster, but about doing them smarter, creating systems that learn, adapt, and unlock new value. Three interconnected pillars define this shift: Platformisation, Data-Driven Intelligence, and Business Modelisation.
Platformisation: Building the Intelligent Core
True intelligence begins with architecture. Platformisation is about replacing fragmented digital initiatives (often disparate software products) with a unified, AI-native platform that connects data, models, and business processes. Such a foundation doesn’t just support automation; it enables agility and change. It allows teams to plug in new tools, scale innovation instantly, and maintain a common intelligence layer that keeps every decision grounded in shared insight.
Data-Driven Intelligence: From Information to Foresight
In most organisations, data still travels more slowly than decisions. Applied Intelligence reverses that equation. It leverages the data generated by the platform, streaming across systems in real time, interpreting it through governed models, and translating it into action. This insight, which is instantaneous and explainable, shifts decision-making from hindsight to foresight. That’s how intelligence moves from the dashboard to the front line.
Business Modelisation: Turning Intelligence into Competitive Advantage
The ultimate expression of Applied Intelligence is not operational efficiency; it’s strategic reinvention. When enterprises harness the data-driven intelligence, they can turn insights into entirely new business models: predictive services, adaptive pricing, ecosystem partnerships, or outcome-based offerings.
Applied Intelligence, hence, becomes the new currency of growth, monetised not through scale, but through continuous learning.
Applied Intelligence in Action Across Industries
Across industries, Applied Intelligence is redefining what it means to be a Software-Defined Business.
In financial services, banks are embedding AI into wealth management platforms to deliver hyper-personalised portfolio insights that strengthen client trust and deepen engagement, rather than simply automating advisory tasks. In healthcare, large language model–powered diagnostic systems built on RAG frameworks are transforming unstructured clinical data into real-time insights that accelerate diagnosis and enable preventive, remote-first care. In consumer goods, enterprises are moving toward always-on commerce by deploying demand-sensing platforms that integrate sales, inventory, and market signals to drive dynamic pricing and personalised promotions. In manufacturing, predictive maintenance platforms analyse sensor data to forecast equipment failures and automatically generate service workflows, creating new business models such as maintenance-as-a-service, while improving reliability and uptime.
Turning Vision into Action
Being Applied Intelligence–first is about transforming how leaders think about technology, not just how they implement it. It calls for a mindset where intelligence becomes the organising principle of every business decision, product design, and customer interaction. Leaders must move from managing projects to architecting systems that learn and evolve.
In the Applied Intelligence realm, software development shifts from delivery cycles to living ecosystems that gather data, learn from outcomes, and continuously refine themselves. Every application becomes a source of learning, not just execution.
This approach also reshapes how teams work and how the role of the engineering team changes. Engineers, data scientists, and domain experts collaborate as unified intelligence teams, designing solutions that improve with use. Software engineers evolve into creator-engineers, who work at the intersection of technology, business, and human empathy. Governance focuses on transparency and trust, ensuring that every model and insight aligns with ethical, explainable outcomes.
Closing thoughts
Applied Intelligence honors the real shift from labor arbitrage to tech arbitrage, where the end-to-end process – from designing to delivery – evolves to become platform-enabled with AI. This is where technology and transformation intersect, creating the perfect storm for meaningful disruption.
In essence, it’s about becoming a Da Vinci with AI — ideating, creating, and innovating with intelligence as the canvas. Because the future won’t belong to those who use AI, but to those who apply it with purpose. And enterprises that lead with Applied Intelligence will define the next era of innovation, which will be driven not by automation, but by awareness, adaptability, and purpose.