By Mukund Rao, President of Global Markets, Xoriant
I think we can all agree that Artificial Intelligence (AI) has moved from ‘someday’ to ‘right now.’ As Jensen Huang put it recently, “this is the iPhone moment of AI — every company is becoming an AI company.” We are living that reality already in our personal and professional lives. Workflows are changing. Habits are evolving. Products are getting smarter. Entire industries are being re-designed around intelligence that learns and adapts.
As the excitement rises, one truth becomes clearer: the companies seeing real results are not the ones chasing the shiny tech. They are the ones who are balancing Human Intelligence with Artificial Intelligence.
They are the ones ‘Applying’ Intelligence.
What is Applied Intelligence
Applied Intelligence is the integration of artificial intelligence, advanced analytics and human intelligence to reimagine business processes and platforms to deliver tangible outcomes. It is about applying AI across the continuum (‘embedding AI’) versus focusing on isolated tasks (‘shiny AI’). This means reimagining existing platforms to be intelligent by applying the tech end-to-end towards driving measurable results, rather than retrofitting them with AI for the sake of AI. And it must necessarily be human-centric, leading to empathy-based, ethical and trustworthy outcomes.
Intelligence, applied
When done well, the impact of Applied Intelligence shows up in ways that actually matter to businesses.
For an industrial manufacturer of pumps and valves, it could mean acceleration of its quote to order process, with tangible benefits. Prospects get responses sooner, deals close faster, and the business grows because sales teams are now able to be more responsive, spend more time selling and less time compiling information for the quote.
Applied Intelligence also improves efficiency.
Efficiency can look different depending on the organisation and department. For technology companies and tech teams, it is about accelerating the software development lifecycle so teams can deliver new features faster, better, with less effort, saving time and money. For others, it may show up as frontline teams handling effective customer conversations with better first-contact resolution, directly meaning a happier customer. Or for others teams, it could mean preventing breakdowns before they happen, with intelligence driving predictive maintenance.
That is the possibility of applied intelligence — taking the promise of the tech, applying it holistically and making it real.
Three elements of Applied Intelligence
Applied Intelligence succeeds when the following three elements come together:
1. Embedding intelligence into platforms
Embedding AI and analytics in the platforms that power the business to learn and enable decisions as and when they matter; not offline or standalone.
2. Building for end-to-end impact
Real value shows up when AI is applied across the entire lifecycle, not to isolated tasks alone – from pulling the right data and triggering the right action to learning from every outcome. This means modernizing legacy systems and enabling agentic orchestration to drive predictive and proactive outcomes.
3. Keeping it human centric
AI should help people make better calls, but with explainability. The deployment of AI should be guided by empathy, ethical oversight and client context, so recommendations are relevant and trustworthy.
So where do we go from here?
Some of the most successful adopters of AI begin by identifying and executing one or a few key workflows that matter to the business, with the three elements of Applied Intelligence captured above. An industrial manufacturer could pick up the quote to order process, as suggested earlier. Or a leading insurer could choose to focus on accelerating the claims verification and resolution process. Or a wealth advisory can finally monetize unique datasets that they always had. Different industries, different use cases, different business outcomes.
What stays common is the discipline of working backwards from a specific solve with a measurable impact. When teams rally around a clearly defined problem, AI gets shaped to address friction in the process and stitch together data flows to drive insight. Human and machine roles are defined more sharply because the goal is specific.
Measurement becomes simpler because there is one (or a few) success metric(s) to care about.
That clarity, along with the three elements, can transform Artificial Intelligence into “Applied Intelligence” – the real AI we should be talking about.