Innovis Technology Services and Solutions Private Limited

02/09/2026

Everyone's asking: how do we use AI in our business?

Wrong question.

The right question is: "Where" do we use AI in our business?

Look at each step in your workflows and ask: "Where" can AI help?

Three interesting cases:

1. JP Morgan's loan agreement review used to take about 360,000 hours a year of lawyers and staff manually checking terms. They built Contract Intelligence platform to do that first-pass review. What took hours, now takes seconds.

2. Unilever processes approx. 1.8M job applications a year. The bottleneck was screening them. They moved video interviews and initial assessment to AI. The end result: 50,000+ hours saved annually, time-to-hire down by 75-90%.

3. Klarna's AI assistant took over two-thirds of customer service chats, resolving issues in under 2 minutes instead of 11. But they cut human support too aggressively and the AI struggled with complex, emotional cases. So, they reassessed and rebuilt that team. The lesson: identify where AI fits in the workflow, and what can be efficiently handed over to it.

All these cases are about finding the specific points in your workflow where AI creates leverage: faster, cheaper, better, or possible at a scale you couldn't reach before.

Start with the workflow. Locate "where" and then put AI there. Simple.

Where in your workflow would you first put AI?

24/07/2026

AI as Infrastructure: Four Questions Every Leadership Team Should Ask

AI infrastructure is becoming widely available. Building the organizational capabilities to leverage it requires a long-term strategic view.

1. Can AI access the right information?
Asian Paints uses AI and ML-based demand forecasting to predict what products will be needed, where, and when across India. That capability was built on years of investing in data and supply chain systems.

2. Can your organization act in real time?
Swiggy uses ML models for demand forecasting, ETA prediction, and delivery partner allocation. The models are valuable because operations can respond immediately.

3. Can you pilot and scale AI use cases quickly?
JPMorgan Chase has rolled out GenAI tools to tens of thousands of employees across software engineering, customer service, and operations. Organizations that can move from pilot projects to enterprise-wide adoption will have an advantage.

4. Is your organizational knowledge shared or trapped inside silos?
Siemens uses industrial AI and digital twins to make engineering and operational knowledge accessible across teams and geographies. AI becomes far more useful when organizational knowledge isn't locked away in teams or individuals.

AI is evolving faster than organizational structures. That gap is where the next competitive advantage will emerge.

21/07/2026

AI as Infrastructure: Shift in Competitive Advantage

Every time a technology becomes infrastructure, the competitive landscape shifts. Companies stop competing on access to the technology and start competing on what they do with it.

Some patterns are already visible as AI becomes commonplace. The organizations that stand out will be those that can:

->Make better decisions, faster. DBS identified over 95% of non-performing SME loans at least three months before businesses experienced credit issues, and averted over 80% of borrowers from actual default.

->Run more experiments at lower cost and risk. Netflix runs continuous, large-scale testing on recommendations that no manual process could sustain.

->Reduce time to market. Moderna used AI to compress iteration cycles from months to weeks during vaccine development.

->Adapt quickly when markets change. Zara monitors real-time customer demand to adjust stock and production in days. All while competitors are planning around the next season.

AI is unlikely to be your competitive advantage. What you build on top of it might be.

09/07/2026

From Passive AI to Agentic AI - Why Governance Must Take the Front Seat.

According to the Gartner Hype Cycle for Agentic AI, we are moving away from chatbots that just answer questions, toward independent systems that plan, use tools, and execute workflows. It marks a fundamental shift from passive assistance to autonomous action.

But as autonomy scales, the real challenge is governance.

Agentic systems introduce a completely new risk profile. First, autonomous decision paths can be highly unpredictable because these agents dynamically choose how to accomplish a goal. Second, the jump to machine-to-machine interactions creates immediate security vulnerabilities. And finally, accountability becomes an issue.

That is why the shift from traditional "Data Governance" to strict "Decision Governance" is essential. Establishing trust, risk mitigation, and security controls are the true prerequisites for agentic deployment. True scaling is possible only with placing human specialists at strategic points to define the goals, set the ethical guardrails, and audit the ex*****on.

How is your organization balancing autonomous ex*****on with human governance?

20/05/2026

Integrating AI in Strategy

Everyone wants to “use AI in strategy” right now. The problem is that most organizations are treating AI as an add-on productivity tool and continuing to make decisions with the same old ways.

AI becomes strategically useful only when it changes how an organization senses change, interprets signals, and responds faster than before.

That requires integration at a deeper level.

A practical way to begin:

1. Use AI to detect patterns, not just generate content. Presentations, summaries, and drafts are useful. But limited use of AI. The bigger advantage comes from identifying weak signals across customer behavior, operations, competition, and market shifts before they become obvious.

2. Redesign workflows around faster learning. Traditional strategy cycles are built for slower learning. AI helps organizations speed up, test assumptions continuously, shorten feedback cycles, and adapt in near real time. No more quarterly reviews or annual plans.

3. Use AI to challenge strategic assumptions. Most organizations use AI to support existing thinking. The real value emerges when it is used to test assumptions, surface blind spots, simulate scenarios, and expose patterns that leadership teams may otherwise miss.

4. Treat AI adoption as a cultural shift. The technology is the easy part. The harder challenge is building trust, encouraging experimentation, and helping teams become comfortable working alongside systems that augment thinking.

The organizations that benefit most from AI are the ones that learn faster, challenge assumptions earlier, and integrate intelligence into everyday decision-making.

How is your organization integrating AI into strategic thinking beyond productivity gains?

29/04/2026

You invested in AI. Did you invest in humans?

Gartner is pointing to a problem most companies are missing - AI literacy.

Not tool usage. Not prompts. Actual understanding of where AI works, where it breaks, and how it fits into real decisions.

Right now:
1. Budgets flow into AI systems.
2. Adoption stays at the surface.
3. Depth of use remains shallow.

ROI doesn’t show up when humans aren’t at the same level as the technology.

Gartner’s view is clear - AI literacy has to be continuous, role-specific, and tied to real work. Not one-time training, workshops, or videos.

Gartner recommends linking learning activities to high-value use cases and strategic goals. And measuring real business outcomes, not just completion rates.

We are scaling AI faster than we are scaling human capability. And until that flips, most AI spend will look justified only on paper. The next advantage won’t come from better AI. It will come from people who know how to use it well.

Worth asking inside your org: Are we investing in AI, or in the ability to use it?

AI literacy is the missing link between AI hype and business value. Learn how CIOs can drive ROI with outcomes-driven, role-based AI literacy programs.

27/04/2026

AI is making us look smarter. Not work smarter.

At least not by default. The output is faster. It looks cleaner and more structured. It gives a sense of superior work. But it is only polish, and the gap still shows.

AI helps execute faster, but the judgment of what is worth doing, what to ignore, and where effort actually compounds remains with us. So, a lot of us are doing the same work quicker, just better dressed. But that is not smarter.

Real leverage still comes from three things good leaders weigh.
1. Prioritisation, choosing the few things that truly move the needle.
2. Framing, asking the right question before chasing answers.
3. Decision making, acting on what emerges instead of endlessly refining it.

AI is a force multiplier. It multiplies whatever we give it. If the input is scattered, the output only scales that scatter.

It's our choice. Should we stop at AI making us "look" smarter, or actually "work" smarter? How are you managing this? Where has AI made you look better without actually improving the work?

21/04/2026

AI and the Rise of the One-Person Team

Most work in large teams is coordination. Handoffs, briefings, and constant alignments needed to prevent loss of context as ideas move.

AI collapses that loop. You move from intent to output in a single, unbroken flow.

• AI handles the structural heavy lifting. Synthesizing data, drafting initial frameworks, and refining details.
• Your effort shifts from mechanical to conceptual work.
• You focus on strategy, judgment, and high-stakes refinement.

The real advantage is continuity of thought. One mind carries the idea end-to-end without fragmentation. The one-person team is viable because AI absorbs the operational friction.

Where does this work in your domain? Where does it break? Curious to hear.

15/04/2026

AI in UX

One of the most underrated uses of AI in UX is reducing decision fatigue.

Most digital products still assume that more choice equals better experience: more filters, more options, more customization. In reality, users are often tired. They don’t want to decide ten things before getting one result. A curated experience, tuned to context and past behavior, feels easier.

Done quietly, in a supportive way. For example,
- Pre-selecting defaults based on context
- Narrowing choices dynamically instead of showing all options upfront
- Suggesting next steps instead of waiting for user action
- Learning from patterns without making users repeat themselves

Good AI-driven UX balances convenience with user control. Too much, and it starts feeling intrusive. The sweet spot is when the user feels – ah, right!

Curious how others are implementing this in live products, especially where stakes are higher than content or shopping.

08/04/2026

Don’t outsource your thinking to AI

AI makes work faster. That part is clear.

The real question is not speed, but direction.

We give AI a prompt and get an answer. What often drops out is the thinking in the middle. The part where we question, test, and decide.

AI can write, explain, suggest, proofread, embellish. Its responses feel reliable because they are structured and confident.

But confidence is not accuracy. AI does not understand our context, our constraints, or our trade-offs. The risk builds quietly. We begin by accepting "good enough" answers in small tasks. Over time, the same habit shapes bigger decisions. Instead of thinking, we start relying on it.

That may feel efficient. But that's the trap. AI cannot judge what matters more in a given situation. That responsibility stays with us.

Using AI well means engaging with it, not deferring to it. It means asking better questions, probing the response, challenging it, and reshaping it until it fits our purpose.

Then we make the final call.

Do not outsource your thinking to AI. Keep the most critical part with you. And keep sharpening it.