AI-fit
Judgement and pattern work where AI adds real value.
Don’t just adopt AI.
Reimagine your enterprise.
We help organisations move AI from isolated experiments to embedded business capability.
A Decade of Impact
600+engagements delivered
40,000+leaders impacted
120+organisations partnered with
21+industries served
24countries reached
Don’t just make your business AI-enabled.Make it future-ready.
- Where is time being lost?
- Where is margin leaking?
- Where are leaders operating without visibility?
- Where are decisions taking too long?
That's where AI transformation begins.
How a traditional business transforms with AI.
An 11-minute documentary on Olive’s AI transformation journey.
Where does your organisation sit on the AI maturity curve?
Level03
AI Value Creation
AI changes what the organisation can achieve
Teams redesign experiences, solve meaningful business challenges and create AI-powered solutions that deliver measurable impact.
Level02
AI Integration
AI improves how the organisation works
AI becomes embedded into workflows, knowledge, decision-making and cross-functional processes.
Level01
AI Adoption
AI improves how people work
Teams use AI to accelerate everyday tasks, automate routine work and improve individual productivity.
The AI transformation journey
The six stages of AI transformation
Aligning at the top
Defining current and target AI maturity, and the priority workflows for AI-led transformation.
Cascading the AI-first mindset
Enabling key leaders to champion AI-first thinking rooted in business purpose and context.
Building dual fluency
Connecting business challenges with AI opportunities.
Enabling AI-centric ideation
Hands-on immersion in current AI tools and their use across domains.
Designing the AI sandbox
Prototyping AI solutions through quick experiments and user feedback loops.
Leadership showcase
Presenting working models to leadership for full-scale implementation.
We start with your business challenge, not with AI tools.
We shadow real roles and workflows, then sort every finding into one of four responses.
Automation-fit
Rule-based work that simple automation can handle.
Process-fix-first
Broken processes that need redesign before any technology.
Human-led
Decisions that stay with people.
Then we prioritise by business impact and how feasible it is to deliver.
Impact
Strategic bets
High value, higher complexity
Quick wins
High value, easier to deliver
Deprioritise
Low value or low readiness
Enablers
Foundations for future use cases
Feasibility
The Science Behind the Transformation
Research-backed. Proven methodologies. Real-world transformation.
Recent AI Transformation Projects with Measurable Business Impact.
01Turning inventory complexity into real-time visibility using AI
Outcome
- ₹1.43 Cr annual savings
- 100% inventory accuracy
- Business challenge
- Fragmented inventory records were creating errors, wasted effort and zero real-time visibility.
- What changed
- AI-enabled image recognition and automated categorisation created a more intelligent, trackable inventory system.
- Business value
- Lower cost. Greater accuracy. Better operational visibility.
02Giving each employee back 10+ hours every week
Outcome
- 10+ hours saved per employee, per week
- 30% productivity uplift
- Business challenge
- People were losing time navigating disconnected tools, fragmented information and repetitive workflows.
- What changed
- A context-aware GenAI hub, a workspace that brought relevant knowledge, workflows and personalised insights into one experience.
- Business value
- Less searching. Faster decisions. More productive work.
03Cutting engineering effort by 50%
Outcome
- 50% engineering effort saved
- 80% accessibility coverage
- Business challenge
- Manual testing was slow, inconsistent and difficult to scale.
- What changed
- AI automated test generation, validation and standards-based checks across the development cycle.
- Business value
- Faster testing. Broader coverage. Lower engineering effort.
04Reducing build time from 4 days to 22 minutes
Outcome
- Product prototype development: 4 days → 22 minutes
- 40% efficiency gain
- Business challenge
- Generic AI tools lacked organisational context, leading to rework and slower delivery.
- What changed
- AI was grounded in internal architecture, libraries, standards and organisational knowledge.
- Business value
- More context. Less rework. Dramatically faster execution.
05New seller onboarding from 11 days to 30 minutes
Outcome
- Onboarding time: 11 days → 30 minutes
- +5% seller NPS
- Business challenge
- A complex, manual onboarding process involved hundreds of specifications and multiple points of failure.
- What changed
- AI agents automated matching, information extraction and user support across the onboarding journey.
- Business value
- Faster onboarding. Fewer errors. Better customer experience.
06Moving cloud optimisation from manual decisions to intelligent autonomy
Outcome
- 3 critical decision dimensions: Cost | Performance | Governance
- 9 optimisation scenarios being checked
- Business challenge
- Cloud cost, performance and governance decisions required significant manual intervention, making optimisation difficult to scale consistently.
- What changed
- An autonomous AI-led optimisation approach is being designed to interpret signals, recommend actions and progressively execute decisions within defined human oversight and governance guardrails.
- Business value
- Faster decisions. Continuous optimisation. Governed autonomy.
07Reimagining the monetisation journey using AI
Outcome
- 12 customer journey stages redesigned
- 19 friction points eliminated
- Business challenge
- Fragmented monetisation journeys created unnecessary complexity, slowing customer adoption and delaying value realisation.
- What changed
- An AI-enabled experience is being prototyped to understand user context, simplify choices and guide customers towards the most relevant next action across the monetisation journey.
- Business value
- Less friction. Faster adoption. Quicker value realisation.
08Turning complex decision-making into an AI-guided experience
Outcome
- 18 decision pathways simplified
- 16 workflows / use cases transformed
- Business challenge
- Users had to navigate multiple inputs, rules and information sources before reaching the right decision, creating delays and inconsistent experiences.
- What changed
- A context-aware AI experience is being developed to bring relevant information together, interpret organisational context and provide intelligent recommendations at the point of decision.
- Business value
- Less complexity. Faster decisions. More consistent outcomes.
09From scattered ideas to a unified AI platform vision
Outcome
- 80+ AI opportunities identified and synthesised into a focused platform direction
- Business challenge
- The organisation had multiple user needs, data requirements and stakeholder perspectives, but no shared view of what the future AI platform should enable.
- What changed
- Research, stakeholder discovery and structured design work brought clinical, technology, security and consent perspectives together to define priority user journeys, data needs and solution opportunities.
- Business value
- Clearer platform direction. Better alignment. Stronger foundation for AI-enabled healthcare experiences.
10Redesigning the role of managers for an AI-supported future
Outcome
- A 90-day transition roadmap for new human + AI ways of working
- Business challenge
- As AI began taking over more routine work, managers needed to evolve from managing queues and tasks to orchestrating people, judgement and AI-enabled workflows.
- What changed
- Role research, behavioural scenarios and immersive simulations helped define the human capabilities that become more important in an AI-supported operating model: judgement, root-cause thinking, empathy and orchestration.
- Business value
- Clearer future roles. Stronger human capability. A practical roadmap for AI-enabled workforce transition.
11Turning AI strategy into live business experiments
Outcome
- 3 AI projects moved from strategic intent into real-world application
- Business challenge
- Leadership had ambition around AI, but needed a structured way to translate strategy into meaningful business use cases and demonstrate value.
- What changed
- Research, Design Thinking, future-back strategy and ongoing project coaching helped teams identify opportunities, shape solutions and move selected ideas into live experimentation.
- Business value
- Strategy translated into action. Stronger business cases. Real AI projects with organisational sponsorship.
12Building an enterprise pipeline for AI-led innovation
Outcome
- 8 AI solutions shaped into a broader transformation roadmap
- Business challenge
- AI and automation activity existed across the organisation, but there was no consistent mechanism to connect opportunities to business strategy or align priorities across geographies.
- What changed
- Strategic research, opportunity discovery and design sprints helped identify, prioritise and shape a portfolio of AI solutions tied to long-term business priorities.
- Business value
- A stronger AI opportunity pipeline. Better cross-functional alignment. A clearer path from experimentation to scaled adoption.
INDUSTRIES SERVED
AI at work across 21+ industries
Technology
IT and ITES
Financial services
Manufacturing
Automotive
Retail
Healthcare
Education
Hospitality
Logistics
Connect with our AI transformation experts
Saveen Hegde
Principal Consultant, AI & Enterprise Transformation
Has led AI, innovation and strategic transformation engagements across global organisations, helping leadership teams move from AI possibilities to business-led transformation.
Santoshi Kittur
Enterprise Technology & AI Transformation Advisor
Across roles with McKinsey, PwC and 360 ONE, she has led AI transformation in global BFSI and enterprise environments, helping organisations modernise technology and turn emerging technologies into scalable business solutions.
Lalit Kumar
AI Governance, Cybersecurity & Enterprise Transformation Advisor
At AWS, he helps AI initiatives move from experimentation to secure, governed, enterprise-ready deployment. His work combines cybersecurity, AI governance and enterprise transformation.
Geetha Prasanna
AI Adoption Consultant
Works at the intersection of people, leadership and AI transformation, helping organisations translate AI strategy into new behaviours, capabilities and ways of working. Has worked across 50+ AI transformation projects at Unbox.
Madhavi Nadig
AI Transformation & Upskilling Advisor
Former CTO and senior data engineer at Spotify. Designed cybersecurity and 21st-century skills curricula for 1,000+ professionals. Brings systems thinking to AI adoption, from cohort-based learning to coaching executives through tech transformation.
Get the AI Transformation Playbook
The approach we use with leadership teams, written for leaders who want to start.
What’s inside
- The four stages of AI maturity and how to move up
- How to find problems worth solving with AI
- How to prioritise quick wins and strategic bets
- How to bring your people with you
Want to see the real work?
Book a call and we will walk you through the projects behind this page, and what a first step could look like for your business.