TECH INDUSTRY
Thushera Kawdawatta
AI DESIGNS THE DESTINATION
Redefining value creation, innovation and competitive advantages
Definition of AI driven strategy in today’s business environment
Creation of a cognitive enterprise where AI is the central nervous system – not merely analysing the past but architecting future realities.
First step in shifting from data informed to AI driven decision making
Transcend retrospective analysis by identifying a core and complex challenges – such as improving IVF success rates – and reimagining the entire value chain with predictive AI native intelligence at its heart.
AI driven strategies and long-term business vision
AI strategy becomes business strategy when it’s engineered to deliver a bold future focussed vision beyond existing limitations.
Distinguishing between a company driven by AI and one driven by data
An AI driven company shapes the future proactively while a business that’s driven by data simply responds to the past – one designs the destination whereas the other follows the map.
Business outcomes best suited to be shaped by AI
Outcomes that demand the navigation of immense complexities – such as predicting molecular efficacy in oncology or determining embryonic viability from microscopic cues.
AI to disrupt or reimagine traditional business models
AI reimagines traditional business models by shifting core value creation from human expertise to scalable data driven precision.
Role of AI in shaping innovation pipelines
AI acts as a large-scale serendipity engine, uncovering unexpected breakthroughs by simulating countless possibilities beyond human reach.
AI on competitive strategy
Competitive strategy is reshaped by shifting the advantage from market share to owning the data ecosystem, predictive models and learning velocity of your AI.
Unlocking hidden revenue streams
By tapping into adjacent datasets, revealing new opportunities and expanding value beyond the core business focus.
AI’s influence on ‘go to’ market strategies
It enables a shift from mass market to quantum market by targeting a segment with predictive precision by knowing customers’ needs before they do.
Integrating AI into executive decision making
By translating complex model outputs into clear and probabilistic business scenarios, and illuminating the strategic pathways, AI reveals rather than focusses on the black box.
Balancing AI recommendations with human judgement at leadership level
Effective leadership balances AI’s probabilistic insights with human values, ensuring optimisation serves meaningful and purposeful goals.
Avoiding overreliance on AI in strategic planning
By fostering a culture of constructive scepticism and treating AI’s output as a highly informed hypothesis that must still withstand rigorous human led strategic debate.
Metrics to evaluate AI strategies
Beyond ROI, by tracking velocity to insight, predictive accuracy and crucially, disruptive potential – the rate at which AI makes old business models obsolete.
Business functions that would gain the most from AI
Functions defined by navigating complexity and prediction: R&D such as drug discovery; high stakes decision support – as in clinical embryology – and hyper personalised customer experience.
AI used to drive personalisation in customer experience
By creating a digital twin of each customer, which enables a simulation of their needs and personalisation of his or her journey – a technique mastered in large-scale telecom environments.
Impact of AI on pricing and revenue optimisation
It allows for dynamic value based pricing, where the price is a fluid variable optimised in real-time based on factors ranging from supply to a customer’s perceived value.
AI in supply chain or operations
AI is revolutionising operations by enabling autonomous self-healing supply chains with predictive logistics that ensure precision and reliability.
Leveraging AI in workforce planning
By modelling future skills requirements, AI predicts the capabilities needed to invent the future, guiding us to hire, upskill and build symbiotic human-AI teams.
Data maturity needed to build effective AI strategies
A data first culture is needed where every business process is designed to generate the high fidelity contextualised data that fuels the AI engine.
Prioritising AI investments
Invest in foundational AI platforms, not point solutions; and create strategic agility, enabling rapid re-tasking of core AI capabilities to deal with new challenges or opportunities.
