What is a feasible approach to transform business model value propositions with AI?
How Reinventing Value Propositions with AI?
Many realities once considered complex human knowledge are now prime candidates for AI replication. So rather than more narrow process improvements, the business question is not: how can artificial intelligence enhance our operations?
How Could AI Change the Value We Offer Customers?
Discerning AI’s highest-order potential requires clarifying industry assumptions back to first principles and then rigorously reexamining them.
Reinventing the core value or recreating value offered, pass through a simple reflection on:
§ What elements of value looked permanently fixed or scarcely deliverable before recent tech advancement?
§ Where can automated expertise, dynamic personalisation, and predictive ability unveil unprecedented value?
§ Which new AI capabilities might even invalidate yesterday’s notions of value?
These questions are known but may not be evident ( answered yet) in the executive minds, including the fact that reinventing value propositions with AI is the primary justification trumped by AI operators.
it is unclear if and how AI can generate value; thus, it is not yet mainstream business practice today. Since radically reinvented value propositions have yet to be widely demonstrated, many executives struggle to discern or quantify the ultimate value AI unlocks relative to investment costs. With value drivers of emerging intelligent offerings seeming intangible, many companies cannot build solid business cases, limiting experimental development.
AI is Not Seen as a Top Priority ( yet) due to experimentation.
Most companies are beginning to explore basic AI automation for internal process improvements. So, there is limited executive awareness or urgency around AI’s transformative potential for external-facing value propositions and offerings. Without this top-down strategic mandate, possible bolder innovations around intelligent products and services are not prioritised.
Newer AI Technologies Remain Challenging ( but not forever)
While AI is trending rapidly overall, advanced machine learning capabilities allowing hyper-personalisation, natural language interfaces, and emotion/sentiment analysis are still emerging. Average companies’ mastery and adoption of these complex AI methods trails the technology’s pace. Most current success stories feature tech giants pioneering new offerings - not mainstream business applications.
Building New Business Approaches is Hard, Even with AI.
Designing and launching entirely novel AI-based offerings requires significant business model changes - from new partnerships to revenue models and internal capabilities development. These end-to-end realignments around innovative value propositions appear daunting compared to localised AI pilots. Without compelling proofs of concept, most business leaders hesitate to pursue unverified new value propositions AI enables.
Essentially, most mainstream companies remain focused on early optimisation use cases. Until more tangible examples and frameworks emerge proving AI’s value proposition transformation potential, conservative strategic inertia will limit developments primarily to forward-thinking tech firms. But the principles, questions and foresight to reimagine core offerings via AI represent a vital competitive frontier every business must begin exploring, even in these early days.
However, these questions promise to spark new trajectories of value proposition possibilities and product innovation. The starting point for transformative thinking is to analyse the building blocks of customer problems and existing solutions.
Here are some questions I will answer soon about AI and its value
ü How Might AI Reframe Customer Problems?
ü What and How New Things Does AI allow us to Do ( capabilities)?
ü How Can AI ( Effectively) Customise Interactions for Each Customer Persona?
Now is the time to look at how AI can change what your business gives customers. Put together a team to rethink your offerings using AI's abilities. Question long-held industry beliefs and assumptions. Brainstorm new services with personalization, prediction and automation. Bold experiments today allow new intelligent products tomorrow. Though the path isn't fully clear, failing to actively explore AI risks ceding leadership to disruptive competitors.
Reinventing offerings with AI makes choosing AI systems later easier. Defining brand new intelligent services first makes clear what machine learning is needed. This spells out must-have technical needs when eventually picking AI vendors and partners later, cutting through a complex marketplace.
Deciding about core AI strategy now pays off down the road.