A strategic framework for identifying opportunities and threats.
In previous discussions, I've highlighted the critical importance of sensing, seizing and shaping as essential elements for analysing and reacting to changes brought about by artificial intelligence (AI) in the business landscape. Today, I delve deeper into the need for a novel, robust framework that enhances our ability to sense these shifts effectively.
The importance of strategic sensing
Strategic sensing has always been pivotal for businesses to navigate their environments, but with AI's rapid advancements, the traditional frameworks are falling short. We need to revisit and rethink these frameworks to better align with AI's transformative potential.
Drawing a parallel between the human body's sensory system and how businesses can 'sense' changes in their environment provides insightful strategies for tackling these challenges. This analogy forms the core of our discussion.
All my recommendations and work are based on the thought-provoking, intelligent discussions and learning I had while analysing various aspects with my colleagues at Saïd Business School. These relationships with unique groups of people provide an immense level of knowledge advantage.
The five senses of business sensing
Allow me to extend the analogy by comparing human senses to how AI can detect gaps in a business:
1. Sight (visual analytics)
This represents market trends, competitor analysis, and visual data representation tools that help businesses 'see' the current state and dynamics of their environment.
2. Hearing (customer feedback and market signals)
Analogous to customer feedback mechanisms, social media listening tools, and market analysis reports that allow businesses to 'hear' and respond to customer needs, preferences and the competitive landscape.
3. Smell (predictive analytics)
Similar to detecting changes early in the business environment, using predictive analytics to 'smell' potential opportunities or threats before they become apparent.
4. Taste (product and service testing)
Corresponding to product testing and market trials, enabling businesses to 'taste' consumer reactions to new offerings before a full-scale launch.
5. Touch (customer experience and engagement)
Relating to direct customer interactions and engagement, allowing businesses to 'feel' and understand the customer experience and satisfaction levels.
Framework to identify AI-driven gaps
Now that I've set the sensing parameters, I also need to create a framework that uses these senses to identify gaps.
1. Segmentation of Porter's value chain (Porter, 1985)
Primary activities: inbound logistics, operations, outbound logistics, marketing and sales, service.
Support activities: firm infrastructure, human resource management, technology development, procurement.
Key data-driven processes: Define critical data-driven processes for decision-making in each segment.
2. Data dependency and decision-making mapping
Framework creation: develop a matrix mapping each process against data dependency (low, medium, high) and decision-making influence (low, medium, high).
Example: for marketing and sales, map processes like customer segmentation, pricing strategies and promotional campaigns, assessing their data dependency and influence on decision-making.
3. Identification of gaps
Gap analysis questions: identify gaps with questions such as 'Is the current data utilized effectively for decision-making?' and 'What are the potential areas where AI can enhance data analysis or automate decisions?'
Gap scoring: assign scores to highlight the urgency or impact of addressing each gap.
4. AI integration opportunities
Solution proposal: propose AI solutions for identified gaps, such as machine learning models for predictive analytics or natural language processing for analyzing customer feedback.
Example: in marketing and sales, use machine learning models for dynamic customer segmentation based on real-time data.
5. Implementation roadmap
Prioritization: prioritize gaps based on impact and feasibility of AI integration.
Plan development: create a step-by-step implementation plan, including pilot phases, technology requirements, and staff training needs.
6. Monitoring and evaluation
Establish KPIs: monitor the effectiveness of AI integration in addressing identified gaps.
Regular review: update the gap analysis framework regularly to adapt to new data insights and technological advancements.
By following these steps, organisations can systematically enhance their value chain processes through data-driven insights and AI technology, leading to more informed decision-making and increased operational efficiency as markets evolve.
In my next blog, I will delve into the 'seizing' phase, focusing on how to capture identified opportunities and address threats, prompting necessary pivots or new actions. Finally, I will explore 'shaping' internal and external resources to sustain these strategic changes.
Stay tuned for the continuation of this exploration into the profound impact of AI on strategic business sensing, seizing, and shaping.
Oxford Executive Diploma in Artificial Intelligence for Business