Generative AI is revolutionising the business strategies and operational frameworks of both established enterprises and startups. Beyond performing well-defined tasks, GenAI is now addressing larger and more intricate challenges. It is not far-fetched to envision a future where a “GenAI strategist” can independently formulate and implement a business strategy. After having numerous discussions with various CXOs and startup founders who share this vision, I have gleaned insights into how they intend to integrate GenAI into their business strategies and operational methods. Here are some key observations from these discussions and the profound effects of GenAI on strategic and operational models:
Highlights
- 1 Business Processes – Enhancing Productivity by Minimising Disruptions
- 2 Integrated Strategy Machine – The Extensive Implementation of GenAI
- 3 Manufacturing Operations – AI Support on the Ground
- 4 Customer-Facing Activities – Near Real-Time Assessment
- 5 Generative AI for Diverse Operational Strategy Models
Business Processes – Enhancing Productivity by Minimising Disruptions
AI algorithms, while not inherently “intelligent,” learn inductively through data analysis. Many leaders and startup founders are investing in AI capabilities and establishing solid information infrastructures.
Airbus faced a multibillion-euro challenge when ramping up production of its new A350 aircraft, with an objective to elevate production rates at an unprecedented speed. To achieve this, they needed to swiftly address factory disruptions.
Turning to AI, Airbus combined historical production data, ongoing inputs from the A350 programme, fuzzy matching, and a self-learning algorithm to spot patterns in production issues. This utilisation of AI enabled Airbus to resolve approximately 70% of production disruptions by aligning them with previously effective solutions in near real-time.
Similar to Airbus, numerous pioneering organisations are leveraging AI to enhance speed and efficiency, leading to superior processes and outcomes. Other major companies, including BP and Wells Fargo, as well as Ping, an insurance entity, are already tackling significant business challenges using AI. Nevertheless, many organisations are yet to embark on this journey.
Integrated Strategy Machine – The Extensive Implementation of GenAI
The integrated strategy machine serves as the AI equivalent of innovative factory designs introduced by electricity. The increasing intelligence of machines, however, can only be harnessed effectively when businesses realign how they formulate and implement strategies. Regardless of technological advancement, human collaboration is essential to leverage competitive advantage; this collaboration must be embedded within what is termed the integrated strategy machine.
An integrated strategy machine consists of a blend of technological and human resources that work together to devise and execute business strategies. This encompasses various conceptual and analytical tasks, including problem definition, signal processing, pattern recognition, abstraction, analysis, and predictive modelling. A crucial function of this machine is reframing, which entails continuously redefining problems to foster deeper insights.
Amazon exemplifies the pinnacle of integrated strategy machine deployment. The company operates at least 21 AI systems, including multiple supply chain optimisation systems, an inventory forecasting system, a sales forecasting tool, and a profit optimisation system, among others.
These systems are intimately connected with one another and with human strategists, creating a cohesive, well-functioning machine. When the sales forecasting system identifies a surge in an item’s popularity, the resulting cascade of adjustments occurs throughout the system: the inventory forecast updates, prompting the supply chain system to optimise stock across its warehouses; the recommendation engine enhances visibility for the item, subsequently driving up sales forecasts; the profit optimisation system then adjusts pricing, which again updates the sales forecast.
Manufacturing Operations – AI Support on the Ground
CXOs within industrial firms anticipate significant impacts in operations and manufacturing. BP plc, for instance, enriches human abilities with AI to optimise field operations. They utilise the BP well advisor, a tool that processes data from drilling systems, offering guidance for engineers to adjust drilling parameters to remain in the optimal zone, while also alerting them to potential operational disturbances and risks ahead.
Furthermore, they are innovating automated root-cause failure analysis, developing a system that learns over time, enabling it to quickly transition from description to predictive analysis and prescriptive actions.
Customer-Facing Activities – Near Real-Time Assessment
Ping, the second-largest insurer in China, boasting a market capitalisation of $120 billion, enhances customer service across its insurance and financial services portfolio through AI.
One notable advancement allows them to provide online loans in just three minutes. This rapid turnaround is partly due to a customer scoring tool that employs an internally developed AI-driven face recognition capability, surpassing human accuracy. This tool has successfully validated over 300 million faces and complements Ping An’s cognitive AI functionalities, including voice and image recognition.
Generative AI for Diverse Operational Strategy Models
To fully harness GenAI in various business operations, enterprises should consider three principal applications of GenAI:
- Insights Enabled Intelligence: This widely accessible approach enhances current operations without altering fundamental processes. An example is Google’s Gmail, which categorises incoming emails into “Primary,” “Social,” and “Promotion” tabs, boosting efficiency without changing user interaction.
- Recommendation Based Intelligence: This emerging technology allows organisations to perform tasks that would otherwise be unattainable. Netflix, for instance, utilises machine learning algorithms to recommend choices to customers based on broader audience behaviours, thus transforming media consumption.
- Decision Enabled Intelligence: This future-driven approach is focused on creating autonomous machines capable of making decisions independently. Examples include automated trading systems and facial recognition technologies.
As you contemplate large-scale deployment of generative AI, determine the combination of these three strategies that best aligns with your goals:
- If your primary objective is enhancing existing processes, reducing expenses, and boosting efficiency, initiate with insights-enabled intelligence and develop a clear AI strategy roadmap.
- If you intend to establish your enterprise around new responsive and self-driven products or services incorporating AI, pursue a decision-enabled intelligence approach with more complex AI solutions.
- If you’re cultivating a genuinely novel platform, contemplate building an AI-led strategy foundationally integrated across the platform’s functionalities and processes.
CXOs and startup founders should formulate their own AI strategy playbook to reimagine business strategies and operational models, ultimately leading to enhanced business performance.
