By: Mitchell Burman & Laks Srinivasan
There’s a common AI misconception.
That AI’s primary emphasis is synthesizing information to uncover insightful relationships, data-driven analysis, and intelligent text/image responses.
However, AI’s equally essential strength lies in its capacity to inform decisions, automate processes, and drive concrete actions that effect change and impact the bottom line.
Imagine having a detailed list of ingredients for a classic French dish, but no step-by-step cooking process. And then, without additional intuition to ask the right questions, just proceeding by mixing all the ingredients at once and cooking the concoction at 600 degrees for 5 minutes.
Many organizations invest heavily in AI capabilities that produce fascinating discoveries yet struggle to translate these into meaningful outcomes. This gap between data-driven insightful analysis and real-world impact is what we call the “AI Chasm” – a critical challenge facing businesses today.
In this article, we’ll break down the AI value chain, examine its key components, and introduce a framework for implementing AI solutions that don’t just inform but transform your business operations. By understanding and applying these concepts, you can begin to bridge the AI Chasm and unlock AI’s full potential for your organization.
Understanding the AI Value Chain
The AI value chain represents the journey from raw data to business outcomes. It’s a comprehensive process that transforms vast amounts of data of all types into, ultimately, tangible outcomes:
- Data Ingestion and Processing: The collection and processing of raw data from various sources (e.g., numerical, images, text, video, etc.). This could include customer interactions, sensor readings, market trends, or other relevant data.
- AI: The development and execution of Human-like creative or decision-making capabilities executed by software systems. It includes everything from simple rules-based systems to extraordinarily complex decision/control processes and the most advanced generative AI.
- Delivery Mechanism: The interface between AI algorithms/software and real-world actions.
Examples might include AI-managed chatbots, self-driving autos, supply chain optimizers, medical diagnostics, personalized marketing emails, etc. - Outcomes: This is the value enabled by the delivery mechanism. Examples include elevated sales, increased efficiency, enhanced customer satisfaction, automated transportation, or the improvement of other key performance indicators.
Within AI, two essential elements work together to transform data into a form usable by the delivery mechanism to achieve an intended objective.
These elements, which we call the Circle and the Diamond, represent distinct yet interconnected components of the AI process.
The Circle: Data Relationships
The Circle is where AI synthesizes raw data into insightful output. It empowers organizations to understand their current and future business landscape better. It is often mistaken for representing the totality of AI.
At its core, the Circle is a realm of technical prowess. It leverages tools like:
- Advanced machine learning algorithms
- Generative AI models (LLMs)
- Sophisticated statistical methods
These technologies work in concert to extract meaningful patterns and predictions in the form of text, images, video, quantities, etc. from vast amounts of data.
The Circle’s activities are primarily mathematical. They form the bedrock of data-driven decision-making.
However, it’s crucial to recognize that the Circle has limited standalone value. It typically incorporates analytical snapshots or the one-time building of models rather than ongoing processes.
In addition to the function or model building, circle activities include data preparation tasks such as:
- Collecting diverse datasets
- Cleaning and standardizing
- Organizing for optimal analysis
Let’s consider a retail example to illustrate the Circle in action:
A large retail chain wants to optimize its operations and improve customer satisfaction. In the Circle phase, the retailer analyzes various data sources:
- Historical sales data across all stores
- Customer foot traffic patterns captured by in-store sensors
- Local weather data for each store location
- Economic indicators for different regions
- Social media sentiment about the brand and products
Using advanced machine learning algorithms and statistical methods, Circle activities might generate:
- Demand forecasts: Predicts product demand for each store location based on historical sales, weather patterns, and economic indicators.
- Customer behaviors: Identifies peak shopping hours and days based on foot traffic data.
- Product affinities: Discovers which products are often purchased together.
- Brand sentiments: Analyzes social media data to understand customer perception and emerging trends.
These insights can be output in the form of reports, graphics, videos or simply predicted quantities. They can provide a comprehensive view of the retail chain’s current and potential future scenarios.
However, while illuminating, this output alone doesn’t drive business outcomes. They form the foundation for data-driven decision-making, but the real value comes from acting on this information.
The Diamond: Driving Actions
The Diamond, similar to what people refer to as “Decisioning, ” not to be confused with simpler prescriptive analytics, involves generating recommendations, policies, and procedures that lead to measurable business outcomes. The Diamond covers a range of decision-making processes, from fully automated systems to those heavily reliant on human intuition and interaction.
The Diamond is often dynamic in nature. Rather than a single model and associated interpretation, it is repeatedly employed to make decisions using a broad spectrum of sophisticated techniques such as:
- Business rules that codify business logic and policies into automated decisions.
- Recommendation engines that analyze patterns in user behavior and preferences to suggest relevant items or actions.
- Optimization algorithms for complex systems
- Sequential decision-making models that make use of updated real-time data
- Simulations that predict results before real-world implementation
- Reinforcement learning which allows AI systems to improve through trial and error
These techniques form the foundation of the Diamond’s decision-making capabilities, but their implementation varies significantly based on the complexity of the decision and the level of human oversight required.
The role of humans
The role of humans within Diamond processes exists on a spectrum, carefully calibrated to the level of risk and complexity involved.
On one end, we have fully automated Diamond processes where AI systems independently execute actions – like automated inventory reordering or real-time digital ad bid adjustments – where speed and consistency are paramount and the cost of errors is relatively low.
On the other end, we find Diamond processes where humans play a crucial role in the final decision-making, particularly in high-stakes scenarios like medical diagnoses, large financial investments, or strategic business pivots. The decision to incorporate human oversight isn’t arbitrary – it’s a strategic choice based on factors like potential impact, reversibility of decisions, regulatory requirements, and the need to incorporate contextual knowledge that may not be captured in data.
Continuing our retail example from above:
The Diamond creates actionable strategies and automated processes such as:
- Inventory Optimization:
- Action: Recommend stock level adjustments based on demand forecasts
- Outcome: Reduced stockouts and overstock situations, leading to improved inventory turnover and reduced carrying costs.
- Technique: Algorithms that balance competing factors – such as storage costs, ordering costs, and stockout penalties, while simultaneously accommodating the risks associated with demand uncertainty – to find the ideal ordering quantities and reorder points for each product.
- Dynamic Staffing:
- Action: Alignment of employee shifts with predicted peak shopping hours.
- Outcome: Improved customer service during busy periods and optimized labor costs during slower times.
- Technique: Constrained optimization that simultaneously balances multiple real-world limitations – such as labor laws, employee availability, skill requirements, and budget constraints – while maximizing coverage during predicted high-traffic periods.
- Personalized Marketing:
- Action: Product suggestions for customers based on their purchase history and product affinity insights.
- Outcome: Increased cross-selling and upselling, resulting in higher average transaction values.
- Technique: Recommendation engines that leverage collaborative filtering and content-based algorithms to identify patterns in customer behavior. The system analyzes both individual purchase histories and similar customer segments to predict which products a customer is most likely to buy next.
- Proactive Customer Engagement:
- Action: Address common customer queries and proactively engage customers using social media sentiment analysis.
- Outcome: Improved customer satisfaction and brand loyalty through timely and personalized interactions.
- Technique: Business rules that create a structured decision tree for customer interactions. The system follows predefined if-then-else logic chains that determine when and how to engage with customers.
- Dynamic Pricing:
- Action: Pricing adjustments in real-time based on demand forecasts, competitor pricing, and inventory levels.
- Outcome: Optimized pricing strategy that maximizes revenue while remaining competitive.
- Technique: Reinforcement learning algorithms that treat pricing decisions as a continuous experiment, where each price point is like a move in a complex game. The system learns from the outcomes of its pricing decisions by receiving “rewards” (increased revenue, sales volume) or “penalties” (lost sales, excess inventory) and adjusts its strategy accordingly.
These actions generated by the diamond create a direct connection to outcomes; the system can continuously learn and adapt its recommendations based on the results of these actions, creating a closed loop in the decision-making processes over time.
Conclusion: Embracing the Diamond
The journey from data to business impact is not a straight line. As we’ve explored, the true power of AI lies not in generating insightful output alone but in translating those insights into tangible, measurable outcomes.
By embracing both the Circle and Diamond, you can transform your organization’s approach to AI by:
- Recognizing that insights and data relationships(circle) are just the beginning, not the end goal.
- Focusing on developing robust decision-making processes (the Diamond) that turn insights into action.
AI is not just about insights—it’s about building a bridge to create action.
The most successful organizations in the coming years will be those that can effectively implement comprehensive, AI-driven systems that deliver real business impact. The time for action is now.