AI decisioning

What is AI decisioning?

AI decisioning is the application of artificial intelligence (AI) technologies to improve decision making processes in various business contexts, including marketing and data activation. It involves using advanced algorithms and data analytics to analyze data, identify patterns, and provide recommendations. These AI-powered systems are designed to support human decision-making for more consistent data-driven outcomes.

How does AI decisioning work?

In the context of marketing, AI decisioning works through a series of steps, combining advanced technology with human insight to make informed decisions. 

Here’s how the process unfolds in detail:

1: Data collection and analysis

AI decisioning systems start by collecting large amounts of data from various sources. Typically, data scientists will choose the relevant data sources and determine which types of data to collect. 

This can include structured and unstructured data like:

• Customer interactions
• Financial documents
• Text documents
• Sensor readings
• Images
• Videos

To ensure the data is high-quality and relevant, humans clean and pre-process it before proceeding. The AI algorithms then analyze this data to identify trends, patterns, and correlations.

2: Context understanding

After collecting and analyzing the data, the AI system understands the decision-making content.

Humans define the context for the AI to consider in its decisions, like:

• Historical trends
• Market conditions
• Business objectives 
• Regulatory requirements

That way, the AI system gains a holistic view of the situation, considering all relevant factors before making decisions.

3: Pattern recognition and solution generation

The AI algorithms identify key insights within the data using advanced analytics and pattern recognition. They recognize similarities and differences between the current situation and past experiences.

Based on this analysis, the AI system generates potential solutions for the current problem or challenge. 

It evaluates each solution by considering:

• Probability of success
• Potential risks
• Alignment with desired outcomes

Human experts can then review these AI-generated solutions and contribute additional ideas based on their expertise and intuition.

4: Decision-making and recommendations

Lastly, the AI decisioning system combines its analysis and recommendations into a coherent solution. It provides a recommendation based on the most promising solution, taking into account:

• Available evidence
• Contextual factors
• Desired objectives

Human decision-makers then review these AI-generated recommendations, applying their judgment and expertise on whether to implement the suggested solution.

Types of AI decisioning platforms

There are three main types of AI decisioning platforms. Let’s look at each of them:

Decision support systems

Decision support systems are designed to help humans with decision-making by providing insights derived from data analysis. 

Main capabilities include:

• Data aggregation and analysis
• Pattern recognition
• Trend identification
• Visualization of complex data

Decision augmentation systems

These systems go a step further by suggesting specific decisions or actions based on predictive and prescriptive analytics. They let users evaluate and act on AI-based recommendations. 

Capabilities include:

• Predictive analytics
• Scenario analysis
• Risk assessment
• Recommendation generation

Decision automation systems

These systems are designed to fully automate decision-making processes using predefined rules, machine learning models, and advanced analytics. They don’t require human intervention, as they apply algorithms and logic to execute decisions in real time.

Some of its capabilities are:

• Rule-based decision-making 
• Real-time data processing
• Automated action execution

Here’s a table comparing the differences between the models to help you decide which one is best suited for your specific situation:

How do other AI-related technologies (LLMs, machine learning, generative AI, and agentic AI) relate to AI decisioning?

AI decisioning is a broad concept that leverages various AI technologies to improve decision-making processes. We’ll explore how technologies like large language models (LLMs), machine learning, generative AI, and agentic AI relate to AI decisioning, along with their differences.

Here’s how they relate:

LLMs

LLMs are integrated into AI decisioning systems where natural language understanding or generation is needed. One example is in customer support chatbots, where LLMs analyze questions and decide appropriate responses. Another example is in content analysis, where LLMs process unstructured text like customer feedback to identify trends or sentiments.

The difference between LLMs and AI decisioning is that LLMs excel at processing unstructured text data, while decisioning AI usually involves broader logic and structured models for tasks like classification or automation.

Machine learning

Machine learning is a fundamental component of AI decisioning, as it provides the algorithms to analyze patterns in data and make predictions.

A few examples of machine learning used in AI decisioning systems for marketing include:

• Customer segmentation: Machine learning algorithms analyze data to group customers into categories. This helps tailor marketing campaigns to each segment.
• Ad targeting and personalization:  Machine learning algorithms analyze user behavior, search history, and demographics to predict which ads are most relevant. As a result, a more informed decision about when ads should be placed can be made.
• Churn prediction: Marketers can use machine learning to analyze patterns like reduced activity or complaints. They can then use retention strategies like personalized offers and loyalty rewards.

While machine learning focuses on building and refining models, AI decisioning involves the rules, workflows, and actions triggered by these models.

Generative AI

Generative AI supports AI decisioning by creating content that informs decisions, rather than making decisions itself. One example of this using generative AI to create tailored ad content, which AI decisioning systems then optimize for different audiences.

Note that generative AI provides creative outputs that complement decisions instead of determining them. It focuses on content creation, while AI decisioning chooses actions or recommendations based on data and logic.

Agentic AI

AI decision-making systems often use agentic AI technologies to improve their decision-making processes. This is mainly because agentic AI’s autonomous decision-making capabilities, adaptability, and goal-oriented nature align well with the objectives of AI decision-making systems. 

Agentic AI’s capacity to handle multi-step, complex challenges boosts AI decisioning’s ability to handle difficult situations. In addition, agentic AI focuses on achieving long-term objectives, aligning with AI decisioning’s aim to make informed choices to meet specific goals. 

AI decisioning also benefits from agentic AI’s ability to learn and adapt to new situations by refining its decision-making processes based on past experiences and new data.

While AI decisioning often incorporates agentic AI, one key distinction is that AI decisioning is a broader concept that focuses on making informed choices whereas agentic AI specifically refers to autonomous, goal-oriented AI systems.

Additionally, AI decisioning often involves human oversight and input at various stages, while agentic AI aims for higher levels of autonomy. One example is using agentic AI to continuously monitor campaign performance and adjust variables like ad placements and audience targeting without human intervention.

Benefits of AI decisioning

The main benefits of AI decisioning include:

• Faster testing and iteration: AI decisioning can automate processes like testing different versions of campaigns (A/B testing) and iterating based on performance data. Marketers can launch campaigns faster by determining what works and scaling up high-performing campaigns for better ROI. For example, content marketers can test different ad headlines or email formats to see what drives the most engagement.
• Predictive analytics and forecasting: Predictive AI decisioning systems analyze historical and real-time data to predict future outcomes and forecast metrics like conversion rates or sales. Marketing analysts can use these forecasts to advise teams on strategy and performance expectations.
• Enhanced customer insights: AI decisioning systems sift through large datasets containing demographics and preferences to give marketers deeper insights into customer behavior. As a result, marketers can identify pain points or reasons for churn and take corrective action. Brand managers can better understand customer perceptions to build stronger brand loyalty.
• Campaign optimization: AI decisioning systems can analyze performance data and continuously optimize campaigns across different channels. This automates tasks like adjusting ad spend and refining targeting. Growth marketers can continuously test and refine strategies to maximize customer acquisition and retention.

Challenges of AI decisioning 

Some challenges to consider when using AI decisioning include:

• Over-reliance on automation: AI decisioning can automate key marketing functions like ad optimization and customer segmentation, but marketers might become too dependent on these systems. This might reduce human creativity and strategic thinking, making marketing campaigns feel generic to customers and ultimately hurting campaign ROI
• Difficulty in scaling across channels: Many marketing campaigns span multiple channels like social media and email, but AI decisioning can struggle to provide consistent optimization across diverse platforms. 
• Lack of flexibility: AI decisioning systems struggle to adapt to new data sources and emerging marketing initiatives. This becomes a huge problem in fast-moving markets. Slow adaptation to new data or customer feedback can lead to missed opportunities or poorly optimized campaigns.
• Lack of transparency: Most AI decision-making systems operate as “black boxes,” making it difficult for marketers to understand how the system reached certain decisions. For example, it might not be clear why the algorithm chose one customer segment over the other. This means it’s important to keep humans involved throughout the entire process.

AI decisioning examples

Here are some examples of how industries like healthcare, finance, and retail are using AI decisioning.

Healthcare

There are a few scenarios where the healthcare industry has tapped this technology:

• Clinical decision-making: AI systems analyze data from wearable sensors and cameras to monitor patients’ mobility, safety, and treatment adherence. This benefits patients, physicians, and specialists who track patient progress remotely. It provides data-driven insights and recommendations that support accurate and personalized treatment decisions.
• Medical image analysis: AI systems can review medical images like x-rays and CT scans to help with diagnosis. One example of this is seen by Mayo Clinic’s use of AI for improved diagnostic accuracy. Radiologists can receive AI-generated insights to support their interpretations and cardiologists can analyze ECGs more efficiently. 
• Organizational decision-making. AI analyzes operational data to predict performance metrics, benefiting hospital administrators who can optimize staffing and resource allocation. 
• Improved collaboration between healthcare providers and patients: Generates tailored health information and recommendations. Patients can receive more relevant health education, while clinicians can offer more targeted treatment options.

While AI decisioning systems can provide various benefits across the healthcare sector, it’s important to note human healthcare professionals still need to be involved in the process to review AI-generated decisions. This ensures AI decisions account for individual patient context, ethical considerations, and accuracy.

Finance

Benefits of AI decisioning in finance include:

• Risk management and assessment: AI analyzes large amounts of data to assess credit risk, detect fraud, and identify potential market risks more accurately and quickly than traditional methods. A real-world example is J.P. Morgan, which implemented AI for improved fraud detection. Risk managers can use AI-driven risk assessment models for better risk identification and mitigation strategies. 
• Regulatory compliance: AI systems monitor transactions and flag potential compliance issues. This benefits compliance officers who can use AI tools to boost regulatory compliance monitoring and reporting. For example, HDFC ERGO uses AU to automate compliance checks in underwriting processes.
• Investment portfolio optimization: AI processes market data, news, and economic indicators to optimize investment portfolios and generate trading strategies. This benefits investment managers by enhancing portfolio performance and supporting better decision-making.

Retail and e-commerce

Here’s how pricing analysts, customer experience managers, and data analysts use AI decisioning systems in their retail processes:

• Inventory management: AI analyzes data from multiple sources to forecast trends and adjust stock levels, helping product managers make smarter decisions about product channel and stock management. It can also identify trending products and optimize product assortments. 
• Demand forecasting: AI decision-making systems use machine learning to predict customer demand, optimizing inventory levels and minimizing waste.
• Dynamic pricing: AI-driven pricing strategies adjust prices in real-time based on demand, inventory levels, and competitor actions. For example, Amazon uses dynamic pricing algorithms to maximize revenue while staying competitive. In marketing, dynamic pricing can be used for ad campaign bidding and personalized offers.

Best practices for using AI decisioning tools

Organizations planning to implement AI decisioning tools should follow these best practices::

• Human-AI collaboration: Maintain human involvement in AI decision-making processes, from design to ongoing evaluation. This ensures AI decisions align with human values and ethics while allowing professionals to intervene when necessary for more nuanced judgment.
• Ethical considerations and transparency: Develop clear guidelines for AI decision-making and ensure transparency in AI-driven decisions through explainable AI techniques. This is key to maintaining trust, accountability, and fairness in AI systems. It also helps humans understand how certain decisions were reached by the AI system.
• Leverage the power of the data cloud: Store and manage all organizational data in a secure, scalable data cloud to ensure AI systems can access clean and high-quality data for accurate decision-making. This reduces silos and improves the AI’s ability to process large datasets quickly.

Continuous improvement and adaptability: Implement a feedback loop for continuous model refinement and improvement. This can be done by regularly monitoring AI performance, collecting real-world data on its decisions, and using that data to update the model for improved accuracy over time. Moreover, reassess AI use across business areas to identify and address inefficiencies.