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Tailored AI: Unlocking Business & Government Value with Industry-Specific Models

Updated: Jun 4

As the old adage goes, "the riches are in the niches." In today's fast-changing world, this phrase couldn't be more relevant. With the rise of Artificial Intelligence (AI), organizations are looking for ways to create value and drive efficiency in their specific domains. With my team's focus on Applied AI, I've had the privilege of working with various organizations - from private businesses to government agencies to specialty AI product and services companies - helping them to harness the potential of AI. The common thread across all of them is that they are challenged to implement AI solutions that truly drive value. That's where secondary models come in – making tailored, domain-specific value creation a reality.

Dr. Lisa made in partnership with Microsoft Designer

Imagine having a personalized assistant that understands the intricacies of your business or organization. This assistant has spent years learning from your industry's best practices, regulatory requirements, and unique challenges. With this expertise, it provides tailored insights and guidance to help you make better decisions, optimize operations, and drive growth.

This is where secondary models come in – making domain-specific, tailored value creation a reality. By capturing the intricacies of your industry / organization / role, secondary models can help you unlock significant value and drive real results.

In this post, I explore the power of secondary models, how they work, and the benefits they can bring to your organization (Figure 1). Please don't let this terminology stop you from reading further! I promise that I break it down into accessible and understandable business lingo.

Figure 1: Mindmap of this blog. Dr. Lisa made in partnership with Mapify (fully AI-generated mindmapping tool)

The "One-Size-Fits-All" AI Solution is like Generic Prescription Glasses

Imagine walking into an optometrist's office with a unique prescription that requires a specific lens shape, material, and coating to correct your vision. But instead of getting a customized pair of glasses, the optometrist hands you a pair of generic, off-the-shelf glasses that are supposed to fit everyone.

These glasses might work okay for some people, but they won't provide the same level of clarity and comfort for you, with your specific prescription needs. The frames might be too big or too small, the lenses might not correct your astigmatism, and the coating might not reduce glare from your computer screen.

Similarly, many AI solutions on the market today are like these generic glasses. They're designed to be a one-size-fits-all solution, attempting to solve broad problems with generic algorithms and models. But just like the generic glasses, they often fall short of delivering high value outcomes because they ignore the unique nuances and complexities of individual industries and organizations.

For example, a generic AI chatbot designed to handle customer service queries might work okay for a small e-commerce company, but it won't be able to handle the complex, industry-specific queries of a large healthcare organization or a financial institution. The chatbot might not understand the specific terminology, regulations, or workflows of these industries, leading to frustrated customers and low-value outcomes.

In contrast, a customized AI solution, like a bespoke pair of glasses, is designed to address the specific needs and challenges of an individual organization or industry. It's tailored to their unique requirements, taking into account their specific data, workflows, and goals. This approach leads to higher-value outcomes, increased efficiency, and better decision-making.

The Power of Secondary Models

Just as in the customized glasses example, imagine having a special kind of AI that's designed just for your industry, your organization, or your role. This AI, called a secondary model, is like an expert in your field, who has spent years learning and understanding the intricacies of your business or mission. This expert provides valuable insights and guidance, helping you to make better decisions and optimize your operations. Example outcomes that businesses and government entities can drive through the use of these secondary models include:

  • Create customized solutions: That address specific pain points and challenges in their industry or organization.

  • Drive efficiency and productivity: By automating routine tasks and freeing up staff to focus on higher-value work.

  • Improve decision-making: With AI-driven insights that are tailored to their specific needs and goals.

  • Enhance customer experience: By providing more personalized and effective services.

  • Increase revenue: By identifying new business opportunities, optimizing pricing, and improving sales forecasting.

  • Reduce costs: By streamlining operations, reducing waste, and improving resource allocation.

  • Improve compliance: By automating regulatory compliance, reducing risk, and ensuring adherence to industry standards.

  • Enhance innovation: By providing data-driven insights that drive innovation, improve product development, and accelerate time-to-market.

  • Improve employee experience: By automating routine tasks, providing personalized training, and enhancing employee engagement.

  • Drive sustainability: By optimizing resource usage, reducing waste, and improving environmental sustainability.

The Brain of the System: Secondary Model Engine

At the heart of a secondary model lies the secondary model engine, often referred to as the "brain of the system." This intelligent component is responsible for understanding the intricacies of your industry or organization and using that knowledge to provide valuable insights and guidance.

The secondary model engine is a sophisticated AI system that has been trained on a vast amount of industry-specific data, rules, and best practices. This training enables the engine to develop a deep understanding of the complex relationships between different variables, and to identify patterns and trends that may not be immediately apparent to humans.

Imagine the secondary model engine as a master puzzle solver. It takes in vast amounts of data, identifies patterns and relationships, and then pieces together the insights to provide a complete picture of your business. This allows you to make informed decisions, optimize operations, and drive growth. Examples of content in niched secondary models include:

  • Industry-specific regulations and compliance requirements

  • Best practices and standards for your organization or industry

  • The nuances of your business operations, including workflows, processes, and systems

  • The complex relationships between different data sources and variables

With this expertise, the secondary model engine can analyze data from various sources, identify opportunities for improvement, and provide actionable recommendations to drive business value. This might include:

  • Identifying areas of inefficiency and proposing process improvements

  • Detecting anomalies and predicting potential risks or opportunities

  • Providing personalized insights and guidance to support decision-making

  • Automating routine tasks and freeing up staff to focus on higher-value work

The secondary model engine is the key to unlocking the full potential of secondary models and is what sets them apart from more generic AI solutions. By leveraging the expertise of this "brain of the system," organizations can drive real value and achieve their goals more effectively.

Real-World Examples of Secondary Models in Action

By focusing on the specific needs of individual industries and organizations, secondary models unlock significant value for businesses and government agencies. Whether it's improving efficiency, driving revenue growth, or enhancing customer experience, secondary models offer a powerful way to create tailored solutions that drive real results. Here are a few examples of the content included in these specialized models:

  • Financial Services: Capture regulatory requirements, risk management policies, and customer data to facilitate the development of AI-powered solutions for fraud detection, credit scoring, and customer service.

  • Healthcare: Standardize clinical workflows, capture medical knowledge, and integrate with electronic health records (EHRs) to improve patient care, streamline clinical decision-making, and enhance operational efficiency.

  • Manufacturing: Capture production workflows, quality control procedures, and equipment specifications to optimize production planning, predictive maintenance, and supply chain management.

  • Retail and E-commerce: Standardize customer data, capture business rules, and integrate with inventory management systems to improve customer service, optimize inventory levels, and enhance supply chain efficiency.

  • Telecommunications: Capture network infrastructure data, standardize service provisioning, and integrate with billing systems to improve network reliability, reduce churn, and enhance customer experience.

  • Insurance: Capture policy information, standardize underwriting rules, and integrate with claims processing systems to improve risk assessment, reduce claims processing time, and enhance customer service.

  • Airlines and Transportation: Capture flight schedules, standardize crew management rules, and integrate with reservation systems to improve operational efficiency, reduce delays, and enhance customer experience.

  • Education: Standardize curriculum development, capture student data, and integrate with learning management systems to improve student outcomes, enhance teacher effectiveness, and optimize resource allocation.

  • Public Safety: Analyze crime patterns, capture incident data, and integrate with emergency response systems to improve response times, reduce crime rates, and enhance public safety.

  • Environmental Agencies: Standardize environmental monitoring, capture data on air and water quality, and integrate with regulatory systems to improve environmental compliance, reduce pollution, and enhance public health.

  • Transportation Agencies: Optimize traffic flow, reduce congestion, and improve public safety by capturing data on traffic patterns, road conditions, and weather.

A Simplified Explanation of this Technological Approach

The design of such a robust AI system can be broken down into several key components, each playing a vital role in ensuring smooth operation and valuable insights.

  1. Data Collector: Called the data ingestion layer, this part collects information from different sources, like your company's databases, spreadsheets, or even external data providers. It makes sure the data is clean and organized.

  2. Business Context: Known as the secondary model engine, this is the "brain" of the system that understands your specific business and industry. It's like an expert in your field that knows the rules, regulations, and best practices. Underpinned by the general intelligence of the AI Models Layer, it uses this knowledge to make sense of the data and provide relevant insights tailored to your organization's needs.

  3. AI Models: The AI Models Layer is the general intelligence of the system. It includes a collection of advanced AI models, each trained to perform specific tasks or provide unique insights. For example, language models like GPT-4o could be used for natural language processing, computer vision models for image recognition, and predictive analytics models for forecasting and anomaly detection. These AI models analyze the data, identify patterns, and generate predictions, recommendations, or answers to complex questions. By leveraging this robust AI Models Layer, the system can deliver intelligent insights, automate workflows, and empower users to make more informed and data-driven decisions.

  4. Connector: This integration layer connects the system to your existing tools and software, like your customer relationship management (CRM) system or enterprise resource planning (ERP) system. It makes sure the data flows smoothly between systems.

  5. User Interface: This is the part that you interact with. It provides a user-friendly way to access the insights, reports, and recommendations from the system. You can use it to make informed decisions, track progress, and optimize your business.

  6. Security and Governance: This part ensures that the system is secure, reliable, and compliant with industry regulations. It protects your data and ensures that only authorized people can access the system.

Dr. Lisa built in partnership with DiagramGPT by Eraser

How it Works Together

These elements work together to create specific, valuable outcomes for organizations who have been savvy enough to build niched solutions. The data collector gathers information, the brain of the system provides context and expertise, the AI models analyze the data, the connector integrates with your existing systems, and the user interface provides insights and recommendations. The security and governance layer ensures that everything runs smoothly and securely. Bottom line, it's like having a customized team of experts working for you, 24/7!

Challenges and Limitations of Secondary Models

While secondary models offer a powerful way to drive value and efficiency in specific domains, their implementation is not without its challenges and limitations. Implementing a secondary model is like building a custom home. It requires careful planning, precise execution, and attention to detail. While the end result can be a beautiful, tailored solution, the process can be complex and time-consuming.

One of the primary challenges is the need for high-quality, industry-specific data to train the secondary model engine. This can be a time-consuming and resource-intensive process, particularly for organizations with complex or legacy systems. Additionally, secondary models require ongoing maintenance and updates to ensure they remain relevant and effective, which can be a significant undertaking. Furthermore, the complexity of secondary models can make them difficult to interpret and explain, which can lead to trust and adoption issues among stakeholders. Finally, secondary models are not a silver bullet, and their effectiveness can be limited by factors such as data quality, model bias, and the complexity of the problem being addressed. Despite these challenges, the benefits of secondary models can be significant, and organizations that carefully plan and execute their implementation can reap substantial rewards.

Getting Started with Secondary Models

Implementing secondary models can significantly enhance the effectiveness and efficiency of AI solutions within your organization by providing context-specific insights and reducing spin-up times. Secondary models act as a bridge, capturing and standardizing industry/agency-specific data, rules, and workflows, which can then be shared across various AI solutions. This approach not only ensures seamless integration but also fosters flexibility and adaptability when adopting new AI technologies. To take advantage of secondary models, follow these steps:

  1. Identify your niche: Determine the specific areas of your organization that require tailored AI solutions.

  2. Partner with experts: Work with AI experts to develop (or buy/tweak) a secondary model that captures the intricacies of your industry or organization.

  3. Integrate with existing systems: Ensure seamless integration with your existing infrastructure, data sources, and workflows.

  4. Monitor and evaluate: Regularly monitor and evaluate the performance of the AI system, making adjustments as needed.

By embracing the concept of the "riches are in the niches" and leveraging secondary models, businesses and government agencies can unlock significant value and drive real results in their specific domains.


Applying these Concepts

Whenever I write, my intent is to make technical AI concepts understandable by non-technical professionals. To that end, below are examples for both an enterprise company and a public sector scenario where I clarify how ONE niched secondary model can be used as a feeder for multiple other AI solutions.

Example 1: Upstream Energy Companies

In the upstream energy sector, which involves the exploration and production of oil and gas, a secondary model can significantly enhance the efficiency and effectiveness of multiple AI solutions. A niched secondary model would capture industry-specific data, rules, workflows, and best practices, ensuring AI systems can quickly adapt and provide accurate insights and recommendations.

Contents of the Niched Upstream Energy Secondary Model

  • Exploration Data: Historical seismic data, geological surveys, and exploration logs.

  • Production Data: Well performance data, production rates, and equipment status reports.

  • Contextual Rules and Policies: Safety regulations, environmental compliance requirements, and operational protocols.

  • Domain-Specific Knowledge: Industry terminology, drilling techniques, reservoir management best practices, and maintenance schedules.

  • Standardized Interfaces: APIs for integrating with exploration and production systems, data management tools, and real-time monitoring systems.

  • Pre-Defined Scenarios: Common exploration and production scenarios, workflows for drilling operations, and emergency response procedures.

How This Secondary Model Can Be Used by Multiple AI Solutions

AI for Exploration Planning

  • Integration: The secondary model integrates with the company's exploration planning system, providing context on seismic data, geological surveys, and exploration history.

  • AI Functionality: The AI analyzes geological data to identify promising drilling locations, assesses potential reserves, and optimizes exploration strategies.

  • Outcome: Improved accuracy in locating resources, reduced exploration costs, and enhanced decision-making in exploration activities.

AI for Drilling Operations

  • Integration: The secondary model connects with the drilling management system, offering insights into drilling techniques, equipment status, and safety protocols.

  • AI Functionality: The AI monitors drilling operations in real-time, predicts equipment failures, and recommends adjustments to drilling parameters to enhance efficiency.

  • Outcome: Increased drilling efficiency, reduced downtime, and enhanced safety during drilling operations.

AI for Production Optimization

  • Integration: The secondary model links with the production monitoring system, incorporating data on well performance, production rates, and reservoir conditions.

  • AI Functionality: The AI analyzes production data to optimize extraction processes, predict maintenance needs, and enhance reservoir management.

  • Outcome: Improved production rates, extended well life, and reduced operational costs.

AI for Environmental Compliance

  • Integration: The secondary model interfaces with environmental monitoring systems, providing details on regulatory requirements, emission levels, and compliance records.

  • AI Functionality: The AI monitors environmental impact, ensures compliance with regulations, and predicts potential environmental risks.

  • Outcome: Enhanced regulatory compliance, reduced environmental impact, and better management of environmental risks.

Benefits of Using a Shared Secondary Model

  • Consistency: Ensures uniform application of industry standards and best practices across different AI solutions.

  • Efficiency: Reduces the need for each AI solution to develop and maintain separate models, saving time and resources.

  • Interoperability: Facilitates seamless integration and data sharing between different systems and AI solutions.

  • Scalability: Allows for easy adaptation and expansion of AI capabilities as new technologies and operational needs emerge.

A well-crafted secondary model for upstream energy companies can significantly enhance the capabilities of multiple AI solutions, improving efficiency, safety, and compliance across exploration and production activities. By leveraging a shared model, upstream energy companies can achieve greater operational efficiency, reduce costs, and enhance their ability to respond to industry challenges, ultimately driving better outcomes and value in their operations.

Example 2: Emergency Response in Public Safety Agencies

A well-designed secondary model for emergency response can be highly versatile and utilized by multiple AI solutions across different public safety agencies. This model would capture critical data, rules, workflows, and best practices relevant to various emergency scenarios, ensuring that AI systems can quickly adapt and provide accurate insights and recommendations.

Contents of the Niched Emergency Response Secondary Model

  • Emergency Incident Data: Historical data on various emergency incidents, response times, outcomes, and lessons learned.

  • Contextual Rules and Policies: Emergency response protocols, safety regulations, and compliance requirements.

  • Domain-Specific Knowledge: Terminology, best practices for emergency management, and procedures for different types of emergencies (e.g., natural disasters, fires, medical emergencies).

  • Standardized Interfaces: APIs for integrating with emergency management systems, communication tools, and geographic information systems (GIS).

  • Pre-Defined Scenarios: Common emergency scenarios, workflows for response coordination, and decision-making processes.

How This Secondary Model Can Be Used by Multiple AI Solutions

AI for Fire Department Dispatch

  • Integration: The secondary model integrates with the fire department’s dispatch system, providing context on fire incidents, protocols, and resource allocation.

  • AI Functionality: The AI analyzes incoming emergency calls, prioritizes incidents, and recommends the optimal deployment of fire units based on historical data and current conditions.

  • Outcome: Faster and more efficient response times, better resource management, and improved safety for both firefighters and the public.

AI for Medical Emergency Response

  • Integration: The secondary model connects with the medical emergency response system, offering insights into medical emergencies, patient triage protocols, and hospital capacities.

  • AI Functionality: The AI assists in triaging emergency calls, directing ambulances to the nearest available hospitals, and providing real-time updates to medical teams en route.

  • Outcome: Enhanced triage accuracy, reduced patient wait times, and better coordination of medical resources.

AI for Natural Disaster Management

  • Integration: The secondary model links with disaster management systems, incorporating data on natural disaster patterns, evacuation plans, and recovery protocols.

  • AI Functionality: The AI predicts potential disaster impacts, optimizes evacuation routes, and coordinates resources for disaster relief efforts.

  • Outcome: Improved disaster preparedness, efficient evacuation processes, and effective post-disaster recovery.

AI for Law Enforcement Emergency Response

  • Integration: The secondary model interfaces with law enforcement systems, providing details on crime patterns, response strategies, and communication protocols.

  • AI Functionality: The AI analyzes emergency calls related to criminal activities, prioritizes responses, and recommends tactical approaches based on situational analysis.

  • Outcome: Quicker response to criminal incidents, better situational awareness, and enhanced public safety.

Benefits of Using a Shared Secondary Model

  • Consistency: Ensures uniform application of emergency response protocols across different agencies.

  • Efficiency: Reduces the need for each agency to develop and maintain separate models, saving time and resources.

  • Interoperability: Facilitates seamless integration and data sharing between different emergency response systems.

  • Scalability: Allows for easy adaptation and expansion of AI solutions as new emergency scenarios or technologies emerge.

A well-crafted secondary model for emergency response can be a powerful tool, enhancing the capabilities of multiple AI solutions across various public safety agencies. By leveraging a shared model, these agencies can achieve greater efficiency, consistency, and interoperability in their emergency response efforts, ultimately leading to better outcomes for the communities they serve.

Unlock the Full Potential of AI in Your Organization

I hope that I've successfully explained the power of tailored AI models in terms that leave you excited to apply this tech in your environment! If you're ready to turn AI potential into tangible business value, my team helps leaders like you to succeed with AI. Here are areas where we love to help you:

  • Pursue your first AI win

  • Improving operations with AI

  • Driving revenue with AI

  • Improving government services with AI

  • Upskilling Executives and Board Directors about AI

  • Inspiring and educating your team on the power of AI through interactive keynote speaking

Don't let AI potential go unrealized. Contact us today to take the first step towards unlocking the full potential of AI in your organization!

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