AI leadership

First and foremost, we have a duty to our clients to do what's in their best interest. The majority of organizations can benefit hugely from integrating AI into their operations. If that's not the case for you, we'll tell you so. 

Assuming you're a good candidate for some sort of AI solution, our first step is a deep dive to understand your operations. We'll look at where you have inefficiencies that could be streamlined with AI, as well as where AI could unlock advanced capabilities to give you an edge. We'll then hone in on what seems like the most low-hanging fruit, using our proprietary APEX (AI Prioritization and eXecution) framework.

The APEX process starts with a cluster of potential initiatives and scores each against five criteria in three successive rounds of elimination. 

APEX evaluates every potential AI project through five dimensions:

1. Most pressing needs: These are the squeakiest wheels, the biggest pain points, the highest priorities in the minds of key stakeholders.
2. Biggest impact: Which issues, if resolved, would drive revenue and/or increase margins most?
3. Technical feasibility: What can current AI/ML tech reasonably accomplish? We look for “slam dunks” and steer clear of “meh” outcomes.
4. Cost and complexity: What will it actually take to deploy a solution, both in money, time, and other resources?
5. Strategic alignment. Does this initiative move you toward total organizational intelligence or create another silo?

Rather than drowning you in an analysis of every possibility, APEX uses competitive elimination:

Tier 1: Initial evaluation. We assess 10-15 opportunities, including your current initiatives and new possibilities that emerge from stakeholder conversations. Each gets scored on our five dimensions. The math is transparent. You see why certain initiatives advance.

Tier 2: Semi-finalist analysis. The top 3-5 opportunities receive deeper analysis. We explore implementation approaches, refine cost estimates, and identify dependencies. This gives you backup options and alternative paths.

Tier 3: Winner deep dive. The highest-scoring initiative gets comprehensive analysis. You receive precise cost projections, step-by-step deployment plans, resource allocation schedules, and risk assessments. This is your roadmap.

If you want to proceed with the winning initiative, Talbot West can deploy it for you, whether it involves an out of the box SaaS product or a custom ensemble we design and build for you.

Talbot West also provides a wealth of other AI-related services, including AI education and training, AI workshops, AI governance, and more. 

See our solutions page for the full scope of what we offer. 

When we look at the impact AI can have on an organization, Talbot West looks at four swimlanes of value:

1. Process optimization: Reduce costs and increase revenue by using AI to do things more efficiently. 
2. Data activation: Leverage proprietary datasets for competitive advantage.  
3. Product/service augmentation: Enhance existing products or services with AI and machine learning technologies.
4. Intelligence fusion: Correlate multiple data sources or feeds to extract intelligence and create a common operational picture.

Let's look at each of these in more detail. 

Here's the revised version with cleaner, more direct prose:

1. Process optimization

Process optimization targets the mundane friction that drains organizational energy. Every company has workflows that consume excessive time, require unnecessary handoffs, or depend on manual steps that machines could handle better.

Take invoice processing. A typical enterprise might have staff manually entering data from PDFs, matching purchase orders, routing for approvals, and handling exceptions. AI can read invoices regardless of format, match them against orders, route based on intelligent rules, and flag only genuine anomalies for human review. What took hours becomes minutes.

The compound effect proves powerful. Each optimized process frees human capacity for higher-value work. Errors decrease. Cycle times shrink. Costs drop. The organization becomes more agile, able to adapt processes rather than living with inefficiency until the next major system overhaul.

2. Data activation

Data is the new gold, but only if the value is extracted. Imagine a rich vein of gold on your land in which fine gold particles are mixed with soil in a 10/90 ratio (10% gold). You can't take a wheelbarrow of this soil to the bank and pay off your mortgage. The gold needs to be extracted and refined. Similarly, data has to be processed and insights extracted from it. Talbot West turns raw data into something incredibly valuable.

Organizations sit on years of accumulated data that remains as useless as unrefined ore. The value exists but stays locked away, inaccessible to those who could benefit from it.

Data activation applies the refining process. A manufacturer might discover that minor temperature fluctuations predict equipment failures days in advance. A retailer could identify micro-segments of customers with unique purchasing behaviors. A healthcare provider might find correlations between unrelated factors that improve patient outcomes. These insights—the refined gold—drive decisions that create competitive advantage.

3. Product/service augmentation

AI enhances what organizations offer their customers. Products become smarter. Services become more responsive. Customer experiences become more personalized and valuable. We may even discover adjacent products or services that can be added at low marginal cost for new revenue streams.

These augmentations create competitive moats. Customers experience tangible benefits that make switching costs higher. Premium pricing becomes justified by superior capabilities. New revenue streams emerge from AI-enabled features that customers willingly pay for.

The augmentation approach reduces risk. Rather than replacing existing offerings, AI enhances them incrementally. Organizations can test, learn, and refine without betting everything on unproven technologies.

4. Intelligence fusion

Intelligence fusion correlates multiple data sources to create unified operational awareness. Organizations operate with fragmented visibility—sales sees one picture, operations another, finance a third. Critical insights hide in the gaps between these views.

Intelligence fusion creates a common operational picture by ingesting and correlating diverse data streams. Market signals, operational metrics, financial indicators, and external data sources combine to reveal patterns invisible to siloed analysis.

An airline might correlate weather forecasts, aircraft maintenance schedules, crew availability, and booking patterns to optimize operations days in advance. A retailer could combine point-of-sale data, inventory levels, social media sentiment, and economic indicators to predict demand shifts before they materialize.

This convergent intelligence enables proactive rather than reactive management. Organizations see problems forming rather than discovering them after impact. They identify opportunities while windows remain open. They coordinate responses across departments rather than through lengthy meetings and email chains.

Firing on all cylinders

The true power emerges when all four swimlanes work together. Optimized processes generate clean data. Activated data improves products. Enhanced products create new data streams. Converged intelligence identifies new optimization opportunities. Each capability amplifies the others, creating compound advantages that ultimately lead to total organizational intelligence.

Want to explore how AI could fit into your operations? Let's discuss your specific challenges and opportunities.

In medieval Europe, the Talbot was known as a harbinger—one who announces or signals what is to come. Like the Talbot, we help organizations see around corners and prepare for emerging technologies that will reshape their operations.

"West" speaks to the pioneering spirit of American expansion. Just as the western frontier represented both challenge and opportunity, artificial intelligence opens new territories for enterprise innovation. We guide organizations through this technological frontier, helping them stake their claim in the AI landscape while avoiding common pitfalls.

Together, these elements—the foresight of the Talbot and the pioneering spirit of the West—define our approach to AI implementation.

Cognitive Hive AI (CHAI) is a configurable, adaptable, modular swarm architecture for complex use cases, such as are found in the defense, manufacturing, and healthcare industries. 

For many basic business AI needs, a standalone product—from ChatGPT to any of the thousands of AI tools, platforms, and tools available commercially—gets the job done. It's a matter of finding the right tool and plugging it in. But there are many environments in which no product is sufficiently configurable, adaptable, explainable, and agile. For these use cases, it's more helpful to approach AI implementation as the assembling of a capabilities set rather than deploying a tool. 

With Cognitive Hive AI, we build an ensemble of capabilities to match the needs of the client. A CHAI instance can incorporate any number of module types, from large language models to small language models to quantitative engines to various types of machine learning, IoT integrations, and data streams. These collaborate together toward a common goal, whether that goal is finding a cure for cancer, or monitoring gray zone activities by U.S. adversaries. 

Because CHAI is modular, individual modules can be updated, replaced, or fine-tuned without disrupting the entire system. As AI improves and new capabilities come online, the CHAI ensemble can incorporate the latest. It's more akin to an evolving organism than a monolithic product. 

If you'd like to explore what a CHAI implementation might look like for your use case, contact us to discuss. 

Explainability is crucial because AI systems that make opaque decisions create unacceptable risks in business and government operations. When AI makes decisions about loans, medical diagnoses, or defense operations, organizations must understand how those decisions were reached.

Cognitive Hive AI addresses this through its modular architecture:

• Each AI module handles specific tasks with clear, traceable decision paths
• Organizations can examine how individual components contribute to final outputs
• The system maintains audit trails showing exactly why decisions were made
• Human operators can intervene at specific points when needed

Unlike black-box AI systems, CHAI's explainability enables:

• Regulatory compliance in highly regulated industries
• Detection and correction of biased outputs
• Clear accountability for AI-driven decisions
• Trust building with stakeholders and end-users
• Effective human oversight of AI systems

This transparency is particularly vital in high-stakes applications where errors could result in financial losses, legal liability, or harm to individuals. The ability to understand and validate AI decisions becomes a fundamental requirement for responsible deployment.

A modular approach to AI offers several strategic advantages over monolithic, black-box systems:

Enhanced security and control

• Deploy components in air-gapped environments when needed
• Control exactly which modules access sensitive data
• Update individual modules without compromising the whole system
• Maintain stricter governance over AI operations

Faster deployment and updates

• Add new capabilities without disrupting existing operations
• Update specific modules to address emerging threats or needs
• Scale individual components based on demand
• Test new modules in isolation before deployment

Lower operational costs

• Run only the modules you need for specific tasks
• Reduce computational requirements compared to monolithic systems
• Deploy on standard hardware rather than specialized infrastructure
• Scale resources efficiently based on actual usage

Better explainability

• Trace decisions through specific modules
• Understand exactly how the AI reaches conclusions
• Maintain clear audit trails for compliance
• Enable effective human oversight

Reduced vendor dependency

• Avoid lock-in to single AI providers
• Mix capabilities from different vendors
• Maintain control of your AI infrastructure
• Replace components without system-wide changes

This modular approach aligns with the Department of Defense's Modular Open Systems Approach (MOSA), ensuring compatibility with emerging standards while maintaining the flexibility to adapt as technology evolves.

A system of systems approach breaks down AI implementation into specialized modules that work together while maintaining independence. Unlike monolithic AI systems that try to solve every problem, this approach lets organizations coordinate multiple AI capabilities—each optimized for specific tasks—into flexible networks that accomplish what no single system could achieve.

Think of it like a hospital network versus a single medical device. While the medical device's components only work within that unified system (if the device breaks down, its components are useless), each hospital in a network maintains independence while contributing to broader capabilities. The same principle applies to AI deployment: instead of relying on one massive model, we deploy specialized AI modules for different functions—fraud detection, risk analysis, pattern recognition, etc.—that maintain independence while working together. This enables rapid updates, clear accountability, enhanced security, and the ability to add new capabilities without disrupting existing operations.

This modular approach also aligns with the Department of Defense's Modular Open Systems Approach (MOSA), which emphasizes defined interfaces and component replaceability. At Talbot West, we implement this through cognitive hive AI (CHAI), which coordinates independent AI modules like specialized bees in a colony—each maintaining autonomy while contributing to emergent and advanced collective capabilities.

When we engage with a client—whether it's the Department of Defense, a municipality, a software company, or an equipment manufacturer—we start with a deep discovery of that client's business processes and chokepoints. We look for where artificial intelligence solutions can make the biggest impact, and we look for which solutions are needed.

If a client's most pressing needs can be solved with an off-the-shelf product, we'll recommend that product. The more complex the use case, however, the more likely it is that a CHAI architecture is needed to address it.