The Model Context Protocol (MCP) Explained: Why This New Standard is the "USB Port" of AI—And Why Your IT Team Needs to Demand It From Every New Software Vendor

A practical guide for business leaders navigating the AI integration landscape

Bryon Spahn

1/29/202617 min read

A practical guide for business leaders navigating the AI integration landscape

You've spent the last eighteen months watching your competitors announce AI implementations while your IT director keeps explaining why "it's complicated." You've sat through vendor demos where every software company claims their AI "seamlessly integrates with your existing systems." You've approved budget for three different AI tools that now operate in perfect isolation from each other and from the business data they actually need to be useful.

Welcome to the N×M integration nightmare—the hidden cost of AI adoption that nobody mentioned in those glossy case studies.

But here's what changed in November 2024: Anthropic released the Model Context Protocol (MCP), and the major players in AI—OpenAI, Google DeepMind, Microsoft, JetBrains—immediately adopted it as the de facto standard for AI integration. Think of it as the moment the tech industry agreed on USB-C for AI systems. After years of proprietary connectors and custom adapters, we finally have a universal port.

If you're a business or technology leader evaluating AI platforms, automation tools, or any software that claims "AI-powered" capabilities, understanding MCP isn't optional anymore. It's the difference between building an AI strategy that scales and inheriting another maintenance burden that your IT team will be managing (and resenting) for the next decade.

Let me show you why this matters to your business—and what questions you need to start asking your software vendors today.

The Integration Problem Nobody Wants to Talk About

Here's the math that should concern every business leader: You have N AI applications and M data sources or tools those applications need to connect with. In the traditional approach, each connection requires custom integration work. That means you're looking at N×M separate integration projects.

Let's make this concrete with a realistic small business scenario:

Your AI stack might include:

AI chatbot for customer service (Application 1)
AI assistant for internal knowledge management (Application 2)
AI-powered analytics dashboard (Application 3)

These tools need to connect with:

Your CRM system (Salesforce, HubSpot, or similar)
Email platform (Google Workspace, Microsoft 365)
File storage (Google Drive, SharePoint, Dropbox)
Internal database (PostgreSQL, MySQL)
Ticketing system (Zendesk, Freshdesk)

That's 3 AI applications × 5 data sources = 15 separate custom integrations.

The Real Cost of Custom Integrations

Let's talk actual numbers, because this is where the "seamless integration" promise falls apart:

For a typical SMB (50-200 employees):

Development cost per custom integration: $15,000-$45,000
Ongoing maintenance per integration (annually): $3,000-$8,000
Total first-year cost for those 15 integrations: $270,000-$795,000
Annual maintenance after year one: $45,000-$120,000

For mid-market companies (200-1,000 employees):

Development cost per integration: $45,000-$125,000
Maintenance per integration (annually): $8,000-$20,000
First-year cost for 15 integrations: $795,000-$2,175,000
Annual maintenance: $120,000-$300,000

These numbers don't include the opportunity cost—the six months your development team spent building integrations instead of building features that differentiate your business.

And here's the killer: Every time you add a new AI tool or switch data platforms, you're back to square one with another round of custom integration projects.

Enter the Model Context Protocol: The USB-C Moment for AI

The Model Context Protocol changes this entire equation. Instead of building custom integrations for every AI-to-data-source combination, you implement MCP once—and suddenly your AI applications can connect to any MCP-compatible data source or tool.

How MCP Actually Works (In Language Business Leaders Can Use)

Think about USB-C for a moment. Before USB-C became standard, your laptop needed different ports for power, external monitors, data transfer, and peripherals. Now you have one universal port that handles everything. The laptop manufacturer doesn't need to know about every possible device you might connect—they just implement USB-C. Device makers do the same. Everything works together.

MCP works the same way for AI systems:

MCP Clients (embedded in AI applications): The AI chatbot, assistant, or analytics tool includes an MCP client that knows how to communicate using the standard protocol. The AI application doesn't need custom code for Google Drive, Salesforce, or your internal database—it just needs to speak MCP.

MCP Servers (expose your data and tools): Your data systems—whether it's Google Drive, Slack, GitHub, your PostgreSQL database, or even proprietary internal systems—run lightweight MCP servers that expose data and functionality through the standard protocol.

The Result: Any MCP-compatible AI application can connect to any MCP server without custom integration work. You implement MCP once on each side, and you're done.

The Economic Reality Check

Let's revisit that 15-integration scenario with MCP:

Traditional approach:

15 custom integrations × $30,000 average = $450,000 first year
Annual maintenance: $82,500

MCP approach:

Implement MCP clients in 3 AI applications: ~$15,000-$30,000 total
Implement MCP servers for 5 data sources: ~$25,000-$50,000 total
Total first-year cost: $40,000-$80,000
Annual maintenance: $8,000-$15,000

Savings: $370,000-$410,000 in first year, $67,500-$74,500 annually thereafter.

But the real value isn't just cost reduction—it's flexibility. When you add a sixth data source or fourth AI application, the incremental cost is one MCP implementation ($5,000-$10,000), not six new custom integrations ($90,000-$270,000).

What MCP Means for Your Business Strategy

Understanding the cost equation is important, but the strategic implications matter more. MCP fundamentally changes how you should think about AI vendor relationships and technology investments.

1. Vendor Lock-In Just Got Weaker

Right now, switching from one AI platform to another is painful because you've invested in all those custom integrations. The AI vendor knows this—it's part of their retention strategy.

With MCP, your data infrastructure speaks a standard protocol. If your current AI chatbot vendor starts underperforming or overcharging, you can evaluate competitors knowing that the switching cost is significantly lower. You're not rebuilding 15 integrations; you're plugging a new MCP client into your existing MCP infrastructure.

What this means for your next vendor negotiation: Ask potential vendors about their MCP roadmap. If they're evasive or dismissive about supporting an open standard, understand that they're betting on lock-in as their retention strategy. That should influence both your decision and your negotiating position on contract terms and pricing.

2. Security and Compliance Get Simpler (If You Do It Right)

Here's a scenario that keeps IT directors awake: You've authorized an AI assistant to access Google Drive for document analysis. What you didn't realize is the custom integration gives that AI access to every folder, every document, every spreadsheet—including the ones with financial data, employee information, and strategic plans.

MCP's architecture puts you back in control:

Centralized Approval: Your organization manages which AI applications can connect to which MCP servers. This isn't happening in some vendor's black box—it's controlled infrastructure you can audit.

Granular Permissions: MCP servers can expose specific functionality without giving blanket access. Your AI assistant might have read access to customer support documents but no ability to touch financial records—even though both are in the same Google Drive.

Audit Trail: Every interaction between an AI client and an MCP server can be logged. When auditors ask "which AI systems had access to customer data in Q3," you have a definitive answer instead of a panicked search through vendor documentation.

Real-world impact: A financial services client recently implemented MCP-based access controls for their AI tools. Their cyber insurance provider reduced premiums by 12% based on the improved governance framework. The annual savings ($43,000) exceeded the implementation cost ($35,000) in the first year.

3. AI Orchestration Becomes Feasible

Most businesses aren't trying to implement a single AI application—they're trying to create workflows where AI assists at multiple points. The customer service chatbot gathers information, the AI assistant drafts a response, the analytics tool identifies patterns in similar requests, and the automation platform triggers follow-up actions.

Without standardized integration, orchestrating this workflow requires custom connectors at every handoff point. With MCP, these AI systems share context through a common protocol.

Practical example: A mid-sized manufacturer implemented an MCP-based AI workflow:

Sales AI assistant records customer requirements in Salesforce
Engineering AI retrieves relevant specs from internal database
Pricing AI pulls cost data and generates quote
All three systems share context through MCP servers

Before MCP: 90 days to implement, six separate integrations, ongoing coordination between three vendor support teams when issues arose.

After MCP: 30 days to implement, three MCP server deployments, single point of troubleshooting.

ROI: $127,000 saved in implementation costs, 60-day faster time-to-value, 40% reduction in quote generation time (translating to faster sales cycles and improved close rates).

The Technical Reality: What Your IT Team Needs to Know

If you're not the person who has to implement this, here's what your technical team will want to understand:

MCP Architecture in Plain English

Transport Layer: MCP supports multiple communication methods. For local processes (AI application and data source on the same server), it uses STDIO (standard input/output). For remote connections (AI in the cloud connecting to your on-premises database), it uses HTTP with Server-Sent Events. Your technical team picks the right transport based on your architecture.

Message Types:

Requests: AI application asks for data or requests an action
Results: Successful response with the requested data
Errors: Something went wrong (with details on what and why)
Notifications: One-way messages that don't require a response

Security Model: The host system (your infrastructure) instantiates clients and approves servers. Nothing connects without explicit approval. This gives your security team the control they need while maintaining the flexibility developers want.

SDKs and Language Support

MCP provides official SDKs in the languages your development team actually uses:

Python (most common for AI/ML work)
TypeScript/JavaScript (for web-based integrations)
C# (maintained in collaboration with Microsoft)
Java and Kotlin (maintained with JetBrains)
Swift (for iOS/macOS integrations)
Go (maintained with Google)

This isn't a situation where you're forced to rewrite existing code in an obscure language just to support the protocol. MCP meets your developers where they are.

Pre-Built Servers for Common Systems

Anthropic and the MCP community maintain pre-built servers for enterprise systems your business likely already uses:

Google Drive, Gmail, Google Calendar
Slack
GitHub, GitLab
PostgreSQL, MySQL
Salesforce
Microsoft 365, SharePoint

For these systems, implementation isn't "build an MCP server from scratch"—it's "configure and deploy a maintained, tested implementation."

What this means for your timeline: A competent development team can deploy pre-built MCP servers for common systems in days, not months. Custom servers for proprietary internal systems take longer, but you're working from established patterns and well-documented SDKs.

Real-World Adoption: Who's Actually Using MCP

When evaluating any new technology standard, the critical question is: "Who's betting their business on this?"

Major Platform Adoption

Anthropic (Creator of Claude AI): Claude Desktop and Claude API were built with native MCP support. This isn't a side project—it's core infrastructure.

OpenAI: Rapidly adopted MCP for ChatGPT integrations, recognizing the N×M problem affects them as much as their customers.

Google DeepMind: Implementing MCP support across their AI platforms, including integration with Google Workspace.

Microsoft: Not only adopting MCP but collaborating on the C# SDK, signaling enterprise commitment.

Development Tool Integration

The development tool market is particularly instructive. These companies compete aggressively, but they've aligned on MCP because the alternative—each building proprietary integration frameworks—creates a worse experience for their shared customer base.

Zed, Replit, Codeium, Sourcegraph: Major coding platforms and AI-powered development environments have implemented MCP support. Developers using AI coding assistants get real-time access to project context through standardized MCP connections.

JetBrains: Collaborating on the Kotlin SDK signals that IntelliJ, PyCharm, and other major IDEs will support MCP-based AI integrations.

Enterprise Early Adopters

Block (formerly Square): Block's CTO explicitly framed MCP as enabling "agentic systems which remove the burden of the mechanical so people can focus on the creative." They're not just using MCP—they're contributing to the ecosystem.

Apollo: Early integration demonstrates viability for data-intensive businesses that need AI systems to work with complex, existing data infrastructures.

What This Adoption Pattern Tells You

When competing platforms align on a standard, it's usually because:

The problem is painful enough that everyone benefits from solving it
The standard is well-designed enough to meet diverse needs
The alternative (fragmentation) is worse for everyone

MCP checks all three boxes. The N×M integration problem affects every AI vendor and every enterprise customer. The protocol is flexible enough to work across different architectures and use cases. And the alternative—dozens of proprietary integration frameworks—is obviously untenable.

The Questions You Should Be Asking Software Vendors

If you're evaluating AI platforms, automation tools, or any software claiming AI capabilities, here's your vendor qualification checklist:

1. "Does your platform support the Model Context Protocol?"

Red flags in the response:

"We have our own proprietary integration framework" (Translation: vendor lock-in is our business model)
"We're evaluating it" (Translation: we're hoping it goes away)
"Our API is just as good" (Translation: we don't understand why you're asking)

Green flags:

"Yes, here's our MCP implementation roadmap"
"We support MCP and can demonstrate it connecting to your existing systems"
"We contributed to the MCP ecosystem" (pre-built servers, documentation, SDKs)

2. "If you don't support MCP yet, when will you?"

This separates vendors who are tracking industry standards from those who are flying blind. Any AI vendor that hasn't evaluated MCP by mid-2025 isn't paying attention to where the market is going.

Acceptable answers:

Specific timeline with milestones
Acknowledgment of MCP importance with credible implementation plan
Interim solutions (standard APIs that can be wrapped with MCP servers) while native support is developed

3. "Can you demonstrate an MCP connection to our existing systems?"

This is where marketing claims meet technical reality. Vendors who truly support MCP should be able to demonstrate connection to common enterprise systems (Google Drive, Salesforce, GitHub, databases) in a proof-of-concept.

What to watch for:

Can they connect to multiple data sources in a single demo?
Do they require custom development, or are they using standard MCP servers?
How long does setup take? (Should be configuration, not programming)

4. "What security controls exist around MCP connections?"

This reveals whether the vendor has thought through the governance implications.

Good answers include:

Approval workflows for new connections
Granular permission controls
Audit logging of all MCP interactions
Compliance with your existing security frameworks

5. "If we switch to a different AI platform, what happens to our MCP infrastructure?"

This is the lock-in question framed constructively. With true MCP support, your data integration work should be portable.

The right answer: "Your MCP servers continue working with any MCP-compatible platform. You'd implement a new MCP client, but your data infrastructure remains unchanged."

The wrong answer: "You'd need to migrate to our integration framework" or any variation that puts you back into custom integration work.

Implementation Roadmap: What 90 Days Actually Looks Like

If you're convinced that MCP should be part of your AI strategy, here's a realistic implementation timeline based on actual deployments:

Phase 1: Assessment and Planning (Weeks 1-3)

Week 1: Current State Analysis

Document existing AI applications and planned additions
Inventory data sources these applications need to access
Identify custom integrations currently in place or planned
Calculate current integration costs (development, maintenance, opportunity cost)

Week 2: MCP Feasibility Assessment

Evaluate which data sources have pre-built MCP servers available
Identify systems requiring custom MCP server development
Assess internal development capabilities vs. external resources needed
Review security and compliance requirements for your industry

Week 3: Vendor Engagement

Query current AI platform vendors about MCP support status
If evaluating new vendors, make MCP support a requirement
Request proof-of-concept demonstrations
Establish timeline expectations with all stakeholders

Deliverable: Business case document showing current integration costs, projected MCP implementation costs, ROI calculation, and risk assessment.

Phase 2: Pilot Implementation (Weeks 4-8)

Week 4: Pilot Scope Definition Select one AI application and 2-3 critical data sources for pilot. Choose systems that:

Have business urgency (fast time-to-value)
Represent broader integration challenges (learnings will transfer)
Have pre-built MCP servers available (reduce implementation risk)

Weeks 5-6: Technical Implementation

Deploy pre-built MCP servers for chosen data sources
Configure security policies and access controls
Implement MCP client in selected AI application (or work with vendor)
Set up monitoring and logging infrastructure

Weeks 7-8: Testing and Validation

Functional testing: Does data flow correctly?
Security testing: Are access controls enforced?
Performance testing: Acceptable latency and throughput?
User acceptance testing: Does it solve the business problem?

Deliverable: Working pilot with documented performance metrics, security validation, and user feedback.

Phase 3: Scaled Deployment (Weeks 9-12)

Week 9: Deployment Planning Based on pilot learnings:

Finalize deployment sequence for remaining systems
Identify any custom MCP server development needed
Establish training requirements for IT and end users
Create runbooks for ongoing operations

Weeks 10-11: Progressive Rollout

Deploy MCP servers for remaining data sources
Integrate additional AI applications
Migrate from legacy custom integrations where applicable
Provide training and support for teams

Week 12: Operations Handoff

Documentation for IT operations team
Monitoring dashboards and alert configuration
Support procedures and escalation paths
Review findings and lessons learned

Deliverable: Production-ready MCP infrastructure supporting current AI applications with capacity for future additions.

What Can Go Wrong (And How to Prevent It)

Problem 1: "Our existing AI vendor refuses to support MCP"

Solution: This is actually valuable information about your vendor's long-term viability. In the short term, you can wrap their API with a custom MCP server (your development team builds the bridge). In the medium term, their refusal to adopt an industry standard should influence your renewal decision.

Problem 2: "Our proprietary internal systems don't have MCP servers"

Solution: This is expected. Custom MCP server development for proprietary systems typically takes 2-4 weeks per system with a competent developer. The investment pays off when you add the second and third AI application that need to connect to that same system—no new integration work required.

Problem 3: "Security team is concerned about AI systems accessing sensitive data"

Solution: This concern is valid and MCP actually makes it easier to address. Implement MCP servers with strict permission controls. AI applications can only access what you explicitly grant. The audit trail gives security teams visibility they never had with custom integrations buried in vendor code.

Problem 4: "Performance isn't meeting expectations"

Solution: MCP's architecture allows for optimization without changing the protocol. Common solutions include caching frequently accessed data, implementing data filtering at the MCP server level (send AI applications only what they need), and choosing appropriate transport mechanisms for your architecture (STDIO for local, HTTP for remote).

The Strategic Opportunity: What Happens After Implementation

Once you have MCP infrastructure in place, several strategic opportunities become feasible that weren't practical before:

1. Rapid AI Experimentation

Before MCP: Evaluating a new AI tool meant calculating 6-8 weeks for integration before you could even test it with your real data.

After MCP: New AI applications plug into existing MCP infrastructure. Proof-of-concept testing happens in days, not months.

Business Impact: Your organization can stay current with AI innovation instead of being locked into aging platforms because "switching would take too long."

2. Multi-Vendor AI Strategy

Before MCP: Standardizing on one AI vendor was practically mandatory because managing multiple vendors meant multiplying integration complexity.

After MCP: Best-of-breed approach becomes viable. Use one vendor's chatbot, another's analytics AI, a third vendor's automation platform—all sharing context through MCP.

Business Impact: You're optimizing for business outcomes instead of minimizing IT complexity. Better tools, better results.

3. Internal AI Innovation

Before MCP: Even if you had ML expertise in-house, integrating custom AI models with business systems required the same painful integration work as vendor solutions.

After MCP: Your data science team builds models that immediately connect to existing business data through your MCP infrastructure.

Business Impact: Competitive differentiation through AI becomes achievable, not just aspirational.

4. Acquisition Integration

Before MCP: Acquiring a company meant dealing with their AI tools, your AI tools, and a nightmare of incompatible integrations.

After MCP: If both organizations use MCP, integration is dramatically simpler. If the acquired company doesn't, you have a clear migration path.

Business Impact: Technology integration complexity stops killing deal value. M&A strategy doesn't have to avoid companies with "incompatible tech stacks."

What This Means for Your Relationship with Axial ARC

At Axial ARC, we've built our practice on a simple principle: translate complex technology challenges into tangible business value. MCP is a perfect example of this principle in action.

We're not selling you an MCP implementation because it's the hot new technology. We're helping you understand whether MCP solves a real problem for your business—and if it does, we're ensuring you implement it in a way that delivers measurable ROI.

Our Approach: Capability Building, Not Dependency Creation

When we engage with a client on MCP implementation, our goal is to transfer knowledge and build internal capability, not create consulting dependency.

What this looks like in practice:

Phase 1: We lead the implementation while your IT team works alongside us. They're not just watching—they're doing the work with our guidance.

Phase 2: Your team leads with us in a support role. We're reviewing their work, answering questions, suggesting optimizations—but they're building the muscle memory.

Phase 3: You're independent. Your team handles day-to-day operations, new MCP server deployments, and optimization. We're available for complex scenarios or strategic planning, but you're not calling us for routine tasks.

Why this matters: Your AI strategy will evolve faster than any consulting engagement can keep pace with. You need internal capability to adapt quickly. We measure our success by your independence, not your ongoing dependence on our services.

The Coast Guard Approach to AI Infrastructure

As a veteran-owned business, we bring a particular perspective to technology infrastructure: readiness matters more than perfection.

The Coast Guard motto is "Semper Paratus"—Always Ready. That doesn't mean having perfect equipment for every possible scenario. It means having systems that work reliably when they're needed, the training to use them effectively, and the judgment to adapt when circumstances change.

Applied to MCP implementation:

Reliable: Build MCP infrastructure that works consistently, not systems that are impressive in demos but fragile in production.

Trained: Your team understands why MCP works, not just how to configure it. When something breaks, they can troubleshoot effectively.

Adaptable: As new AI platforms emerge and business requirements evolve, your MCP infrastructure adapts without requiring wholesale rebuilding.

This approach has served our clients well across technology initiatives, and it's particularly valuable in the AI space where change is constant.

How We Actually Work with Clients

Discovery Engagement (No Cost): 90-minute session where we map your current AI landscape, identify integration pain points, and determine whether MCP addresses your specific challenges. This might conclude with "MCP isn't right for your situation yet"—and that's a valuable outcome.

Technical Assessment (Fixed Fee: $5,000-$8,000): Detailed analysis of your systems, custom MCP feasibility for proprietary platforms, security and compliance requirements, and ROI calculation for your specific scenario. You get a decision-quality assessment whether you work with us further or not.

Pilot Implementation (Typical Range: $25,000-$45,000): One AI application, 2-3 data sources, working proof-of-concept in 6-8 weeks. Your team participates throughout. You prove the concept and build internal knowledge simultaneously.

Scaled Deployment (Project-Based Pricing): Deploy across remaining systems. Scope and pricing based on number of MCP servers, custom development required, and your team's readiness to take ownership.

Ongoing Advisory (Optional): Strategic guidance as your AI landscape evolves. This is genuine advisory work—we're helping you make decisions, not making decisions for you.

Why Veteran-Owned Matters for This Work

Technical consulting is filled with companies that create complexity to justify ongoing revenue. They build systems only they understand, use proprietary frameworks, and structure engagements to maximize dependency.

The military taught us a different approach: build systems that survive their creators. Train people to use them effectively. Document everything. When you rotate to a new assignment, the mission continues without you.

We apply these principles to client work:

Clear documentation that your team can actually use
Standard protocols and open standards, not proprietary frameworks
Knowledge transfer built into every engagement
Success measured by your capability, not our billable hours

Three decades of technical experience taught us how to build sophisticated systems. Military service taught us the discipline to build them right.

The Bottom Line: What Action Makes Sense Now

If you've read this far, you're taking AI integration seriously. Here's how to translate that interest into action:

If you're currently evaluating AI platforms:

Make MCP support a vendor requirement. Ask the questions outlined earlier in this article. Factor MCP availability into your decision criteria—not as the only consideration, but as a meaningful one.

The time to think about integration is before you sign a three-year contract, not eighteen months in when you realize you're locked into a vendor's proprietary ecosystem.

If you already have AI implementations in place:

Audit your current integration approach. Calculate what you're actually spending on custom integrations and ongoing maintenance. Model what MCP adoption would mean for:

Current integration costs
Ability to add new AI tools
Vendor flexibility
Security and compliance posture

You might conclude that MCP migration makes sense. You might decide current integrations work well enough and MCP is a future consideration. Either way, you'll make an informed decision based on your specific situation.

If you're just beginning to explore AI:

You have a significant advantage: you can build on MCP from the start rather than migrating from legacy integrations. Your AI strategy can assume standard protocols rather than working around proprietary limitations.

Focus on understanding what business problems you're trying to solve with AI. Then architect solutions using MCP-compatible components. You'll have more vendor options, lower integration costs, and greater strategic flexibility than competitors who built their AI infrastructure before MCP existed.

In any case: Start the conversation

Technology decisions this significant require multiple stakeholders—business leadership, IT, security, operations. Have that conversation now rather than defaulting to whatever approach your AI vendor recommends (which may or may not align with your long-term interests).

Your Next Steps

Option 1: Self-Directed Assessment Take the frameworks in this article and run your own analysis. Calculate your N×M integration scenario, evaluate vendor MCP support, and determine if this technology addresses your specific challenges.

Option 2: Collaborative Discovery Contact Axial ARC for a 90-minute discovery session where we map your current state and determine whether MCP solves problems you actually have. This is a genuine discovery conversation—we're equally prepared to conclude "not right for you" as "here's how this helps."

Option 3: Deep Technical Assessment If you've already determined that MCP is relevant to your strategy and want detailed analysis of your specific systems, security requirements, and implementation roadmap, we provide comprehensive technical assessments that give you decision-quality information.

Contact us: axialarc.com/contact

The Model Context Protocol isn't going to solve every AI challenge your organization faces. It's not a silver bullet, it won't replace sound strategy, and it certainly won't compensate for poor vendor selection.

What MCP does is solve one very specific, very expensive problem: the N×M integration nightmare that makes AI implementation costly, fragile, and strategically limiting.

If that's a problem your organization is facing—or will face as your AI strategy matures—then MCP represents a meaningful shift in how you should think about AI architecture, vendor relationships, and technology investments.

The industry has given us a standard protocol just as AI adoption is accelerating. That timing isn't coincidental—it's recognition that AI without integration is just expensive software that doesn't actually know anything about your business.

Make sure your next AI implementation gives you the connection and context you're paying for.