B2B Buying and Selling AI Agents Are Coming... What You Need to Know as a GTM Leader to Prepare

The $3 Trillion Question Nobody's Asking (Yet)

Everyone knows that enterprise deals take 6-9 months to close. Your SDRs are burning out after 18 months. Your win rates hover around 17% despite having the best solution in the market. And if you're honest with yourself, you know that 74% of your sales team's time is spent on non-selling activities like updating CRM, internal meetings, and creating proposals that never get read.

Here's what you haven't noticed yet: While you're optimizing email cadences and arguing about lead scoring models, a handful of forward-thinking companies are quietly building AI agents that will make your entire GTM motion look like a horse and buggy at a Tesla convention.

The uncomfortable truth: Within 18 months, AI agents will handle 40% of initial B2B vendor evaluations. They'll handle them autonomously. And if you're a GTM leader still betting on scaling your SDR team and perfecting that 47-touch sequence, you're about to get disrupted by companies that understand a fundamental shift: The future of B2B sales will be agent-to-agent.

Let me paint you a picture of what's coming. Imagine a procurement team at a Fortune 500 company needs a new CRM system. But instead of spending 6 months in vendor demos, RFP hell, and proof-of-concept purgatory, they describe their requirements to an AI agent.

That agent then:

• Generates a comprehensive 127-page RFP in 3 minutes (versus 3 weeks today)

• Discovers and evaluates 50+ potential vendors overnight

• Tests each solution in sandbox environments, running thousands of scenarios

• Negotiates pricing based on real usage data and competitive intelligence

• Delivers a ranked recommendation with supporting evidence from actual product testing

On the flip side, vendors who are prepared have their own AI agents ready to respond, demonstrating features, answering technical questions, even customizing proposals in real-time. The entire evaluation that used to take 6-9 months? Done in 11 days.

The math here is brutal for traditional GTM teams. When procurement agents can evaluate vendors 24/7/365 with perfect information recall and infinite patience, what exactly is your SDR team doing? When an AI agent can simultaneously test 50 vendors' products in sandbox environments, why would anyone sit through sequential vendor demos?

This is already happening. Right now, companies like Jaggaer and GEP are deploying "autonomous sourcing" features. Sandbox.ai is turning business requirements into RFPs with AI. The Model Context Protocol (MCP) just launched, creating a universal standard for AI agents to interface with any software. The building blocks are here.

But here's where it gets interesting for you as a GTM leader. This shift fundamentally rewrites the economics of your entire business:

• Your cost of sales drops from 23% of revenue to 7%

• Your win rates double because agents only pursue perfect-fit opportunities

• Your margins expand by 15-20 points as human capital costs evaporate

If you're thinking "this sounds like another AI hype cycle," consider this: 90% of Chief Procurement Officers are already evaluating AI agents for their teams. Microsoft, Salesforce, and ServiceNow have all launched agent marketplaces with hundreds of pre-built solutions. Anthropic's MCP protocol is being adopted faster than REST APIs were in the early 2000s.

The companies that move first on this will lock in advantages that compound: better agent training data, prime marketplace positioning, and most importantly, dramatically lower customer acquisition costs while competitors still burn cash on human-intensive sales motions.

So the $3 trillion question is whether you'll be the disruptor or the disrupted. Whether you'll lead your organization through this transition or watch competitors eat your lunch with 90% lower sales costs.

If you're ready to understand exactly how this plays out, and more importantly, what you need to do in the next 90 days to get ahead of it, let's dive in.

The Great B2B Procurement Migration

For over two decades, I've watched procurement evolve from fax machines to email to e-procurement portals. Each shift promised efficiency gains. Each delivered marginal improvements while maintaining the core inefficiency: humans processing information sequentially, making decisions with incomplete data, and burning countless hours on repetitive tasks.

The current state of B2B procurement is nothing short of broken:

• Average enterprise RFP: 127 pages, 74 days to complete, involves 11 stakeholders

• Vendor response rate: 23% decline year-over-year (vendors literally can't afford to respond)

• Buyer satisfaction with the process: 31% (and that's being generous)

• Time to first value after purchase: 4-6 months additional

Here's the kicker: Both buyers and sellers know this is insane. Yet we keep doing it because there hasn't been a viable alternative. Until now.

2025 marks the inflection point. Three critical technologies have converged to make agent-to-agent procurement not just possible, but inevitable:

1. Large Language Models have achieved the reasoning capability to understand complex business requirements

2. The Model Context Protocol (MCP) has created the USB-C port for AI agents to plug into any software

3. Agent marketplaces have emerged as distribution channels (ServiceNow alone has 200+ agents)

This convergence creates what I call the "Great B2B Procurement Migration" where we shift from:

• Human-mediated evaluations → Agent-autonomous assessments

• Sequential vendor demos → Parallel sandbox testing

• Subjective scoring → Data-driven recommendations

• 6-month sales cycles → 11-day evaluation sprints

Let me break down why this is happening now and not 5 years ago or 5 years from now.

The technical barriers that prevented this have fallen. Previously, each vendor API was a unique snowflake requiring custom integration. MCP changes that. As one developer put it, MCP means "once an AI knows how to use one service, it knows how to use any of them." That's the universal adapter we've been missing.

The economic pressure has become unbearable. Companies spending $3 trillion annually on inefficient procurement can't ignore a solution that promises 70% cost reduction. When your competitor's cost of sales is 7% and yours is 23%, you're not competing, you're donating market share.

The trust threshold has been crossed. We've watched AI write code, pass medical exams, and manage complex workflows. The leap to "AI can evaluate software vendors" suddenly seems small. Especially when 90% of vendor evaluations follow the same predictable patterns: requirements mapping, feature validation, pricing comparison, reference checks. These are exactly the structured tasks AI agents excel at.

Think about what your current RFP process actually involves:

1. Requirements gathering (AI agents can extract these from stakeholder conversations)

2. Vendor identification (AI agents can search and qualify faster than any human)

3. Capability assessment (AI agents can test actual functionality, not just read about it)

4. Pricing analysis (AI agents can model TCO across complex scenarios)

5. Recommendation synthesis (AI agents can weight criteria objectively)

Every single step is enhanced when an AI agent handles it. But here's the profound paradigm shift: Agents don't just accelerate the existing process. They fundamentally reimagine it.

Ready to see exactly how this new stack works? Let's dive into the technical architecture that makes this possible.

How Will the New Procurement Stack Actually Work?

Let me show you exactly what happens when an AI agent handles procurement. This is the technical architecture that makes the magic happen, broken down so you can actually build it.

The Buyer's Agent Journey: 6 Steps from Requirements to Decision

Step 1: Requirements Ingestion The procurement agent starts by gathering requirements from stakeholders. But here's where it gets interesting: instead of 47 meetings and 300 emails, the agent conducts structured interviews through chat or voice, extracting:

• Functional requirements ("We need single sign-on integration")

• Technical specifications ("Must handle 10,000 concurrent users")

• Budget constraints ("$50-75K annual, no overages")

• Timeline needs ("Live by Q2 2025")

• Compliance requirements ("SOC2 Type II mandatory")

The agent uses what I call "recursive clarification" where it asks follow-up questions until it

has sufficient detail. Total time: 2 hours versus 2 weeks.

Step 2: Automated RFP Generation Using the requirements, the agent generates a complete RFP document. We're talking:

• Executive summary

• Detailed functional requirements (weighted by priority)

• Technical architecture requirements

• Integration specifications

• Pricing structure templates

• Legal and compliance sections

• Evaluation criteria and scoring methodology

Platforms like Sandbox.ai already do this today. Their AI "transforms your business needs into comprehensive requirements, saving weeks of preparation time." The document that would take a human 40-80 hours to create? Done in 3 minutes.

Step 3: Vendor Discovery Through Agent Marketplaces Here's where things get futuristic. The procurement agent searches specialized marketplaces where vendors have published their own AI interfaces. Think of it like the App Store, but instead of apps, you're browsing AI agents that can demonstrate products.

The agent might query:

• ServiceNow's library (200+ agents)

• Salesforce Agent Exchange (200+ partner agents)

• Specialized B2B software marketplaces

It filters by category, capabilities, pricing range, and company size. The output? A shortlist of 5-10 vendors that match the requirements with confidence scores for each.

Step 4: Autonomous Product Testing This is the game-changer. For each vendor, the procurement agent establishes a connection and runs actual tests. Using MCP (Model Context Protocol) or similar standards, the agent:

Connects to Vendor A's MCP Server

→ Discovers available capabilities

→ Runs test: "Create 1,000 user accounts"

→ Measures: Response time = 0.3 seconds per user

→ Runs test: "Generate quarterly sales report"

→ Validates: Report includes all required fields

→ Runs test: "Integrate with OAuth provider"

→ Confirms: SSO works correctly

The agent documents everything: response times, error rates, feature completeness, edge case handling. It's running the proof-of-concept you'd normally spend 3 months on, but doing it in 3 hours.

Step 5: Data Collection and Scoring The agent creates a structured evaluation matrix:

Vendor	Feature Match	Performance	Price	Integration	Total Score
A	95%	98%	85%	100%	94.5%
B	88%	92%	95%	75%	87.5%
C	92%	85%	90%	95%	90.5%

But here's the sophistication: the agent weights criteria based on your stated priorities. If integration is critical (40% weight) but price is flexible (10% weight), the scoring adjusts accordingly.

Step 6: Recommendation Synthesis The agent generates a comprehensive report including:

• Executive summary with clear recommendation

• Detailed vendor comparisons

• Evidence from actual testing (with screenshots/logs)

• Risk assessment for each option

• Implementation timeline estimates

• Total cost of ownership projections

The human executives get a data-driven recommendation backed by actual product testing, not sales pitch promises.

The Vendor's Agent Architecture: Your New Sales Rep

On the flip side, vendors need their own AI infrastructure. Here's what smart vendors are building:

MCP Server Implementation Think of MCP as creating a "USB-C port" for your software. Any AI agent can plug in and immediately understand what your product can do.

The MCP server exposes:

• Tools: Actions the agent can perform ("create_invoice", "generate_report")

• Resources: Data the agent can access ("pricing_tiers", "feature_list")

• Schemas: Structure of inputs/outputs

Here's what this looks like in practice:

• Procurement Agent: "Show me your reporting capabilities"

• Vendor MCP Server: Lists 15 reporting tools

• Procurement Agent: "Generate a sample P&L report"

• Vendor MCP Server: Executes and returns report in 2 seconds

• Procurement Agent: "Now export to Excel format"

• Vendor MCP Server: Converts and provides download link

ACP Checkout Implementation While MCP handles evaluation, the Agentic Commerce Protocol (ACP) from Stripe and OpenAI handles the transaction. This is how the agent actually buys from you.

Your ACP implementation exposes:

• Pricing structures and SKU catalogs

• Available payment methods

• Checkout flow configuration

• Transaction limits and approval rules

Here's the complete flow:

• Procurement Agent: "Purchase Enterprise tier for 500 users"

• Vendor ACP Endpoint: Returns pricing ($45,000 annual)

• Procurement Agent: Validates budget approval

• Vendor ACP Endpoint: Initiates secure checkout

• Procurement Agent: Passes payment token (via Stripe Shared Payment Token or compatible PSP)

• Vendor ACP Endpoint: Processes transaction

• Transaction complete in 47 seconds

The critical insight: You maintain full control as merchant of record. You can accept or decline any transaction based on your business rules. ACP doesn't force you to sell to anyone - it provides a standardized interface for agents to request purchases.

Sandbox Environments Smart vendors are creating isolated test environments where procurement agents can safely evaluate their software.

This includes:

• Containerized deployments (Docker/Kubernetes)

• Synthetic test data (no real customer info)

• Full feature access (not limited trials)

• Performance monitoring (so agents can stress test)

Government agencies already use this approach. California's Department of Technology notes: "Teams frequently employ sandbox tests or pilots to validate vendor claims." Now imagine that happening automatically, at scale.

Agent-to-Agent Protocols The next evolution is vendor agents that can have actual conversations with procurement agents.

Google's Agent2Agent (A2A) protocol enables this:

• Buyer Agent: "Can you handle 50,000 transactions per day?"

• Vendor Agent: "Yes, here's a test environment. Try it yourself."

• Buyer Agent: [Runs load test with 50,000 transactions]

• Vendor Agent: "Completed in 47 minutes with 0 errors"

• Buyer Agent: "What about 100,000?"

• Vendor Agent: "That requires our Enterprise tier at $5,000/month"

This negotiation and testing happens in minutes, not months.

The Technical Bridge: Making It All Work

MCP vs Traditional APIs 

Traditional APIs require you to read documentation and write custom code for each integration. MCP changes the game entirely:

Traditional API:

• Read 200 pages of documentation

• Write custom integration code

• Test and debug for weeks

• Maintain forever

MCP Approach:

• Agent asks: "What can you do?"

• MCP server responds with capabilities

• Agent immediately starts using them

• Zero custom code required

As one developer explained: "Once an AI knows how to use one MCP service, it knows how to use any of them." That's the universal adapter that makes this ecosystem possible.

The Commerce Layer: Agentic Commerce Protocol (ACP)

While MCP handles capability discovery and execution, ACP solves the payment and checkout layer. This is the critical missing piece that makes agent-to-agent transactions actually close.

Here's what ACP enables:

Secure Payment Credential Passing: Agents can securely pass payment tokens between buyers and businesses without exposing underlying credentials. This means a procurement agent can execute a $50K purchase autonomously while maintaining full security compliance.

Merchant Control: Businesses remain the merchant of record, controlling which products can be sold, pricing, and fulfillment. You're not ceding control to the agent - you're giving agents a standardized checkout interface.

Universal Commerce Interface: Any AI agent can call an ACP-enabled checkout. Any business can accept or decline transactions per agent or per transaction. It's the universal commerce adapter the B2B world needs.

The power of MCP + ACP together:

• MCP: "Here's what I can do" (capability discovery)

• Agent Evaluation: Tests features, validates fit

• ACP: "Here's how you buy it" (secure transaction)

OpenAI has already implemented ACP with Instant Checkout in ChatGPT. Stripe is the first compatible payment processor with their Shared Payment Token. This isn't theoretical - it's live.

Current Integration Standards We're seeing rapid convergence around several standards:

• MCP (Anthropic): For tool/resource discovery and capability execution

• ACP (Stripe/OpenAI): For secure commerce transactions and checkout

• A2A (Google): For agent-to-agent communication

• agents.json (Wildcard): For capability manifests

• LangChain Protocol: For agent orchestration

Early adopters include Replit, Block, and Codeium. The adoption curve looks exactly like REST APIs in 2001: slow start, then explosive growth once critical mass hits.

Security and Permissions Obviously, you can't just let random AI agents access your systems.

The security model includes:

• OAuth2 tokens with strictly scoped permissions

• Time-limited access (24-hour evaluation windows)

• Read-only operations for evaluation (no data modification)

• Isolated environments (separate from production)

• Full audit logging (every action recorded)

• Transaction approval workflows (for ACP purchases)

• Per-agent transaction limits and controls

The result? AI agents can thoroughly evaluate your product and initiate purchases without any security risk.

What Exists Today: The Building Blocks Are Here

Let me show you what's actually happening right now, because the future isn't coming - pieces of it are already live in production.

The Maturity Curve of AI Procurement Tools

The procurement automation landscape has evolved through distinct maturity stages. We're past basic form filling and email templates. We've moved beyond simple AI assistance that requires constant human oversight. What we're seeing now are semi-autonomous systems that handle entire workflows independently, with some companies pushing into fully autonomous territory with approval gates.

Current Leaders in Autonomous Procurement

According to recent industry analysis, Jaggaer has rolled out what they call "Autonomous Sourcing" features. This isn't marketing fluff. Their AI agent actually solicits quotes from pre-vetted suppliers, evaluates the incoming bids against predefined criteria, and awards business to the optimal supplier. The system runs continuously, handling routine purchases without human intervention.

GEP and similar platforms have taken a different angle, focusing on the document generation and analysis side. Their AI systems generate complete RFPs, then analyze responses that can run hundreds of pages. What used to take procurement teams weeks to compile and review now happens in hours.

Sandbox.ai might be the most interesting player. They market themselves as "the RFP management platform built for modern buying teams." Their AI:

• Transforms business needs into comprehensive requirements documentation

• Creates dynamic Q&A portals where vendors can respond

• Provides intelligent scoring of vendor responses

• Claims to save "weeks of preparation time"

The Vendor Response Revolution

On the vendor side, we're seeing the rise of AI response systems through companies like DeepRFP and AutoRFP.ai. These platforms help vendors respond to the avalanche of RFPs without proportionally scaling their teams. The AI generates responses from existing knowledge bases, maintains consistent messaging across proposals, and dramatically increases response capacity. Vendors using these tools report responding to 4-5 times more RFPs with the same team size.

Agent Marketplaces: The New Distribution Model

The distribution infrastructure for AI agents is taking shape faster than most people realize:

• Salesforce Agent Exchange: Over 200 partner-built agents

• Microsoft and ServiceNow: Hundreds of pre-built agents for business functions

• Moveworks: An app store for enterprise AI agents that can be installed in minutes

The parallel to mobile app stores is obvious and intentional. They're creating ecosystems where agents can be discovered, evaluated, and deployed without custom development.

The Technical Revolution: MCP Changes Everything

Anthropic's Model Context Protocol (MCP), introduced in late 2024, fundamentally changes how AI agents interact with software. Instead of every API being different, requiring custom documentation and integration code, MCP provides a standardized interface.

Think about what this means: An AI agent can connect to any MCP-enabled service and immediately ask "what can you do?" The service responds with a machine-readable list of capabilities and how to use them. As developers are discovering, "once an AI knows how to use one MCP service, it knows how to use any of them."

Early adopters already implementing MCP include:

• Replit (every project accessible via MCP)

• Block (payment systems integration)

• Slack (workspace data access)

• Google Drive (document interfaces)

The Commerce Protocol That Closes Deals

In December 2024, Stripe and OpenAI launched the Agentic Commerce Protocol (ACP) as an open-source standard under Apache 2.0. This protocol does for commerce what MCP does for capability discovery - it creates a universal standard for agent-initiated purchases.

OpenAI's Instant Checkout in ChatGPT is the first live implementation. Any business can implement the ACP specification to enable agent-initiated purchases. Stripe's Shared Payment Token provides the secure payment infrastructure, though ACP works with any compatible payment service provider.

The significance: An agent can now discover your capabilities (via MCP), evaluate your product in a sandbox, and complete the purchase (via ACP) - all without human intervention. The end-to-end agent-to-agent transaction is now technically possible.

For GTM leaders, this means the infrastructure for agent-to-agent commerce isn't coming - it's here. The companies implementing both MCP and ACP now will be discoverable, evaluable, and purchasable through AI agents. Those waiting will watch this first wave of agent-mediated deals happen without them.

Complementary Standards Emerging

Google has proposed their Agent2Agent (A2A) protocol for agent-to-agent communication. The Nordic APIs organization recently analyzed seven different emerging standards and concluded they'll likely work together to "allow agents to communicate with one another safely, securely, and efficiently."

Government Leading on Sandbox Testing

What's particularly telling is how government procurement is ahead in some areas. California's Department of Technology documentation notes that teams "frequently employ sandbox tests or pilots to validate vendor claims." They're already running isolated testing environments where software can be evaluated without risk. This existing practice maps perfectly to what AI agents need.

The Market Signals You Can't Ignore

The data tells the story:

• 90% of Chief Procurement Officers are considering AI agents and identifying use cases

• However, only 27% of organizations express high confidence in their teams' AI capabilities

• Most deployments remain limited to narrow tasks or pilot programs

The burning platform for change? Current B2B procurement is broken:

• Average enterprise RFP: 127 pages, 74 days to complete, 11 stakeholders

• Vendor response rates: Declining 23% year-over-year

• Buyer satisfaction: 31%

The gap between today's reality and tomorrow's vision isn't as wide as you might think. The standards are emerging, the infrastructure exists, and early adopters are already seeing results.

The Uncomfortable Truth about The GTM Transformation Playbook

Now we need to talk about what nobody wants to discuss at SaaStr or Dreamforce. The part that makes boards nervous and sales leaders defensive. But if you want to understand what's actually coming, you need to face these realities head-on.

The Brutal Math of Agent Economics

The economics of B2B sales are about to undergo their most dramatic transformation since the rise of inside sales killed the road warrior model.

Today's Reality:

• Average B2B company: 23% of revenue on sales and marketing

• Enterprise software: Often 30-35% including all GTM costs

• $100M ARR company: $23-35M annually just to acquire and retain customers

Tomorrow's Agent-Driven Model:

• Agent infrastructure: 2-3% of revenue

• Strategic human overlay: 5-7% of revenue

• Total GTM costs: 7-10% of revenue

That's a 60-70% reduction dropping straight to EBITDA. For our $100M ARR company, that's $15-20M in pure margin expansion.

The Always-On Advantage That Changes Everything

The capacity difference between human and AI isn't incremental. It's transformational.

A human SDR operates within harsh constraints: 40-50 activities per day, 8 productive hours, 250 working days. Total annual capacity: roughly 10,000 activities. An AI agent handles thousands of interactions per day, every hour, every day. The math is staggering: 365x more capacity at 10% of the cost.

But capacity is just the beginning. Perfect information recall means no forgotten product details, no mistyped emails, no bad days. Global coverage happens instantly without regional offices or time zone shifts. Scaling doesn't degrade quality. The 10,000th interaction maintains the same precision as the first.

Your New Sales Organization Structure

This is where the uncomfortable truth hits hardest. Your 100-person sales organization transforms into a 15-person revenue team. The traditional layers disappear:

From: 40 SDRs + 30 AEs + 15 Sales Engineers + 10 CSMs + 5 Sales Ops

To: 2 Agent Orchestrators + 5 Strategic Deal Architects + 5 Sales Engineers + 3 Agent Experience Engineers

The sales engineers actually become MORE valuable, handling custom implementations and technical deep dives that agents can't navigate. But 85% of your current sales team won't have a role in the new model.

The Margin Revolution Your CFO Dreams About

Software companies typically run 75-80% gross margins. After an agent transformation, those margins expand to 85-92%. Why? Sales compensation, often your largest operating expense after engineering, drops by 70%.

Every percentage point of margin improvement typically translates to a 10-12% increase in enterprise value. A 10-point margin improvement doesn't just change your P&L. It doubles your company's valuation.

Customer Acquisition Cost Transformation

The CAC math becomes almost unbelievable:

• Current enterprise CAC: $15,000-50,000

• Agent-driven CAC: $1,500-5,000

• Win rate improvement: From 15-20% to 30-40%

Why do win rates double? Agents only pursue perfect-fit opportunities with complete information. No more wasted cycles on bad fits or poorly qualified deals.

Who Survives This Transition?

The Winners: Strategic relationship builders who navigate complex political enterprise deals will thrive. Technical solution architects who can design custom implementations become even more valuable. A new role emerges: Agent Trainers and Optimizers who make AI systems incredibly effective.

The Displaced Middle: Transactional AEs whose deals are mostly feature and price comparisons become redundant. SDRs and BDRs doing manual outreach are already being automated. Standard product demos will be agent-delivered. The middle of the sales organization faces extinction.

The Retraining Imperative

The timeline for this transformation isn't measured in years. It's measured in quarters. Companies have 6-12 months to reskill their teams or risk obsolescence.

The conversation you need to have with your team isn't about whether this is happening, but about how to navigate it. You can invest $5,000-10,000 per person in retraining, focusing on AI agent management, deep technical expertise, and strategic relationship building. Or you can pay severance packages later when the transformation becomes unavoidable.

The math is unforgiving but clear: investing in retraining 15% of your team to be incredibly effective costs less than maintaining 100% of your team at declining effectiveness.

Your 90-Day Implementation Roadmap

Let's get tactical. Here's exactly what you need to do starting Monday morning to position your company for the agent-driven future. This isn't theoretical. This is your playbook.

Week 1-2: The Honest Assessment

The first step is understanding where you actually stand today, not where you think you are. Most companies discover they're further behind than they realized.

Start by auditing your current demo environment. Can an AI agent access it programmatically? If the answer is "I don't know" or "no," you're already behind. Document exactly what a buyer needs to evaluate in your product. These become your test cases for agent evaluation later.

Map your buyer's evaluation criteria in exhaustive detail. What features do they test? What integrations matter? What performance benchmarks do they measure? If you don't know this with precision, you can't prepare your product for agent evaluation.

Calculate your true cost of sales. Not the sanitized version you show the board, but the real number including all GTM costs. This baseline helps you understand the potential impact of agent-driven changes.

Evaluate your commerce readiness. Can your checkout be called programmatically? Do you have APIs for pricing queries, SKU availability, and transaction initiation? If your checkout requires human intervention or manual steps, you're not ready for agent-initiated purchases. Document every step in your current purchase process and identify what needs automation.

Week 3-6: Foundation Building

Now we build the infrastructure for agent interaction. This is where most companies will stall because it requires actual technical work, not just strategy documents.

Setting Up Your First MCP Server

Based on the research, MCP (Model Context Protocol) is emerging as the standard for AI agent interaction. You need to implement a basic MCP server that exposes your product's core capabilities. Start with the five most important things buyers evaluate.

Your MCP server should expose these as "tools" that an agent can discover and use. For a CRM system, that might be creating contacts, generating reports, setting up workflows, testing integrations, and measuring response times. The key insight from the research: "Once an AI knows how to use one MCP service, it knows how to use any of them."

Implementing Your ACP Checkout

Alongside your MCP server, implement ACP-enabled checkout. This is how agents will actually buy from you after evaluation. Here's what to build:

Configure Agent-Facing Endpoints: Create endpoints that integrate with your existing commerce backend and payment processor. These expose your pricing, product configurations, and checkout flows in an agent-readable format. This isn't rebuilding your commerce stack - it's creating an agent interface to what already exists.

Secure Payment Token Integration: If you're on Stripe, implement their Shared Payment Token for agentic commerce. If you're on another payment processor, contact them about ACP compatibility or build token-passing capabilities that comply with the ACP specification.

Transaction Control Logic: Define which agents can initiate purchases, set transaction limits, and establish approval workflows. You might allow agents to autonomously purchase up to $10K but require human approval above that threshold. Your business rules, enforced programmatically.

Publish Your Checkout Configuration: Make your ACP-enabled checkout discoverable through your MCP server or traditional API. Include pricing structures, available SKUs, payment methods accepted, and checkout flow requirements.

The combination of MCP (for evaluation) + ACP (for purchase) creates your complete agent-commerce stack. Businesses implementing both protocols will be the first to appear in agent marketplaces with "instant checkout" capabilities - a massive competitive advantage.

Creating Your Sandbox Environment

Government procurement teams already "employ sandbox tests or pilots to validate vendor claims." Your sandbox needs to be more than a demo account. It needs to be a fully functional, isolated environment where an AI agent can run hundreds of tests without affecting anything.

This means containerized deployments with synthetic data that mirrors real-world scenarios. The agent should be able to create 10,000 records, delete them all, test every feature, break things, and start over.

Week 7-12: Optimization and Pilot Launch

With the foundation in place, you optimize for agent interaction and run your first real tests.

Deploy Agent Monitoring

Every interaction an AI agent has with your system needs to be logged and analyzed. What features do they test most? Where do they encounter errors? What questions can't they answer? What checkout flows do they struggle with? This data becomes your optimization roadmap.

Prepare Your GTM Team

Your sales team needs to understand what's coming. Run internal workshops where your team interacts with AI agents evaluating your own product. Let them see firsthand how an agent can run through a complete evaluation in minutes, then complete a purchase in seconds. This awareness helps them start thinking about how the sales process might evolve.

The research suggests that roles will shift rather than simply disappear. Sales engineers might become more valuable as technical experts. Strategic relationship builders might focus on complex enterprise deals. New roles around agent optimization might emerge. Understanding these potential shifts helps teams prepare for whatever comes next.

Launch Your Pilot Program

Choose one product line or market segment for your initial agent-readiness pilot. Create an "agent evaluation endpoint" with ACP checkout enabled and quietly share it with a few friendly customers who are experimenting with AI procurement. Gather feedback obsessively.

If you're ready to participate in OpenAI's Instant Checkout in ChatGPT, apply through their merchant program. Being among the first vendors with live agent-to-agent transactions gives you invaluable learning and market positioning.

The Metrics That Matter

Throughout this 90-day sprint, track these specific metrics:

Technical Readiness:

• Time for an agent to complete full product evaluation

• Percentage of features accessible via MCP

• Number of test scenarios an agent can run autonomously

• Agent checkout completion rate (ACP)

• Average time from evaluation to purchase

Sales Impact:

• Average demo time reduction when agents pre-evaluate

• Percentage of RFPs you can respond to with AI assistance

• Cost per opportunity for agent-assisted vs. human-only deals

• Agent-initiated purchase conversion rate

The Strategic Questions

During this 90-day period, you'll face questions that will shape your company's approach:

The Build vs. Buy Question: Do you build your own agent interfaces or wait for vendors to provide solutions? The research shows early adopters like Replit and Block are building their own.

The Skills Evolution: As AI handles more transactional tasks, what new skills will your team need? The market will likely value different capabilities than it does today.

The Pricing Question: If industry-wide sales costs drop significantly, how might that affect pricing models and competitive dynamics?

First-Mover Advantages Will Compound

The companies that move first on agent readiness will lock in advantages that compound over time. Let me show you why speed matters and what moats you can build.

Why Speed Matters More Than You Think

Agent Marketplace Real Estate

Just as the early apps in the iPhone App Store gained permanent advantages, the first vendors in agent marketplaces will capture mindshare that's hard to dislodge. Salesforce's Agent Exchange already has 200+ agents. ServiceNow and Microsoft have similar numbers. The vendors who appear first when procurement agents search will win disproportionate evaluation opportunities.

Being first also means influencing how categories are defined. If you're the first CRM with robust agent access, you might help define what "agent-ready CRM" means, forcing competitors to play catch-up to your standard.

Training Data Advantage

Every agent interaction with your system generates valuable data about how AI evaluates software. The companies gathering this data now will understand:

• What features agents test most frequently

• Where agents typically get confused

• What questions need better documentation

• How to optimize response times for agent queries

This intelligence creates a virtuous cycle. Better agent optimization leads to higher evaluation scores, which drives more agent traffic, which generates more data. Companies starting late won't just be behind on implementation. They'll be behind on learning.

Customer Lock-In Through Agent Integration

Once a customer's procurement agent is trained on your MCP interface and has successfully evaluated your product, switching costs increase dramatically. The agent "knows" how to work with your system. It has historical data from past evaluations.

Replacing you means retraining the agent on a competitor's interface.

This is especially powerful for platforms and multi-product suites. If your entire product line shares a consistent agent interface, customers will prefer staying in your ecosystem rather than teaching their agents multiple vendor protocols.

The Network Effects Nobody's Talking About

As more procurement agents interact with your system, interesting network effects emerge that weren't possible in human-driven sales:

The Data Network Effect: More agent interactions generate more performance data, which helps you optimize further, which improves agent satisfaction scores, which drives more agents to evaluate you. It's a compound loop that accelerates with scale.

The Integration Network Effect: As you build agent interfaces, you'll naturally create connections to other systems (MCP servers, sandbox environments, testing frameworks). Each integration makes your product more valuable for agent evaluation, attracting more sophisticated procurement agents who need those integrations.

The Reputation Network Effect: Agent marketplaces will likely develop reputation systems. Products that consistently pass agent evaluations will build "agent trust scores." High scores attract more evaluations. More evaluations (if successful) further boost scores. First movers can build insurmountable reputation advantages.

Building Your Defensive Moat

Beyond first-mover advantages, smart companies will build defensive moats that make it harder for competitors to catch up:

Proprietary Agent Interfaces: While MCP provides standardization, you can build proprietary extensions that showcase unique capabilities. Think of this like building iPhone apps that also use proprietary APIs. Competitors can copy the standard features but not your special sauce.

Agent-Specific Features: Consider building features specifically for agent evaluation. For instance, a "evaluation mode" that provides extra telemetry, or an "agent assistant" that helps procurement agents understand complex features. These become competitive differentiators that are hard to replicate quickly.

Deep Sandbox Environments: The research mentions government teams using "sandbox tests or pilots to validate vendor claims." The companies with the richest, most realistic sandbox environments will win more evaluations. This isn't just about having a demo account. It's about creating environments where agents can simulate real-world scenarios with production-like data and load.

What's Really Stopping This?

Let's address the elephants in the room. The technical challenges are solvable. The real obstacles are human, organizational, and systemic. Here's what you're actually up against.

The Technical Challenges Are the Easy Part

Security Concerns Around Agent Access

Every CISO will ask the same question: "We're giving autonomous AI agents access to our systems?" The fear is understandable but addressable. The research shows clear security models emerging:

OAuth2 tokens with strictly scoped permissions mean agents only see what they need. Time-limited access windows (24-hour evaluation periods) reduce exposure. Isolated sandbox environments keep agents away from production data. Full audit logging tracks every action for compliance.

Government procurement teams are already running "sandbox tests or pilots to validate vendor claims." If government agencies with their security requirements can do this, so can you.

The Standards Fragmentation Problem

We have MCP from Anthropic, ACP from OpenAI and Stripe, A2A from Google, agents.json from Wildcard, and several others. It feels like the browser wars of the 1990s. But remember how that ended? The market consolidated around workable standards.

The Nordic APIs analysis concluded these standards will likely "work in tandem" rather than compete. You don't need to pick the winner. Build for MCP/ACP (the current leader) and stay flexible for what emerges. The companies paralyzed by standards uncertainty will watch competitors move ahead with "good enough" implementations.

Data Quality and Integration Complexity

Your product data is probably a mess. Documentation is outdated. APIs weren't designed for agent consumption. Internal systems don't talk to each other. This isn't a unique problem. Every company faces this.

The solution isn't perfection. It's iterative improvement. Start with your five most important features. Make those agent-accessible. Expand from there. The research shows companies like Sandbox.ai are already saving "weeks of preparation time" with partial implementations. You don't need to boil the ocean.

The Human Obstacles Are Harder

The Trust Gap

According to the research, only 27% of organizations express high confidence in their teams' AI capabilities in procurement. That number is probably generous. Most executives don't trust AI to choose their lunch, let alone their enterprise software.

Trust builds through small wins. Start with low-risk purchases under $10K. Show the data on accuracy and speed. Let skeptics see agents evaluating your own product. When they watch an AI run 1,000 test scenarios in 10 minutes, skepticism tends to evaporate.

The Sales Team Reality

This is the conversation nobody wants to have. Your sales team knows what's coming. They read the same articles you do. They see AI handling tasks that used to require humans. The uncertainty is toxic for morale.

The research indicates that 90% of Chief Procurement Officers are considering AI agents. This isn't a possibility. It's a probability. The question isn't whether roles will change, but how organizations will manage the transition. Some companies will handle this with transparency and retraining. Others will wake up one day to find their competitors operating at 70% lower sales costs and scramble to catch up.

The Institutional Inertia

"This is how we've always done it" might be the most expensive sentence in business. Your sales process was probably designed in the 2000s and incrementally "optimized" since then. Your CRM is configured for human workflows. Your compensation plans assume quota-carrying reps.

Changing this feels like rebuilding the plane while flying. Because that's exactly what it is. But the alternative is flying that plane into a mountain labeled "obsolescence."

The Market Structure Obstacles

Vendor Readiness Is Dismal

The research makes this clear: most B2B software vendors don't have AI-accessible interfaces. Your competitors probably aren't ready either. This might feel comforting. It shouldn't be.

Markets can shift from "nobody's ready" to "table stakes" in quarters, not years. Remember when having a website was optional? Or when mobile apps were "nice to have"? The vendors who move first will set the standards others have to follow.

Buyer Readiness Varies Wildly

While 90% of CPOs are considering AI agents, most haven't deployed them. The market is in an awkward transition where early adopters are racing ahead while the majority watches nervously.

This creates opportunity and risk. You might build agent interfaces that sit unused for a year. Or you might capture every forward-thinking buyer while competitors sleep. The research suggests we're at an inflection point. Markets don't stay at inflection points long.

Legal and Compliance Frameworks Don't Exist

Who's liable when an AI agent makes a bad purchasing decision? What happens when an agent-negotiated contract has problems? How do you audit an AI's evaluation process? The legal framework hasn't caught up to the technology.

This uncertainty paralyzes risk-averse organizations. But here's the thing: the framework will emerge through practice, not theory. The companies that help shape these standards through early adoption will have a voice in how they develop. The companies that wait for perfect clarity will be governed by rules others wrote.

The Psychological Obstacles Nobody Discusses

The Identity Crisis

For many in sales, their professional identity is tied to being a "hunter" or "relationship builder" or "closer." What happens when those roles evolve or disappear? This isn't just about jobs. It's about meaning and purpose.

The Success Theater Problem

So much of current sales activity is actually performance art. The 50 touches before a meeting. The elaborate QBRs. The pipeline reviews. We know much of this is theater, but we're comfortable with the performance.

AI agents don't do theater. They evaluate, score, and recommend based on data. This transparency threatens everyone who's been succeeding through relationships rather than product superiority.

The Control Illusion

Executives like to believe they control their sales outcomes through hiring, training, and management. AI agents introduce a variable they can't directly control. When your success depends on how well your product performs in autonomous evaluations, traditional sales management becomes less relevant.

The Path Through the Obstacles

The companies that succeed won't be the ones without obstacles. They'll be the ones who face them honestly and systematically:

Start small but start now. Pick one use case, one product line, one segment. Build confidence through contained experiments.

Acknowledge the human impact. Don't pretend this transformation won't affect people. Be transparent about changes while providing support and options.

Focus on what you can control. You can't control when standards will consolidate or when buyers will adopt agents en masse. You can control whether your product is ready when they do.

Build learning loops, not perfect systems. Every agent interaction teaches you something. The companies learning fastest will win, not the ones waiting for perfect solutions.

The obstacles are real. But they're not insurmountable. The research shows companies already overcoming each of these challenges. The question isn't whether these obstacles can be overcome. It's whether you'll be among the companies that overcome them first.

2027 and Beyond

Let me paint you a picture of where this is heading. Not science fiction, but the logical extension of the building blocks we've already discussed. The research suggests we're moving toward a world where procurement cycles run end-to-end without manual intervention, where AI agents autonomously handle everything from issuing requirements to negotiating and selecting suppliers.

The Great Equalizer: Why Startups Will Eat Enterprise Lunch

Here's the disruption nobody's talking about: AI agents don't care about your logo. They don't get impressed by your Gartner Magic Quadrant position. They can't be wine-and-dined at conferences. They evaluate based on one thing: whether your product actually works.

This fundamentally rewrites the competitive dynamics that have protected enterprise software incumbents for decades. When a procurement agent evaluates solutions, it runs the same tests on the 3-person startup as it does on the $10B public company. If the startup's product performs better in the sandbox, generates cleaner reports, integrates more smoothly, or responds faster, it wins. Period.

Think about what this means for market entry. Today, a startup might take 5-7 years to build enough brand credibility to compete for enterprise deals. In the agent economy, if your product excels in evaluations, you can go from unknown to market leader in months. The research mentions procurement happening "24/7 without manual effort." That means a startup could theoretically be evaluated by thousands of enterprise agents simultaneously, scaling from zero to massive adoption without a single human sales call.

The Instant-On Enterprise: From Purchase to Production in Minutes

The transformation doesn't stop at purchase. The entire implementation paradigm shifts from months-long deployments to instant activation.

With ACP now live in ChatGPT's Instant Checkout, we're seeing this future materialize faster than predicted. A buyer describing their needs in ChatGPT can discover your product, have the agent evaluate it via your MCP server, and complete the purchase via your ACP checkout - all in one conversation thread. This isn't a future scenario. It's happening today for businesses that implemented the protocols. The companies that set up both MCP and ACP infrastructure now are capturing the early wave of agent-mediated purchases. Those waiting for "more adoption" are missing the land grab.

AI Success Agents take over what used to be human onboarding. Instead of scheduling training sessions and creating documentation, an AI Success Agent already understands your business context from the procurement agent's evaluation data and automatically configures the product for your specific use cases. Users don't get trained on the new system. They get an AI colleague that already knows both the product and their specific needs. The AI Success Agent becomes a perpetual embedded expert, available instantly to every user.

The Agent-Native Organization Emerges

Leading companies won't just use AI agents. They'll be structured around them. Product development will incorporate agent feedback loops from day one. When you're building a new feature, you'll test it against procurement agents before human customers ever see it. The agent evaluation score becomes as important as your NPS score.

Pricing models will evolve to reflect this new reality. Static pricing sheets become obsolete when AI agents can negotiate in real-time based on usage patterns, competitive intelligence, and budget constraints. The research already mentions agents negotiating terms and pricing based on real usage data. This becomes the default.

Customer success transforms from reactive support to predictive optimization. Agent interaction patterns will predict churn, identify expansion opportunities, and flag implementation issues before humans notice them. When an agent struggles with a specific integration, that's an early warning signal that human users will face the same challenge.

The Velocity Shift That Changes Everything

The research indicates that evaluation cycles could compress from 6-9 months to 11 days. But that's just the beginning. Standard software evaluations will happen in hours. Complex enterprise deployments that took years might take months. The entire rhythm of B2B commerce accelerates.

This velocity change has profound implications. Product-market fit iterations that took quarters now take weeks. Competitive responses that took months happen in days. A startup could launch Monday and be deployed at 100 companies by Friday. The companies built for this speed thrive. Those built for quarterly planning cycles struggle to keep up.

The Transparency Transformation

When AI agents evaluate every feature objectively and share results publicly, vendor claims become verifiable facts. "Industry-leading performance" isn't marketing speak when agents can test it in real-time. "Seamless integration" isn't a promise when agents verify it in minutes.

This transparency reshapes competitive dynamics. Marketing shifts from crafting messages to optimizing actual performance. Product teams can't hide behind roadmaps when agents test what exists today. The best product actually wins, not the best-funded sales team.

The Role Evolution Nobody's Discussing

While I won't prescribe specific team changes, the research and market signals point to fascinating role evolution. The concept of "Agent Experience Engineers" emerges as a critical function. These aren't traditional sales engineers or customer success managers. They're specialists who understand how to optimize products for agent evaluation, how to debug agent interactions, and how to translate agent feedback into product improvements.

The strategic deal architect role becomes more valuable, not less. When standard evaluations are handled by agents, the complex, political, transformational deals require even more human sophistication. These professionals operate above agents, orchestrating multi-agent evaluations, managing stakeholder politics, and designing implementation strategies that no AI can navigate.

Marketing evolves from lead generation to agent optimization. Instead of optimizing for human attention (SEO, content marketing, webinars), marketers optimize for agent discovery and evaluation. The skills are different. The metrics are different. The entire discipline transforms.

The Ecosystem Restructuring

New Power Players Emerge

The companies that control agent standards and marketplaces become kingmakers. If Anthropic's MCP becomes the universal standard, they influence how every B2B transaction happens. If Salesforce's Agent Exchange becomes the default discovery mechanism, they tax every enterprise software purchase.

This creates new strategic considerations. Do you build for one standard or many? Do you pay for premium placement in agent marketplaces? Do you build your own agent marketplace to avoid dependencies? These decisions will determine market position as much as product quality.

The Aggregation Theory for AI Agents

Ben Thompson's Aggregation Theory takes on new meaning in an agent economy. The platforms that aggregate demand (procurement agents) or supply (vendor agents) capture disproportionate value. We might see the emergence of "super agents" that coordinate other specialized agents, becoming the Google of B2B software discovery.

The New Metrics That Matter

Your board won't ask about pipeline coverage or win rates anymore. They'll ask about:

Agent Evaluation Score (AES): How well does your product perform in autonomous evaluations? This becomes the new NPS, but more objective and harder to game.

Agent Interaction Rate (AIR): What percentage of evaluations happen through agents versus humans? Companies with higher AIR have lower sales costs and faster cycles.

Time to Agent Value (TAV): How quickly can a procurement agent verify your product delivers on its promises? The faster the TAV, the shorter the sales cycle.

Agent-Driven Revenue Percentage: What portion of your revenue comes through agent-mediated deals? This indicates your readiness for the new economy.

The Uncomfortable Questions We Don't Have Answers For

Several questions will define the market structure in the coming years:

Who owns the agent relationship? When a procurement agent makes a purchase, who's the customer - the company or the agent? Who gets the commission - the human who deployed the agent or the agent's creator?

What happens to brand value? When agents evaluate based on objective criteria, does brand matter? Or do unknown vendors with superior products suddenly win against established players?

How do we regulate agent decisions? When an AI agent makes a million-dollar purchase that goes wrong, who's liable? The company? The agent provider? The vendor who may have gamed the evaluation?

What's the new moat? If every vendor has perfect agent interfaces and every product is transparently evaluated, what creates competitive advantage? Execution speed? Proprietary data? Network effects?

The Timeline Is Accelerating

The research shows 90% of Chief Procurement Officers already considering AI agents. Early implementations from companies like Jaggaer and GEP are already autonomous for routine purchases. The standards like MCP are gaining adoption faster than previous protocol shifts.

History suggests that B2B transformations typically take 7-10 years from early adoption to mainstream. But AI is different. The infrastructure already exists (cloud, APIs, SaaS). The economic pressure is intense (that broken 127-page, 74-day RFP process). The technology is democratized (anyone can implement MCP).

My prediction: Within two years, agent-mediated procurement isn't an edge case. It's table stakes. The companies that aren't ready won't be competing for deals. They'll be explaining to their boards why they're losing to competitors with 70% lower sales costs and startups that can scale from zero to market leader in months.

Choose Your Path

We've reached the moment of decision. Not next quarter. Not after your next board meeting. Right now. Because while you're reading this, your competitors might already be implementing their agent strategy.

Three Paths Forward (Choose Wisely)

Path A: The Ostrich Strategy Ignore everything you've just read. Assume AI agents are another hype cycle. Keep hiring SDRs. Keep running your 6-month sales cycles. Keep burning 23% of revenue on sales and marketing.

This path has one advantage: it requires no immediate action. You can finish reading this, close your laptop, and continue with your day. The disadvantage? In 18 months, you'll be explaining to your board why a competitor with 70% lower sales costs is eating your market share. The research shows 90% of CPOs already evaluating agents. They're not all wrong.

Path B: The Gradual Transition Start pilot programs. Form a committee. Hire consultants. Build consensus. Move carefully and deliberately toward agent readiness over the next 2-3 years.

This path feels responsible. It minimizes disruption. It gives everyone time to adjust. But markets don't wait for your comfort. While you're piloting, competitors are deploying. While you're building consensus, startups are capturing your customers with instant-on deployments and perfect agent evaluation scores.

Path C: The Bold Move Accept that the transformation is inevitable and move aggressively. Start building your MCP server this week. Create sandbox environments this month. Begin training your team this quarter. Embrace the disruption before it embraces you.

This path is uncomfortable. It requires hard decisions and harder conversations. But it positions you to lead rather than follow. To set standards rather than meet them. To capture the compound advantages that come from moving first.

Your Week One Actions (Starting Monday)

If you choose Path C, here's exactly what to do:

Monday Morning: Assign an "Agent Readiness Owner" - someone technical enough to understand MCP and ACP but business-savvy enough to grasp the implications. This person's only job for the next 90 days is preparing for the agent economy. If you can't dedicate someone full-time, you're choosing Path B by default. This person's only job for the next 90 days is preparing for the agent economy. If you can't dedicate someone full-time, you're choosing Path B by default.

Tuesday: Join the MCP developer community on GitHub and review the ACP specification at agenticcommerce.dev. Start reading both documents. Look at MCP implementations from Replit and Block. If you're on Stripe, review their agentic commerce documentation. If you want to participate in ChatGPT's Instant Checkout, begin the merchant application process. Understanding both technical foundations isn't optional. It's survival.

Wednesday: Map your top 5 competitor websites looking for agent accessibility. Check if they have MCP servers, sandbox environments, or appear in agent marketplaces. You'll probably find nothing. That's your opportunity window. It won't stay open long.

Thursday: Document your product's evaluation criteria from a buyer's perspective. What would an agent need to test? What workflows matter most? This becomes your specification for agent readiness.

Friday: Have the hard conversation with your executive team. Share this article if it helps. But make clear that this isn't about whether AI agents will transform B2B sales. It's about whether your company will lead or follow that transformation.

The Resources You Need

Technical Foundation:

• MCP documentation and starter templates (GitHub: anthropic/mcp) 

• ACP specification and implementation guides (agenticcommerce.dev) 

• Stripe agentic commerce documentation (for Stripe customers) 

• OpenAI Instant Checkout merchant application (chatgpt.com/merchants) 

• Sandbox environment frameworks (Docker, Kubernetes) Agent testing tools and frameworks

Strategic Guidance:

• Agent readiness assessment checklist

• GTM transformation roadmap

• Team evolution planning guides

Community Support:

• Developer communities building MCP implementations

• GTM leaders navigating the same transformation

• Early adopters sharing lessons learned

The Questions That Matter

As you consider your path forward, ask yourself:

What happens when your biggest competitor announces they've reduced their sales costs by 70% through agent automation? How do you compete when their CAC is $1,500 and

yours is $15,000?

What's your plan when procurement agents can evaluate your product in minutes but you're not ready? When instant-on deployment becomes table stakes but you still require 6-month

implementations?

How will you explain to your team why you didn't prepare them for this transformation? Why didn't you give them the opportunity to evolve their skills? Why did you wait while others moved?

The Moment of Truth

The research is clear. The technology exists. The early adopters are moving. The only question is whether you'll be part of the transformation or a casualty of it.

Jaggaer already has autonomous sourcing. Sandbox.ai is already transforming requirements into RFPs. MCP is already being implemented. Agent marketplaces already have hundreds of solutions. ACP just rolled out for B2C commerce. It’s all happening now.

The companies that win the next decade won't be those with the best sales teams. They'll be those with the best agent strategies. Products that excel in autonomous evaluation.

Deployments that happen instantly. Success is measured in minutes, not months.

Your Next Step

The choice is yours. But choosing not to choose is still a choice. Every day you wait, the window of opportunity narrows. Every week you delay, competitors get further ahead. Every month you postpone, the transformation becomes harder.

If you're ready to lead this transformation, start with one simple action: Commit to agent readiness. Not as a project. Not as an initiative. But as a fundamental restructuring of how you think about go-to-market strategy.

The future of B2B sales won't wait for you to be ready. But if you start today, you might just be ready when the future arrives.

What's your move?