So here we are. July 2025. And if you’re a business leader still thinking of AI as a “feature,” you’re about to get lapped, maybe twice.
ICONIQ just dropped their 2025 State of AI report, and let me tell you, this is the roadmap if you’re trying to stay relevant over the next decade. It’s not about ChatGPT integrations anymore. It’s about operational AI. Scalable AI. Profitable AI.
Let’s break it down. First, forget “AI-enabled.” That’s like putting a turbo sticker on a lawnmower. The smart companies? They’re going full AI-native. Meaning: their entire business model, from pricing to infrastructure to hiring, is built around AI. Almost half of those companies already hit scaling stage. That’s insane speed.
Second—costs. Everyone’s hyped about GenAI, but inference and API costs are killing margins. Think $2M/month level killing. The shift to usage-based pricing isn’t optional; it’s survival. Subscription models are breaking under the weight of power users.
Now here’s the kicker—agentic workflows. These are not glorified chatbots. They’re AI employees. And 80% of high-growth firms are already deploying them. If your SaaS doesn’t have an agent roadmap, someone else’s agent is coming for your customer.
But it’s not all buzzwords and burn rates. The real edge? Internal productivity. Smart companies are doubling their AI budgets just to make their teams more efficient. Think developer copilots, knowledge bots, automated documentation. This is the Trojan horse that brings AI in the front door, not just through the product team.
Bottom line? This isn’t 2010 SaaS. It’s 2030 business, showing up early.
If you’re in the C-suite, ask yourself:
- Are we pricing intelligence, or bundling it like Wi-Fi at a hotel?
- Do we own our AI stack, or are we just renting APIs?
- Are we deploying agents—or waiting to be disrupted by one?
Because here’s the truth: AI isn’t coming. It’s already setting up shop. Either you’re building with it, or competing against those who are.
And that’s not just good strategy. That’s survival. That’s why you read, THE COMUNICANO!!!
Andy Abramson
🔟 THE ANDY ANALYSIS: THE TEN MOST IMPORTANT BUSINESS TAKEAWAYS
1. AI Is Reshaping the Business Operating Model
- 80% of high-growth companies are deploying AI agents and agentic workflows.
- AI-native firms are outpacing AI-enabled peers: 47% of AI-native products are already scaling.
- Internal AI usage is no longer experimental—most firms use it for R&D, sales, and knowledge management.
2. Multi-Model & Open-Source Flexibility Is the Norm
- Companies now use an average of 2.8 models, often combining OpenAI, Anthropic, and DeepSeek.
- Open-source models are rising rapidly—driven by inference cost savings, speed, and the ability to self-host.
3. Pricing Models Are Evolving from Subscription to Usage-Based
- 40% of companies include AI in premium tiers now, but 37% plan to shift to usage-based or outcome-based pricing.
- The high variable cost of AI (especially API usage) is pushing teams to rethink how they monetize intelligence.
4. Inference and Infrastructure Costs Will Dominate AI Budgets
- As AI products scale, monthly inference costs average $1M–$2.3M, and storage/processing adds another ~$2M.
- API usage fees are cited as the hardest cost to control—more so than talent or training.
5. Talent Is the Bottleneck, But Also the Differentiator
- 88% of companies employ AI/ML engineers, but hiring is slow—especially for senior AI roles.
- High-growth companies dedicate up to 37% of their engineering workforce to AI by 2026.
6. Agentic Workflows Are the Next Platform Shift
- These autonomous systems are not just chatbots—they are digital workers.
- High-growth firms are actively deploying them in production, with broad applicability in customer service, sales, and internal ops.
7. AI Governance Is Becoming a Strategic Imperative
- 66% of companies have formal AI ethics policies; human-in-the-loop is the most common safeguard.
- Regulatory scrutiny (GDPR, explainability, model transparency) is forcing companies to adopt defensible AI strategies.
8. Internal AI Productivity Spend Is Doubling
- Enterprises are allocating 1–8% of revenue to AI-powered internal tools.
- However, only ~50% of employees with access to AI tools are using them actively—highlighting a training and adoption gap.
9. Coding Assistance Is the Killer Use Case (So Far)
- Coding tools (e.g., GitHub Copilot, Cursor) are the most widely used and impactful, with high-growth firms attributing 33% of code to AI assistance.
- Productivity gains from GenAI in R&D are now measurable—15–30% improvements reported.
10. AI Product Development Is Now a Full Stack Discipline
- Companies are investing heavily across:
- Model hosting (OpenAI, AWS Bedrock, Vertex AI),
- Monitoring & observability (LangSmith, Weights & Biases),
- Data engineering (Apache Spark, Kafka, Glean),
- MLOps (MLflow, W&B).
📌 Key Strategic Recommendations for Businesses
For Leadership:
- Develop an AI-native strategy: Don’t just bolt on AI—consider AI as a foundational capability.
- Prepare for cost volatility: Create financial models around inference scaling, not just headcount.
- Push toward explainability: Especially if you’re in finance, healthcare, or legal-tech.
For Product Teams:
- Adopt agentic workflows early to gain operational leverage.
- Choose multi-model architectures for performance, price, and resilience.
- Build feedback loops for real-time performance and hallucination control.
For HR & Ops:
- Invest in AI training programs across business units—not just tech.
- Shift budgets from SaaS to AI-enabled productivity tools.
- Plan for a talent squeeze in data science, AI PMs, and prompt engineers.
Questions Every Executive Should Be Asking
- Are we building on AI-native foundations—or trying to retrofit?
- What percentage of our infra spend is locked in external APIs?
- Do we know what our top AI use cases are—and their ROI?
- How do we price our AI-driven value creation?
- Do we have governance, transparency, and monitoring in place?
Why This Matters: The Next Decade Belongs to the Bold
The world isn’t simply shifting, it’s being rewritten. These ten AI truths aren’t just trends; they’re tectonic forces reshaping the entire business landscape. And if you’re still framing your strategy around legacy thinking, you’re already behind.
Let’s be real. The “AI-enabled” badge some companies wear like a participation trophy doesn’t cut it anymore. We’re entering an AI-native era, where competitive advantage is coded into the architecture, not layered on top like frosting. This isn’t SaaS 2.0, it’s a platform shift that puts infrastructure, interoperability, and agents at the core of your business model.
Look closely at where the money’s going. Inference costs aren’t a line item. No, they’re a liability. Coding? Ground zero for disruption. Governance? No longer a check-the-box compliance move. It’s now boardroom strategy. The companies that understand this aren’t experimenting, they’re reorganizing.
Why should this infographic sit on your desk or your team’s Slack channel? Because this isn’t theory. It’s a blueprint for where every decision—from product to pricing to people—needs to evolve.
You want growth? Build agents. You want margin? Redesign your stack. You want resilience? Make ethics and explainability part of your brand DNA. The productivity promise of AI isn’t in the GenAI razzle-dazzle. It’s in what it frees up your teams to do better, faster, and smarter.
If your company is still debating whether to invest in AI, ask yourself: What happens when your competitor already has? This next wave isn’t a sprint. It’s a re-platforming of business. And the winners won’t just adopt AI. They’ll architect around it.
TL;DR:
This isn’t just a checklist of AI trends; it’s a business survival guide for 2025–2035. Print it, share it, live by it. Because in this new decade, the real disruption won’t come from AI—it’ll come from the companies who know exactly how to use it.
—Andy Abramson
Founder, Comunicano
Value Creation Through Communication™