On AI modernization, making a safe purchase, and hoping AI “Just Works”
AI investments fail to deliver because companies rush to buy licenses without a clear problem statement, baseline metrics, or a path to production.
I hear the same story every week. A leadership team banned AI in 2023 over data-leak fears. By late 2024 the pressure builds to “pick a platform.” Microsoft Copilot feels safe. ChatGPT Enterprise feels cutting edge. Someone signs the contract. Licenses get issued. Usage stays low. Workflows don’t change. KPIs don’t move.
Now we are seeing organizations enforcing mandatory use policies. Simply put, they bought everyone the AI tool, but nobody is using it. Why? We have found that when asked, staff feel like “using AI tools is cheating,” or worse, they fear they”will put (themselves) out of a job”.
This isn’t a tech problem. It’s an execution problem. Two big names aren’t your only choices. There are many ways to secure your data, and get enterprise performance. The biggest wins come from hybrid solutions built around your data, your policies, and your outcomes — not a sole vendor’s feature set. MIT’s State of AI in Business 2025 report makes the same point: most AI investments fail to deliver because companies rush to buy licenses without a clear problem statement, baseline metrics, or a path to production.
How we approach it at Virgent AI
We start outcome-first. What problem is worth solving? Who’s affected? What does success look like? Then we use roadmaps and service design blueprints to map people, processes, and tools. That makes bottlenecks and opportunities obvious before a single model is selected.
From there we compose a fit-for-purpose mix:
GPT-n/Claude/Azure OpenAI where it makes sense
Local or self-hosted models when you need privacy or cost stability
Retrieval that respects permissions
Agents that actually do work instead of just answering chat questions
We go further with MCP (Model Control Protocol) to securely integrate agents with your ERP, CRM, and DevOps tools without shadow IT. A2A (Agent-to-Agent) communication lets your bots hand off tasks to each other and orchestrate complex workflows end-to-end. These aren’t “cute demos” — they’re pipelines that can draft, review, approve, execute, and report, all under policy guardrails you define.
We use LangChain to orchestrate multi-step reasoning and tool use, Pinecone to give agents long-term memory and permission-aware retrieval, Hugging Face for open-weight models and private fine-tunes you own, and Vercel’s serverless edge functions to run stateless micro-agents close to users with low latency. Everything is modular, so you’re not locked in.
Own your IP. Own your innovation.
We don’t just “implement a tool.” We build custom pipelines where you own the IP, the data, and the innovation. That means:
Fine-tuning models on your interaction data so accuracy rises and latency drops
Training embeddings unique to your workflows to drive your own retrieval and ranking
Designing agents with secure skills for your systems, not someone else’s sandbox
Building a model/agent layer you can swap and extend as new models appear
This is how you build AI systems that save time and money but also make money — by embedding them into revenue-producing workflows, not just internal tasks. In our Beyond Copilot or ChatGPT case study, this approach moved a client from stalled pilots to two production workflows in six weeks, cutting cycle time by 28 percent and unlocking adoption that actually stuck.
The open and flexible future
We’re moving toward a world where businesses license models the way they license software. Think:
a Salesforce CRM model tuned for pipeline hygiene
a Google SEO model updated with every algorithm change
your own claims adjudication model built from your data
Your agent layer will route tasks to whichever model is best — licensed, open source, or self-hosted — based on cost, latency, domain expertise, or compliance. This will create a new economy of proprietary, specialty, and open-weight models that can be swapped dynamically. With MCP, A2A, LangChain, Pinecone, Hugging Face, and serverless infrastructure, you can build bleeding-edge pipelines you control — systems that are flexible, outcome-focused, and not walled off in the name of false “security.”
The takeaway
Don’t try to force a single tool to work. Frame problems worth solving. Use blueprints to target real bottlenecks across people, process, and tools. Compose hybrid solutions. Ship small, measurable steps. Iterate with real users. That’s how you get ROI.
If you’ve already bought Copilot or ChatGPT Enterprise, you’re not stuck. If you’re overwhelmed by options, you’re not alone. We’ll meet you where you are and build something that works — and that you actually own.
Conclusion
Aligning prompts, models, and objectives ensures AI truly delivers—because two out of three is never enough.
We Ask: Prompt engineering must be strategic.
AI: The model must match the task.
To Do Things: The objective has to solve a real user or business problem.
As CEO of Virgent AI, I’ve watched companies transform by respecting these three elements equally. We’re living in a moment when code is half written by us, half by AI, and soon we’ll all have personal agents guiding our workflows. If you haven’t already, please subscribe to this blog to see how we apply this thinking in real-world contexts.
It’s an exciting time. Prompt responsibly, design compassionately, and keep your objective front and center, because that’s how we harness AI for meaningful change. Interested in more content like this? Check out “We Ask AI To Do Things,” and consider subscribing.
Sources & further reading
MIT / Project NANDA — State of AI in Business 2025. Failure rates and why approach matters.
LangChain — Production Patterns. Orchestration, agents, and memory.
Pinecone — LangChain Agents. How agents add tools, retrieval, and memory to make LLMs useful.
Hugging Face — Inference Endpoints. Private, dedicated hardware for your open-weight models.
Vercel — Serverless AI Agents. Building and scaling edge-deployed micro-agents.
Copilot or ChatGPT - Virgent AI Case Studies. Real production pilots, findings, and more.