1. Define the Problem, Not the Tool
Before anyone on your team even mentions a new model or feature, re-center the conversation on your actual business needs. It's tempting to find a problem for a cool new solution, but that's a recipe for expensive detours. Which specific, recurring workflow
is causing the most friction right now? Is it slow customer support, tedious report generation, or a clunky sales process? Write down the problem statement and the desired outcome without naming any technology. An evaluation that starts with a vendor's product description instead of your own business requirement is already compromised. The goal isn't to use the latest AI; it's to solve a real, costly problem that is holding you back.
2. Calculate the Total Cost of Ownership
The sticker price of a new model or API is just the beginning. A cheaper model like the new 'GPT-6.1 Sol' might lower per-query costs, but what's the total investment? Consider the 'hidden' expenses: the engineering hours required for integration, the cost of training your team, the potential need for new hires, and ongoing maintenance. A new, more powerful agent that can operate autonomously sounds great, but it also requires a new layer of oversight and management. Be especially wary of pricing tiers that affect speed or usage allowances, like 'Ultrafast' plans that burn through budgets quicker. A true ROI calculation looks at the full picture, not just the per-token price on a website.
3. Re-evaluate Your 'Build vs. Buy' Stance
Each DevDay shifts the 'build versus buy' calculation. A custom-coded solution that took your team six months to build might now be a standard, off-the-shelf feature in the new Agents API. Conversely, a new, more powerful base model might finally make a bespoke project economically viable. This is a moment to be brutally honest about your existing tech stack. Are you clinging to a custom tool out of pride when a managed service would be cheaper and better? Or are you paying for a third-party service that you could now replicate in-house for a fraction of the cost with a more efficient model? This isn't just about adding new things; it's about re-evaluating everything AI touches in your organization.
4. Ask the Hard Questions About Data and Security
Convenience often comes at the expense of control. As OpenAI pushes to become a central 'workspace' where other apps integrate and work gets done, you need to be clear about data governance. Before adopting a new agent or feature, ask: Where does our data go? Who can see it? And how is it used? OpenAI states that it doesn't use data from its business offerings to train its models, but you are still sending information to a third-party server. For any feature that touches client data or proprietary information, security and compliance are non-negotiable. Involving your IT and security teams early in the evaluation process isn't a bureaucratic hurdle; it's essential due diligence to avoid a major headache later.
5. Start With One, Reversible Pilot Project
Don't try to boil the ocean. The hype around new 'always-on agents' or collaborative 'Spaces' can create pressure for a company-wide rollout, but the smartest approach is to start small. Choose one specific, recurring task for a pilot program. Pick something with a clear definition of success that a human can easily review and, if necessary, reverse. Good candidates include summarizing weekly reports, triaging inbound support tickets, or generating first drafts of marketing copy. The goal of the pilot isn't just to test the technology; it's to understand how it fits into your actual human workflows and what new problems it might create. A successful pilot provides the data you need to make a bigger decision, while a failed one offers a cheap, valuable lesson.
