4 ways to test AI readiness before engineers take over your workflows
OpenAI’s $4 billion deployment company marks the end of AI’s experimentation era. Here is what comms leaders should do next.
Stephanie Nivinskus is principal at Ragan’s Center for AI Strategy.
The OpenAI Deployment Company, a majority-owned subsidiary of Open AI, is embedding its engineers inside companies like yours to build and operate AI systems you can use every day.
The implication for comms and marketing leaders is clear: There is an expiration date attached to AI experiments, and it just passed.
The new company, backed by more than $4 billion from TPG, Advent, Bain Capital and Brookfield, and supported by consulting firms Bain & Company, Capgemini and McKinsey, raises an important question: Where are companies most likely to mistake AI efficiency for AI readiness, and how can comms and marketing leaders catch that mistake before it becomes a reputational problem?
Here is what advisors from Ragan’s Center for AI strategy said:
- Watch the gap between experimentation and operations
Catherine Richards, Expera Consulting: “Make sure AI success in experimentation is not creating false confidence about what your organization is ready to stand behind in the real world.”
- Stop letting speed be the scoreboard
Meiko S. Patton, Ms. Beehiiv News: “Companies are most likely to mistake AI efficiency for AI readiness when speed becomes the scoreboard. Faster drafts, faster campaigns and faster personalization can create the illusion of transformation. But readiness is about judgment, governance and accountability.
Comms and marketing leaders have to ask: Who reviewed it? Who owns it? Who will defend this message publicly if it goes wrong?”
- Choose effectiveness over efficiency
Alex Sevigny, McMaster University: “I am hearing a lot of confusion around the difference between ‘efficiency’ and ‘effectiveness’. Effectiveness means getting the most value out of your workflows. It means being strategic to optimize business development opportunities. That’s why AI roll-out should be bottom-up, with staff examining their workflows and then reshaping them around AI. A top-down approach might save money by automating processes, but it might make staff more robotic and less strategic. Redefine your AI processes to become more business-effective, and efficiencies will follow.”
- Protect the judgment layer that protects the brand
Miri Rodriguez, Empressa AI: “Communications is one of the most AI-exposed industries in the economy and one of the most female-majority. Women are heavily represented in the roles AI is reshaping, yet they hold only 14% of senior AI leadership roles. They are also 29% more likely to question AI accuracy and 38% more likely to raise ethical concerns about AI outputs. That matters because when efficiency outruns human judgment, accountability does not land on the model. It lands on the brand and on the leaders responsible for protecting trust.”
The takeaway
The cost of confusing efficiency for readiness cannot be ignored. Anything from a campaign that scales the wrong message to a personalization that crosses a privacy line will cost more to fix than what was saved by cutting corners.
Learn more with Ragan’s Center for AI Strategy.
