The AI Adoption Gap: Why Enterprises Fall Behind and How to Fix It
An exploration of a very complex topic in plain terms. If you're training or injecting LLMs into your products, what steps are vital to align your models with your company's long term goals?
What Is Model ‘Alignment’?
Sam Altman's latest blog, "The Gentle Singularity," highlights a crucial area that most companies should heavily invest their resources in — model alignment. A few of our customers at Anthromind have been early adopters of this new paradigm, like certain superintelligence labs. Hence, I have a deep understanding of this shift. But first, what exactly is model ‘alignment'?
Model alignment is the process of ensuring that an AI model’s outputs accurately reflect the greater world’s goals, constraints, and expectations. For enterprises, this means making sure the model adheres to compliance standards, domain-specific accuracy, brand tone, and ethical guidelines. Alignment is not just about prompt engineering. It involves systematic evaluation, continuous feedback, and targeted customization.
Without robust alignment practices, models are prone to hallucinations, errors, or, worse still, costly misjudgments. Hence, Altman is quick to point out that alignment is the first area of focus for most enterprises building with AI.
Lesson Zero: The Old Enterprise AI Game
OpenAI’s recent AI in the Enterprise Guide discusses seven crucial lessons for AI adoption, but before delving into those lessons, let's survey the current state of affairs. Before the generative AI wave, enterprise AI efforts were largely confined to automating back-office tasks—rule-based systems, clunky chatbots, and dashboard-heavy business intelligence tools. In that era, AI lacked velocity, relevance, and visibility. Companies underinvested in foundational capabilities like evaluation infrastructure, data labeling, or cross-functional experimentation.
However, with the watershed development of generative AI, the landscape has undergone a significant transformation. Companies are now diving headfirst into LLMs to incorporate them into their workflows. Despite this, there have been mixed results for many enterprises (I’ve seen multi-million-dollar POCs fail whilst I was at Google Cloud, for example), with no real value being attained.
OpenAI’s 7 Seven Commandments
According to the AI in the enterprise guide, there are seven lessons every enterprise needs to take to heart to stand out in the AI race. However, the crucial one is Lesson 1, because you can’t proceed to the subsequent steps without implementing it.
Lesson 1: Start with Evals
OpenAI discusses a compelling customer use case here. Before deploying models in sensitive workflows, top companies must establish structured evaluation systems. Morgan Stanley, for example, ran rigorous tests on translation, summarization, and domain-specific answer quality before rolling out AI to financial advisors. This gave them confidence and control in their own models before becoming AI-first.
Anthromind’s role fits squarely here. We help companies build repeatable and trustworthy evaluation pipelines tailored to their specific domain. Whether it’s compliance in finance, quality in support, or accuracy in summarization, our eval frameworks make model alignment real and operational.
Here are the remaining lessons; the main takeaway is that without evaluations, your AI workflows won’t go very far, as you aren’t yet directionally aligned.
Lesson 2: Embed AI into Products
Lesson 3: Start Now and Invest Early
Lesson 4: Customize and Fine-Tune Your Models
Lesson 5: Get AI in the Hands of Experts
Lesson 6: Unblock Your Developers
Lesson 7: Set Bold Automation Goals
How To Stay Ahead Of The Curve
The AI gold rush is here, and it’s been pretty much sink or swim. Well-established companies are being overtaken by young startups with fewer than 10 employees, solely because they embrace these AI systems from the outset. My advice: get started on evals yesterday, get ahead of the curve, and then deeply embed AI into your processes and applications.
Anthromind’s work with superintelligence labs has consistently demonstrated excellent quality, giving them great confidence before deploying to their end users. In any enterprise, aligning your models with your vision is paramount. Reminder that our calendars are always open if you’d like to chat with us. Sign up here: https://www.anthromind.com/sign-up/.
Finally, here are some great resources I referred to whilst doing this research:
P.S. What Have I Been Reading?
Rendezvous with Rama: A great sci-fi classic that everyone should read. I appreciated how it was rooted in very hard science, a la The Martian. The spaceship, “Rama,” that the humans in this book visit is well-described and fascinating.
SPQR: A History of Ancient Rome: I was told this book is the most excellent analysis of Roman history. With re-contextualizations of Livy’s writings during the 30s BC and later accounts by historians from the 19th and 20th centuries, SPQR presents a wealth of insights into this era of human civilization. I particularly found the gradual development of modern-day political systems, such as the Republic and Senate, engrossing.