Becoming an AI-first company: It’s more than just automation
AI isn’t some far-off idea anymore. It’s tangible, dynamic innovation that’s here to stay — and it’s already having a profound impact on the world.
Yet many companies are underestimating AI's transformative potential. Rather than reimagining their operations, they're simply bolting AI onto existing processes—capturing modest productivity gains while missing the opportunity for fundamental business transformation. Becoming an AI-first company isn’t just about automating what you already do. It’s about reimagining what’s possible.
At Box, we’re here to help you understand the ever-evolving landscape so your business can move at the speed of AI. Read on for some of the key recommendations from our brand-new white paper, Becoming an AI-First Company.
Key takeaways:
- Being AI-first means redesigning workflows, not just automating tasks
- AI frees people up to focus on innovation by handling routine work
- Strong data management unlocks valuable AI insights
- Success requires proper governance and change management
What are the five principles of becoming an AI-first company?
The companies winning with AI aren't just automating—they're redesigning how work gets done. Box's analysis of successful AI transformations reveals five core principles that separate leaders from laggards. These principles go beyond productivity gains to fundamentally reshape how businesses operate and compete.
AI as a capability expander
AI shouldn’t just speed up old tasks — it should open doors to things you couldn’t do before. Imagine writing software with a single prompt or onboarding new hires in hours instead of months! The companies that win will redesign their products, workflows, and operations with AI agents that help solve problems creatively and make smart decisions proactively.
Human-AI partnership
AI isn’t here to replace people but to handle routine tasks, freeing employees to focus on innovation and relationships. Hiring people who are comfortable with AI and training teams through hands-on learning makes using AI natural. When everyone - even technical teams - can experiment, you unlock fresh ideas and foster a culture of curiosity and innovation.
AI-native design
Old-school software interfaces won’t cut it now that thousands of intelligent agents could be running behind the scenes all at once. AI-native design is all about adaptability, continuous learning, and intuitive interaction — and businesses need flexible systems that let humans and agents collaborate in real time. As AI agents start handling more work behind the scenes and organizations use them in new ways, teams will need to rethink software interfaces and how permissions work so they can keep up and communicate smoothly with these smart helpers.
AI anchors: Data privacy, security, trust, and governance
Data privacy is still top-of-mind (and rightly so). Companies must build systems that follow strict governance rules so users only see what they're allowed, even when powerful autonomous agents are involved. A core principle: "AI agents can't keep a secret"—never rely on AI to maintain data security models, but instead ensure clear separation between data permissions and AI agent workflows. Trust also means being transparent about how algorithms make decisions — and actively managing bias from day one — not as an afterthought later on.
Data as a strategic asset
A successful AI strategy must start with secure data management: capturing information accurately, organizing content thoughtfully, enforcing permissions carefully, and keeping everything up-to-date over time. Unstructured data becomes incredibly valuable when paired with agents that can pull insights from millions of documents, from speeding up drug research to making mergers smoother.
Unstructured data (things like customer contracts, financial documents, and video recordings) makes up 90% of an organization’s data, but historically it has been difficult to analyze, make sense of, and extract valuable insights from. For the first time, AI enables organizations to add structure to content, turning it into a goldmine of information that impacts processes across the business. This creates a virtuous cycle: the more structured data you have, the more relevant and useful AI becomes to your organization. This shift isn’t just about cutting down workloads, it’s about unlocking new possibilities for what your business can achieve with your content.
How can companies identify high-value ways to leverage AI?
When beginning to adopt AI, it's crucial to focus your efforts where they'll have the biggest impact. We have developed a simple framework to help companies identify the efficiency frontier in developing an AI-first strategy.
Start with foundational deployments, then scale strategically. Everyday tasks like writing emails, summarizing documents, or transcribing video conferences should be your first priority—not because they're transformative, but because they demonstrate immediate time-saving benefits and ease adoption for more strategic work. Employees need to see AI's value before embracing larger changes.
Once you achieve base deployment and adoption, shift the bulk of your energy toward AI agents that offer the greatest business transformation potential. The biggest transformations tend to happen with work that's either highly frequent or demands significant critical thinking.
The holy grail combines both: work that's relatively frequent and requires critical thinking that only recent AI developments can deliver. Examples include AI-augmented code writing and reviews, and automating contract intelligence and management. Every industry will likely experience different areas of transformational AI use cases, so identifying where AI can augment work, better serve customers, or drive new revenue streams is key.
What are the stages of AI maturity in enterprises?
There are five stages of AI maturity, and knowing where you sit on this curve helps you set realistic expectations and make strategic investments. Your position on the maturity curve (most organizations are only at the first level) can help you set expectations and prioritize investments.
At the first stage of AI maturity, AI is primarily used for basic information retrieval and generating insights, while humans make all decisions and carry out workflows. As organizations progress to the second stage, AI begins executing predetermined workflows across departments, with humans still responsible for defining processes and monitoring outcomes.
In the third stage, AI systems interact with each other to solve complex problems, providing strategic oversight while keeping humans actively involved in decision-making. The fourth stage sees AI improving its own performance autonomously, with humans approving strategic changes and managing multiple AI agents simultaneously.
Finally, at the highest level of maturity, AI independently identifies opportunities and manages entire business functions, allowing humans to focus solely on setting vision and long-term strategy. As our CEO Aaron Levie put it, “When enterprises go AI-first, you’ll have AI agents running 24/7 doing work across the business. Most knowledge work was historically contained to specific hours, and now it’ll just get done continuously. At scale, this moves every business decision forward far faster, while also enabling even more effective decisions.”
As you think about how ready you are for AI and identify your position on the maturity curve, it helps to ask key questions like:
- Do we have the right policies, systems, and infrastructure in place to support AI tools?
- Can our data systems handle agents retrieving information?
- Are we set up so different parts of our AI technology can work well together and give us options?
- Do we have a plan for managing changes as we bring in AI?
- How will we track if using AI is actually making a difference: saving time, cutting costs, or getting more work done?
What does it take to lead with an AI-first mindset?
Becoming an AI-first company is way more than adding innovative product capabilities. It means rethinking how your business works, from embracing experimentation culture all the way down to building secure infrastructure that supports trusted teamwork between humans and machines.
Read the full white paper to explore maturity stages, best practices, and actionable steps to scale AI across your organization.
