Avoiding the 5 common pitfalls of going AI-first
AI agents revolutionize how your entire business operates, with applications across every department. They can conduct deep, specific research on all your unstructured data to help your sales team tap into institutional knowledge, your researchers capitalize on years of notes, your HR team usher new hires through onboarding, and other specific workflows your organization relies on every single day. Far beyond simply helping you write your emails faster, agentic AI creates a radical paradigm shift in the way your business operates.
But success in these efforts isn’t just a toe-dip into automating your status quo with AI. It requires a more visionary approach — one that helps you avoid common pitfalls.
Read on for insights from our brand-new white paper, Becoming an AI-First Company.
Key takeaways:
- The biggest opportunity is to rethink the way your workflows are designed
- Cutting costs cannot be the only goal
- Data-readiness is table stakes to becoming AI-first
- Your AI strategy must be integrated and governed across your organization
- Big bangs fizzle out without the right change management in place
The promise of becoming an AI-first organization
Becoming an AI-first company allows you to unlock bold new possibilities and drive true transformation in your business (that holy grail we’ve all been talking about for years). It empowers your people to break free from routine, fueling innovation and smarter decisions that shape the future. It’s about "reimagining what’s possible" while also ensuring you have strong data management and trusted governance. When you put this all in place, you can harness AI’s full potential while safeguarding what matters most.
Yet, many companies struggle with becoming AI-first because they cling to outdated approaches, roll out AI unevenly, or underestimate the organizational changes required to be successful. There are five big pitfalls organizations fall into as they attempt to take advantage of all that AI enables, and solving these challenges early will help build AI strategies that deliver lasting impact.
Pitfall #1: Automating without rethinking
If you’re treating AI as just another automation tool, you’re missing the greater opportunity it brings. Yes, AI can automate your current tasks. But it also brings the opportunity to rethink the way your workflows are designed.
For instance, contract workflows typically involve multiple handoffs for manual reviews that require sequential signatures. Often, this process results in duplicate versions of a document saved in various places, and the lack of a single signature can hang up the entire process. Agentic AI can redesign this workflow, embed e-signature without hangups, eliminate bottlenecks, and fundamentally transform how contracts flow through your organization.
As part of an Intelligent Content Management platform, agentic AI can also:
- Eliminate review cycles with AI-powered contract analysis that flags issues before human review
- Replace email attachments with secure, version-controlled collaboration spaces where all stakeholders work simultaneously
- Transform static documents into structured data that feeds directly into your business systems
These are just a few examples of the type of transformation you can bring to your everyday workflows with AI. Instead of asking “How do I automate this task?” ask “What should this job look like now that AI exists?”
Pitfall #2: Over-indexing on cost savings vs. acceleration and expansion
Focusing too much on cost savings at the start limits potential. While cutting costs matters, it shouldn’t be the only goal. The real value lies in speeding up progress, expanding capabilities, and improving customer experiences. For instance, you might concentrate on generating insights, enhancing engagement, and finding better methods to complete work. This bigger mindset encourages wider adoption and uncovers richer applications for AI.
Box CEO Aaron Levie talks about customers going beyond simple task automation by leveraging Intelligent Content Management: “When I talk to CIOs and CEOs of organizations, the general bias is to use AI to do more — launch better products, support customers better, launch better marketing campaigns.”
Pitfall #3: An absence of data readiness
AI depends on well-organized data, yet many organizations have become complacent about the status quo of scattered content, inconsistent tagging, and weak metadata management. Early investment in structuring data with Intelligent Content Management is foundational to your AI strategy. Only by capitalizing on clear metadata, well-managed file permissions, efficient version control, and other hallmarks of Intelligent Content Management can organizations achieve effective AI initiatives.
Levie says, “The vast majority of our corporate information is unstructured data. When you give AI agents that context, they can help you make better decisions and turn all of that unstructured information into a valuable goldmine of intelligence for your company.”
Pitfall #4: Lack of early strategic alignment and governance
Launching AI projects in isolation leads to duplicated efforts and uneven results. An AI-first company integrates AI across product development, marketing, and customer success, ensuring every effort supports a shared vision.
Governance must be universal and must go beyond compliance. It should embed ethics, values, and proactive risk management throughout AI deployment. From model selection to human oversight, governance steers responsible innovation and protects your organization’s reputation.
Pitfall #5: Underestimating change management and overestimating “big bangs”
We’re all excited about AI, but technology trends often play out more slowly than thought leaders think they will. That’s because the state of technology is often far ahead of most organizations’ ability to implement it. “We’re collectively limited by human change management,” Levie explains. “Sometimes we extrapolate too quickly that a technology will ripple through every single organization and part of the economy, when actually, in real life, we have systems to upgrade, data environments to improve, employees to train, and change management that has to be deployed.”
Rather than relying on one big rollout, effective AI adoption grows through small steps, early successes, and grassroots involvement to build momentum and integrate AI into daily work. Otherwise, resistance within teams can block even the best AI tools from succeeding. Clear communication, focused training, and visible leadership support build trust and encourage ongoing use.
Reinventing how work gets done with an AI-first approach
Don’t just automate your old workflows — reimagine roles and tasks with AI in mind. Focus on growth and improvement over quick cost cuts. Build a solid data foundation before scaling AI. Align your efforts strategically and make governance a priority. Invest in helping teams adapt.
At its core, becoming AI-first means rethinking work, aligning strategy across the organization, and creating a culture ready for change. With clear principles and guardrails, companies can move beyond isolated projects to unlock AI-driven improvements across the business. When implemented effectively, AI reshapes processes, decision-making, collaboration, and opportunities.
Read the full white paper to discover the principles of an AI-first company and how to identify your most high-value opportunities for AI enhancement.