"With AI, there's not going to be any going back to the way things used to be and how we work. It's just not possible because the efficiency gain between the company that uses AI versus the one that doesn't is just too insurmountable to try and make up for if you're not using these technologies."
That's Aaron Levie, CEO of Box, articulating a reality that many enterprise leaders feel but struggle to act on. The technology is here. The competitive pressure is mounting. But the path from AI experimentation to enterprise transformation remains elusive for most organizations.
In a recent deep-dive published on Box's blog, the company shared their complete methodology for AI-first transformation—the frameworks, governance structures, and prioritization approaches that enabled them to move from ideating over 100 AI agents to systematically deploying 15-25 "big bets" that have transformed how 2,800 employees work across sales, support, engineering, and customer success.
This isn't another thought piece about AI's potential. It's a blueprint for execution.
What makes Box's approach worth studying isn't just the results—it's the systematic rigor they've applied to a challenge that defeats most organizations: moving from scattered experimentation to coordinated transformation.
The Fragmentation Trap
Most companies aren't failing to adopt AI. They're failing to transform with it.
As we explored in our analysis of McKinsey's 2025 State of AI report, 88% of organizations now use AI in at least one business function, but only 6% are capturing meaningful enterprise value. The rest are stuck in what McKinsey calls "a proliferation of disconnected micro-initiatives and a dispersion of AI investments, with limited coordination at the enterprise level."
Box recognized this pattern in their own organization. They had deployed dozens of AI tools. Teams were experimenting across functions. But they weren't seeing business transformation—just incremental productivity gains scattered across the enterprise.
The turning point came when they shifted from asking "Where can we use AI?" to "Where can AI create strategic impact?"
That question—and the governance model they built to answer it—offers lessons for any organization serious about AI transformation.
Why Principles Come Before Technology
Before deploying a single agent, Box established clear AI principles. This wasn't corporate theater—it served two critical functions.
External credibility. Customers and partners need clarity on how you approach AI security, governance, and transparency. This is especially critical when AI touches sensitive enterprise content.
Internal confidence. As Olivia Nottebohm, Box's Chief Operating Officer, explains: "This is the most significant technology most people will have experienced in 15 to 20 years. We needed to think about it from a change management perspective, because ultimately AI is an extension of human activity—a capability expander that gives freedom to do things you otherwise couldn't do."
The framing matters enormously. Employees across every organization are asking the same questions: Will AI replace my job? Do I need to become a technical expert? What happens to my team? Without explicit answers, fear spreads faster than adoption.
Box's Five AI Principles
- AI as a capability expander: AI shouldn't just speed up old tasks—it should open doors to things you couldn't do before
- Human-AI partnership: AI handles routine tasks, freeing employees to focus on innovation and relationships
- AI-native design: Software interfaces must adapt to thousands of intelligent agents running simultaneously
- Strong anchors in data privacy, security, trust, and governance: Systems must enforce strict access controls, even with autonomous agents
- Data as a strategic asset: Successful AI strategy starts with secure data management
The first principle—"capability expander" rather than "efficiency tool"—sets the tone for everything else. It's not about doing the same work faster. It's about doing work that wasn't possible before.
The Governance Model: Functional Ownership with Central Support
This is where Box's approach diverges most sharply from typical enterprise AI initiatives.
Most organizations either centralize AI under IT (creating bottlenecks and disconnection from business needs) or decentralize entirely (leading to fragmentation and duplication). Box built a three-tier model that avoids both failure modes.
Tier 1: Executive Sponsorship
Box's AI executive sponsorship is led by three leaders representing business, IT, and people functions—all critical dimensions of transformation. They set strategic direction and guardrails without becoming an approval bottleneck.
What executive sponsors do:
- Set strategy, direction, and guardrails
- Track progress centrally and drive accountability
- Make final decisions on trade-offs and prioritization of "big bets"
Key decisions they've made:
Box-first principle for content workflows. As Robert Ferguson, Box's Head of Corporate Strategy, explains: "This does three things: First, it proves we believe in what we sell. Second, it creates real-world examples we can share with customers. Third, it provides our product teams with immediate feedback from customer zero."
Big bets focus. "The recognition that we need to pick some big bets enables us to focus our resources and executive sponsorship in areas we think AI will have the greatest impact," Ferguson notes. "We're not saying stop ideation. But we are asking our functional leaders to prioritize which ideas will have the highest impact."
Explicit "no" decisions. To ensure effective change management, executive sponsors work with functional leaders to explicitly decide what not to pursue. Teaching people to leverage select key agents has far more impact than building hundreds.
Tier 2: Functional Ownership
"We want the functional leader to be in the driving seat, because ultimately they're leading a team. It just so happens that part of their team will be agents."
This quote from Ferguson captures the core insight: AI agents aren't a technology initiative—they're a workforce evolution. The people best positioned to determine where agents create value are the leaders who understand business goals, know what good work looks like, and can make the necessary tradeoffs.
What functional leaders do:
- Propose "big bets" for their function based on business priorities
- Define team outcomes that leverage agent capabilities
- Set ambitious goals that assume AI capabilities
What AI Managers do:
- Own adoption and change management within their teams
- Provide feedback loops to improve agents
- Create new agents over time based on operational learning
This creates new strategic questions that didn't exist before:
- Capacity planning: If an agent handles first-response support with 50% deflection, how does that change future headcount needs?
- Role evolution: How do roles evolve to manage agents—validating output, troubleshooting, mapping data pipelines?
- Process redesign: How should workflows be fundamentally redesigned around AI capabilities?
- Performance management: When a process involves both human and AI contributions, how do you evaluate individual performance?
Tier 3: Design & Build Team
Not every AI agent requires centralized development. Many can be built directly by AI Managers using tools like Box AI Studio. A copywriter agent that drafts social posts can be built and maintained entirely within a marketing team.
The Design & Build Team steps in when:
- Multiple functions need the same underlying capability
- Complex enterprise system integrations are required
- Workflows span multiple teams or cross-functional processes
Ferguson explains the logic: "If six different teams need account research capabilities, we shouldn't have six teams each building their own version. And if an agent needs to pull data from Salesforce, our data warehouse, and Box while writing back to multiple systems, that requires technical expertise that most functional teams don't have and shouldn't need to develop."
Avoiding New Bureaucracy
Critically, Box's governance model avoids creating new approval bodies or bureaucratic layers. Existing processes adapt to include AI:
- Legal, GRC, and Cybersecurity teams update AI acceptable use policies within existing approval frameworks
- Procurement follows the same approval avenues for AI tool spend as for existing software
- No new committees or review boards
The key principle: Empower functional leaders within clear guardrails, supported by specialized expertise—without creating new layers of approval.
The Big Bets Framework: Prioritization That Matters
With principles established and governance in place, how do you decide which AI initiatives deserve investment?
Box uses a 2x2 prioritization framework that plots opportunities on two dimensions:
- Repeatability: How often does this task or workflow occur?
- Critical Thinking Required: How complex is the judgment or decision-making?
The sweet spot—high on both dimensions—identifies your highest-ROI opportunities. These are complex decisions that happen frequently enough to matter.
A lead gen agent that performs sophisticated data analysis but executes dozens of times per day delivers far more value than a rarely used tool, no matter how impressive.
This framework enabled Box to move from ideating over 100 agents to focusing on 15-25 "big bets" with production-ready training, integration, and change management support.
Three Value Areas for AI Impact
Box organizes AI value realization across three areas, each with distinct ROI characteristics:
Area 1: Productivity (Agents Help People Work Faster)
Productivity improvements accelerate existing work without changing process structure. Agents function as assistants while humans maintain decision authority and final review.
Example results:
- Sales RFP assistant reduced response time from two days to four hours—a 90% reduction
- Professional services teams generate statements of work 35% faster
The evaluation question: What high-volume, repeatable tasks consume disproportionate time relative to value created?
Area 2: Automation (Agents Oversee Entire Processes)
Value comes from ratio changes—agents handling dramatically more volume with the same team size.
Example results:
- Customer support deflection agent handles 50% of inbound questions without human intervention
This doesn't just save time—it fundamentally changes the equation. The same team can handle dramatically more work.
The evaluation question: What complete workflows can run autonomously with human oversight rather than involvement?
Area 3: Net-New Capabilities (Unlocking the Previously Impossible)
This tier enables capabilities that didn't exist at any headcount or budget level. Value emerges from new revenue streams, market access, or strategic advantages.
Example results:
- Lead generation agent analyzes customer usage patterns, freemium conversion signals, and engagement data to generate targeted outreach recommendations—work that previously required SQL expertise, data warehouse access, and hours of analysis per account. Now any SDR accesses equivalent insights conversationally in seconds.
This represents capability redistribution, not productivity improvement. When any sales rep can perform advanced data analysis, what happens to specialized analyst roles?
The evaluation question: What becomes possible that was impossible before—not merely harder or more expensive?
Implementing This in Your Organization
Box's framework translates to organizations of any size. The core elements:
1. Start with principles, not pilots
Before experimenting with AI tools, establish clear principles that address both external stakeholders (customers, partners, regulators) and internal concerns (employees, teams, unions). Frame AI as a capability expander—not a cost-cutting tool.
2. Own transformation functionally, support centrally
The people closest to the work should determine where AI creates value. Centralize only what requires coordination: strategy, guardrails, and complex technical builds that serve multiple functions.
3. Prioritize ruthlessly
Use the 2x2 framework—Repeatability × Critical Thinking—to identify big bets. Better to deploy 3 agents with full training, integration, and change management than 30 agents that nobody uses effectively.
4. Distinguish productivity, automation, and net-new value
Different value areas require different success metrics and different organizational approaches. Don't measure all AI initiatives the same way.
5. Adapt existing processes—don't create new bureaucracy
AI governance should fit within existing approval frameworks. New committees and review boards slow transformation without adding value.
The Bottom Line
Box's AI-first transformation offers a concrete example of what McKinsey's research describes in the abstract: the practices that separate the 6% capturing real AI value from the 88% merely adopting AI tools.
The differentiators aren't mysterious:
- Principles before technology: Clear AI principles that address both external credibility and internal confidence
- Governance that empowers: Functional ownership with central support—not centralized control or fragmented experimentation
- Ruthless prioritization: Moving from 100+ ideas to 15-25 big bets with full organizational support
- Value clarity: Distinguishing productivity, automation, and net-new capabilities
As Levie puts it: "AI agents both free up people to do more of what they couldn't do before, which will lead to all new work being done. But it also accelerates work in ways that essentially redefines the output expectations."
The gap between AI adoption and AI transformation isn't closing on its own. Organizations that build the governance structures, prioritization frameworks, and cultural foundations for AI-first operations will compound their advantages. Those that remain in perpetual experimentation mode will find themselves increasingly disadvantaged.
The blueprint is public. The question is execution.
At OuterEdge, we help organizations build the governance structures and execution frameworks required for AI transformation. Our approach mirrors what Box demonstrates: functional ownership, clear principles, and ruthless prioritization. If you're ready to move from experimentation to strategic execution, book a strategy call to discuss your AI-first transformation.