The true measure of success in enterprise artificial intelligence is not found in the novelty of the technology itself, but in its strategic and meticulous integration into the core workflows that drive a business. Many organizations discover that treating AI as a simple software rollout, akin to deploying a generic chatbot, often leads to failure, as this approach overlooks the profound need for a deep, process-oriented implementation. The journey of Gold Bond Inc., a 77-year-old promotional products company, serves as a powerful case study, demonstrating that successful adoption hinges on solving tangible business problems with precision. Their CIO, Matt Price, championed a vision where AI is valued less for its technological hype and more for the meticulous change management, robust governance, and human-centric focus required to augment employee capabilities rather than merely replace them. This perspective transforms AI from a speculative tool into a foundational component of operational excellence, proving that the most advanced algorithms are only as effective as the business processes they are designed to improve.
The Human-Centric Blueprint for AI Adoption
Cultivating Champions, Not Mandates
Rather than imposing a rigid, top-down mandate for AI adoption, Gold Bond’s leadership pursued a grassroots strategy meticulously designed to address the daily frustrations of its workforce. The core insight was that genuine buy-in would never emerge from executive directives or abstract promises of future efficiency. Instead, it had to be earned by alleviating the specific, persistent pain points that employees grappled with, such as the tedious manual processing of unstructured order data and the cumbersome management of document handling. This problem-oriented approach rejected broad, benchmark-driven goals in favor of targeted interventions. For this initiative to succeed, it needed to be championed from within, transforming the narrative from a corporate requirement into a shared mission. This required a deep understanding of the human element of technological change, recognizing that resistance often stems not from an opposition to progress but from a fear of disruption and a skepticism toward unproven solutions that feel disconnected from day-to-day realities.
The mechanism for this transformation was the cultivation of a small but highly influential team of approximately eight “super-users” who became the internal engine for change. This carefully selected group was empowered not just to use the new technology but to co-create its application within the company. Their mandate was threefold: identify high-impact, Gold Bond-specific use cases where AI could offer immediate value; rigorously test new tools in a controlled sandbox environment to validate their effectiveness; and, most importantly, act as internal champions and peer trainers for the rest of the organization. This peer-led model proved exceptionally effective in navigating the natural apprehension within a legacy company. When colleagues saw trusted peers—not distant executives—demonstrating tangible benefits and offering hands-on guidance, skepticism organically gave way to enthusiastic adoption. This strategy created a powerful “pull” for the technology, with employees actively seeking out AI solutions, a stark contrast to the friction-filled “push” of a traditional corporate rollout.
Targeting High-Friction Processes
The centerpiece of Gold Bond’s AI strategy was the direct and deliberate integration of intelligent automation into its most cumbersome and resource-intensive workflow: order processing. Serving a diverse base of 8,500 active customers, the company was consistently inundated with a high volume of orders, quotes, and requests arriving in a multitude of unstructured formats, including emails, faxes, and disparate web forms. Prior to the AI implementation, this operational reality created a significant bottleneck, as each incoming document required extensive and error-prone manual data entry to be properly logged into the company’s enterprise resource planning (ERP) system. This manual process not only consumed countless hours of labor but also introduced a high risk of data entry errors, which could lead to incorrect orders, shipping delays, and diminished customer satisfaction. The inefficiency of this critical workflow represented a substantial operational cost and a clear opportunity for a high-impact technological intervention that could deliver immediate and measurable returns.
To dismantle this long-standing bottleneck, the company developed a sophisticated, AI-powered system in collaboration with Google premier partner Promevo, which automated the entire data-intake pipeline. The new workflow begins with Google Cloud ingesting all incoming documents, regardless of their original format, and normalizing the data into a consistent structure. Following this initial step, a carefully orchestrated combination of large language models, including Google’s Gemini and select models from OpenAI, is deployed to perform the heavy lifting. These models intelligently scan the normalized documents to extract and structure all relevant fields—such as product SKUs, quantities, customer details, and shipping instructions—with remarkable accuracy. The system then automatically generates a fully completed purchase order from this extracted data. The final step in this seamless process involves pushing the newly created purchase order directly into the company’s ERP system, completely eliminating the need for manual intervention and transforming a once-arduous task into a highly efficient, automated operation.
Expanding the Ecosystem with Pragmatism and Governance
A Multi-Model Toolkit for Diverse Tasks
Building on the foundation of its initial order-processing success, Gold Bond thoughtfully expanded its AI implementation by adopting a pragmatic, multi-model approach, a strategy that is increasingly defining mature enterprise AI. This philosophy rejects the notion of committing to a single vendor or a one-size-fits-all solution, recognizing instead that different tasks require different tools. The company’s technology stack is now a curated ecosystem of best-in-class models, each selected for its specific strengths. Gemini, integrated within Google Workspace, serves as the accessible entry point for most employees, assisting with everyday tasks like drafting emails and summarizing meetings. For more complex backend automation, the company leverages the power of ChatGPT, while Claude is employed for sophisticated quality assurance protocols and high-level reasoning checks, ensuring the integrity of automated outputs. This agnostic strategy provides the flexibility to pivot as technology evolves and ensures that the most effective model is always applied to the corresponding business challenge.
This multi-model toolkit extends far beyond administrative and data-processing tasks, permeating both creative and operational functions throughout the organization. For instance, the marketing and design teams now use Recraft, an AI-assisted tool, to rapidly generate “virtual mockups” of branded products, enabling them to iterate on visual concepts in minutes rather than days and present compelling options to clients with unprecedented speed. In another department, employees leverage AI to generate complex formulas for Google Sheets, unlocking advanced data analysis capabilities without requiring specialized programming skills. Furthermore, the company has implemented NotebookLM to build a dynamic and continuously updated internal knowledge base. This AI-powered repository streamlines employee training, standardizes operating procedures, and ensures that critical institutional knowledge is easily accessible to everyone, fostering a culture of continuous learning and operational consistency across all teams.
Measuring Real-World Impact
The tangible results of this deeply integrated workflow strategy are substantial and are measured not in abstract, high-level metrics but in concrete, observable changes in employee behavior and productivity. Daily AI usage among the workforce surged from a modest 20% to an impressive 71%, demonstrating widespread adoption and integration into daily routines. Even more compellingly, a significant 43% of the workforce reported saving up to two hours per day, a direct testament to the efficiency gains unlocked by the new tools. These improvements are not confined to a single department but are evident across the entire organization. The time required to create a comprehensive client presentation, for example, plummeted from an average of four hours to just 30 minutes. Meanwhile, developers have enhanced code quality and reduced debugging time by implementing a dual-model system to audit NetSuite scripts before they are even sent for testing, and project planning cycles have been dramatically compressed.
To formally quantify these improvements and ensure that the perceived benefits were backed by hard data, the IT team instituted a practice of conducting regular Kaizen events. These short, intensive workshops are specifically designed to document and compare baseline workflows against their new, AI-assisted counterparts. During a Kaizen event, a team methodically maps out every step of a traditional process, measuring the time and resources required for completion. They then repeat the exercise using the AI-powered workflow, capturing detailed data on the improvements in speed, accuracy, and overall effort. This disciplined, granular approach to measurement moves beyond anecdotal success stories, providing clear, quantifiable evidence of the technology’s return on investment. It also creates a continuous feedback loop, allowing teams to identify further opportunities for optimization and reinforcing a data-driven culture of process improvement throughout the company.
Building a Framework of Trust and Verification
Underpinning this entire technological transformation was a robust framework of governance and a mandatory “human-in-the-loop” verification process, which proved indispensable to the program’s success. To proactively combat the risks of shadow AI and ensure data security, Gold Bond implemented a comprehensive set of controls that included clear policies on acceptable use, advanced data loss prevention (DLP) protocols, and robust identity layers to manage access. The company used tools like LibreChat to centralize access to all approved AI models, enforce the use of paid enterprise licenses, and actively block unauthorized services. Furthermore, all proposed changes or new AI integrations had to undergo rigorous testing in a secure sandbox environment and required formal sign-off from both the technical team and relevant subject matter experts before being deployed into the production environment. This structured approach ensured that innovation never came at the expense of security or stability. Critically, blind trust in AI-generated output was strictly forbidden, establishing a non-negotiable principle of human oversight. All public-facing content or mission-critical data generated by AI required human approval before release, and employees received training to cultivate a “trust, but verify” mindset. This verification became a formal step in the workflow, with users prompted to ask the AI for its sources and challenge its reasoning. This institutionalized skepticism helped manage expectations about the technology’s limitations and ensured that human expertise remained the final arbiter of accuracy and quality.
