AI adoption succeeds when integrated into high-friction workflows led by IT
AI doesn’t move the needle when it sits on the sidelines. It becomes meaningful when embedded directly into the daily grind, especially into the processes that people tend to avoid. At Gold Bond Inc., a 77-year-old promotional products company, CIO Matt Price didn’t start with a flashy chatbot launch. He focused on the grunt work, manual ERP intake, document entry, call summaries, tasks people naturally hate. These were the friction points. That’s where AI made the difference.
The deployment wasn’t top-down. Price built a small, trusted group of early adopters, super-users. They found real use cases inside the business and used those to train everybody else. It was deliberate, fast, focused. No accumulating slides with benchmarks. Instead, they wired tools like Gemini and OpenAI directly into the systems already in use. Testing happened in a sandbox environment. Guardrails and human review were built in from the start, specifically for any public-facing content.
The outcome wasn’t hype, it was behavior change. Daily AI engagement jumped from 20% to 71%. Nearly half the workforce reported saving up to two hours each day. That’s meaningful operational leverage. It’s not about announcing AI, it’s about embedding it where it replaces overhead and improves the pace of real work.
There’s a lesson here for executives. Don’t try to dazzle with AI. Start by solving actual workflow pain. Identify where delays accumulate or where teams are buried in process-heavy tasks. Integrate AI there. The success will speak for itself.
AI thrives in operational complexities such as ERP systems and customer interaction channels
When the workflow is messy, multiple inputs, file formats, and disconnected systems, it’s easy to waste time and lose accuracy. Gold Bond is a company that handles quotes, orders, and sample requests from over 8,500 customers. These don’t arrive neatly. They come through websites, emails, faxes, sometimes even handwritten. That’s where AI earns its place.
Price’s team used Google Cloud to standardize incoming documents. Then Gemini and OpenAI models extract the information, product SKUs, quantities, customer details, and translate it into structured ERP fields. What once required a manual keying process is now streamlined into minutes, with AI handling data normalization and entry.
This is a better use of time. Not only are they saving hours per transaction, they’ve also reduced input errors and bottlenecks. It’s real operational efficiency. It’s also scalable without needing more headcount.
Executives need to understand that AI is especially strong at taming complexity. When the work is variable, discontinuous, and error-prone, well-trained AI models can clean the chaos. But the models only matter if they integrate tightly with systems like ERP, CRM, and order management platforms. That’s where the payoff comes, not from AI itself, but because it’s solving a specific inefficiency inside a critical business system.
Integrating AI into noisy, fragmented workflows isn’t just a good idea. It’s the future of scalable operations. Letting it clean up the mess behind the scenes lets your teams focus forward.
A multi-model AI strategy enhances task-specific performance and flexibility
There’s no one-size-fits-all in AI. Different models have different strengths. Gold Bond recognized this early and didn’t get locked into a single platform. They’re using Gemini for integrated Workspace tasks, ChatGPT for backend automation, Claude for quality assurance, and smaller models for edge experimentation. Each one plays a role based on capability and context.
This approach avoids the limits of platform dependency. It gives the company flexibility to evolve. As specific tasks get more complex, code reviews, document interpretation, QA checks, they can swap or combine models based on precision, speed, and reliability. It’s a smarter allocation of compute, and talent.
From a leadership perspective, this is the kind of scale-conscious thinking that matters. You want teams choosing the right tool for the task, not forcing everything through a single pipeline because that’s what was already licensed. Gold Bond’s IT is mature enough to handle the orchestration, and their people are trained to navigate across platforms when needed.
Matt Price, their CIO, made it clear: they’re tech-agnostic for a reason. It’s not just openness for the sake of it, it’s a way of assuring the company remains adaptable. Models will improve quickly. This strategy avoids getting stuck. It also guarantees the best outcome for each job, without compromise.
Deploying multiple models intelligently shows operational sophistication. It’s not about stacking tools. It’s about matching inputs to the most qualified model and getting results the business can act on fast. That’s where advantage comes from.
AI accelerates creative and technical work through automation and collaborative co-piloting
AI has sped up work that was previously slow or repetitive. At Gold Bond, creating presentations used to take hours. Now it takes under 30 minutes. Developers use AI to audit NetSuite scripts across two models before pushing to test environments. Teams generate spreadsheet formulas that once required manual building. Designers create branded mockups using generative tools. Everything moves faster, without sacrificing quality.
The tools aren’t replacing people. They’re moving them past mechanical steps and into creative focus. AI fills the execution gap. For product visuals, teams use Recraft to iterate on digital previews before human review. Internal documentation is built faster using NotebookLM. Email drafting, phone call summaries, contract pre-reviews, these are now done in minutes, not hours.
For leaders, the shift is measurable. Teams aren’t just busier, they’re more effective because resources are shifting to higher-value work. What used to require multiple rounds of review or coordination is now tackled collaboratively between humans and AI in real-time. Decision loops are shortened. Planning becomes executable sooner.
Matt Price put it clearly, they now cycle through concepts faster, run leaner meetings, and ship without backlog buildup. That’s impact. And it didn’t come from automating everything. It came from identifying where AI could assist and letting human skills handle nuance.
This is where AI delivers real business acceleration: more work handled without adding headcount, and more ideas moving from planning to execution. As technology leaders, it’s our job to push for this kind of output-focused transformation.
Rigorous sandbox testing and human oversight are crucial for safe and effective AI deployment
AI without control is risky. Gold Bond doesn’t put anything into production without solid testing, and that’s the right call. Every AI-driven process first runs in a sandbox, where it’s validated by technical staff and subject matter experts. This dual-approval model ensures that any AI-assisted output maintains business logic, data integrity, and aligns with company expectations before roll-out.
This isn’t theoretical safety, it’s operational discipline. AI systems need structure. Gold Bond built in clear checkpoints to assess performance before introducing change. Even with high-confidence AI tools, they don’t skip quality assurance. That kind of strategic patience prevents mistakes from going public or interrupting downstream systems.
For executives, this is essential. AI doesn’t reduce responsibility, it raises the bar. Without validation, small model failures will scale quickly and create liabilities no one wants. What Gold Bond proves is that functional AI needs guardrails, human review, and controlled iteration.
Matt Price, their CIO, underscored this exact point: “Our technical team, along with the subject matter experts, sign off prior to shipping the changes or integrating to production.” That’s how they maintain platform trust while continuing to innovate.
Staying ahead doesn’t mean going fast without caution. It means executing with discipline and bringing people into the loop at the right moments. That’s how AI reaches maturity inside a business, deliberate action, real oversight, and repeatable frameworks.
Effective AI adoption in legacy companies hinges on intentional and structured change management
AI doesn’t install itself. Especially in an older company, people have habits, and habits don’t change automatically. Gold Bond understood that. They didn’t shove tools into every corner and hope for the best. Instead, they started with a focused team, a group of eight early adopters who tested, refined, then trained everyone else. It was intentional, structured, and ongoing.
They used tools that were easy to access, like Gemini integrated into Google Workspace, so employees could start where they already worked. As workflows evolved, more advanced models like ChatGPT or Claude came into play. That eased technical intimidation and reduced resistance.
Change management wasn’t abstract. It was anchored in use cases, early wins, and peer-driven training. As people began seeing actual impact, behavior shifted, organically. Matt Price focused on making it practical, not theoretical. “After we reset some expectations, people started leaning towards it. Our adoption has taken off,” he said.
And it worked. A quiet shift happened, AI usage jumped from 20% to 71% daily because employees started trusting what the tools delivered. It wasn’t forced adoption. It was earned trust. That matters at scale.
John Pettit, CTO at Promevo, who supported implementation, made the point clearly: “You really have to change people’s thoughts and behaviors around it.” That takes leadership focus, not just tools, but people aligned with the transformation.
For leadership teams, don’t ignore this part. The best model in the world won’t matter if your employees avoid it. Change happens through structure, consistent training, and visible results. That’s what sustains AI adoption beyond pilot stage.
Data controls and usage policies are essential to prevent misuse and manage shadow AI
Running AI without governance invites risks most companies aren’t prepared to handle. At Gold Bond, they took that seriously. Instead of leaving AI usage open-ended, they built strong policies upfront, data loss prevention (DLP), identity-based access, and centralized permission controls using LibreChat. These measures created structure and visibility.
What this does is eliminate unauthorized use of tools, what people call shadow AI. It’s a common problem, especially in companies where enthusiasm overtakes regulation. Gold Bond enforced control over which models could be used, how outputs had to be reviewed, and where approvals were necessary. It stopped problems before they surfaced.
Verification is a mandatory step. Outputs used in customer-facing materials go through checks. If the model pulls in data, the team asks for the source. AI must not be trusted blindly. That level of scrutiny turned verification from a side task into a core part of the workflow.
Matt Price, CIO at Gold Bond, said it clearly: “You have to set the right temperature of trust, but verify.” That equilibrium, being open to value but strict about validation, is what keeps the organization safe and agile.
For executives, this is a leadership responsibility. AI tooling isn’t just a technical decision, it’s a compliance and risk management one too. You don’t need to slow down innovation, but you do have to give it rules. That’s what allows scale to happen responsibly.
AI should augment human decision-making rather than replace it entirely
Even the best models miss key context. That’s why Gold Bond treats human-in-the-loop as non-negotiable. AI enhances output, but final decisions sit with people. Anything public-facing or tied to business-critical logic goes through human review. It’s not about micromanaging the tool. It’s about accountability.
Teams are trained to ask the right questions of the model. Where’s the data from? What’s the reasoning behind the conclusion? And they’re expected to probe outputs before acting. That’s not because AI is flawed, it’s because intelligent oversight makes the system more reliable. Blind trust in synthetic results isn’t responsible leadership.
Matt Price emphasized this point directly: “Agentic solutions can only go so far, there still need to be humans in the loop.” He’s not being cautious; he’s being correct. There’s value in what AI produces, but business outcomes are still owned by people, not models.
Executives need to approach AI as decision support, not decision delegation. Augmentation works when humans remain engaged, steer outcomes, and take ownership of the impact. It’s not just about keeping people involved, it’s about improving results by combining speed with judgment. That’s what delivers trustable, scalable execution.
Start small and iterate to build a sustainable foundation for AI adoption
Launching AI into an organization doesn’t require massive upfront investment or a sweeping transformation plan. What matters is starting with specific, manageable use cases, testing them, and building from what works. That’s exactly how Gold Bond approached it. They didn’t try to overhaul everything at once. They chose real problems with immediate value, then refined, tested, and scaled those solutions step by step.
CIO Matt Price made it clear that simplicity was key to traction. Instead of deploying every model at once, his team focused on getting a few workflows right, things like data entry, document parsing, and internal planning support. These early projects established a track record and created internal trust. Then the adoption expanded naturally from there, powered by demand rather than pressure.
Team members were encouraged to experiment with detailed prompting, discover edge cases, and get comfortable iterating until the outputs made sense. It wasn’t driven by hype, it was grounded in business logic. Once the teams saw results, like hours saved on presentations or smoother intake into ERP, confidence grew. That made future adoption easier and faster.
Price summed it up well: “Provide detailed prompting, test it, play around with it.” It’s a direct approach that works. Success came from doing, adapting, and repeating, not waiting for the perfect solution.
For C-suite leaders, this is the priority. Don’t overload your roadmap with AI ambition that hasn’t been tested. Start with what you can measure, build with what you can prove, and support learning at every level. AI compounds in impact when rolled out deliberately and refined in motion. That’s how you build durable, enterprise-grade capability.
In conclusion
AI isn’t a magic switch. It’s a system change. And like any system change, success depends less on the tech itself and more on how you deploy it, who owns it, and how tightly it integrates with real workflows. Gold Bond’s approach works because it’s deliberate. They didn’t chase hype. They solved problems.
For executives, the takeaway is clear: lead AI like infrastructure. Build with discipline. Roll out in controlled stages. Keep humans in the loop. Don’t expect instant transformation, expect measurable improvement, process by process.
The companies that will win with AI aren’t the ones with the most tools. They’re the ones with the right usage, the sharpest playbooks, and the clearest understanding of where AI supports, not replaces, real work. Lead from that position, and the results will follow.
