Case Study: How GadellNet Consulting Services Helped a Growing Creative Services Firm Build AI Governance Without Losing Momentum

Innovation with Guardrails: How GadellNet Consulting Services Helped a Growing Creative Services Firm Build AI Governance Without Losing Momentum

by Mary Dougherty (TechPoint) and Rachel Rizzuto (GadellNet Consulting Services)

The question was never whether to use AI. It was how to use it responsibly while people across the company were already using it in their own way.

That was the situation facing a Midwest-based business-to- business (B2B) creative services firm that helps enterprise clients turn complex ideas into sales presentations, pitch decks, corporate videos and high-impact event materials. As AI tools became easier to access, employees across the organization started experimenting on their own, from using prompts to speed up creative work to testing early workflow automation. The energy was real, but so was the risk. The firm needed a way to keep momentum going while making sure the work stayed safe, consistent and trustworthy for clients.

The firm brought in GadellNet to support the effort both the technology and the people. GadellNet Consulting Services is a managed IT and consulting firm based in St. Louis, with additional offices in Indianapolis and Denver. The company works with small businesses, schools and nonprofits, offering everything from day-to-day IT support to cybersecurity and strategic technology consulting. GadellNet’s strategic consulting team came into this engagement to help the firm think through AI strategy rather than just the tools themselves.

Overview

Instead of starting with a rulebook, GadellNet met the firm where it was. Employees in different departments, including creative, video, account, copy and art, were using AI tools in different ways with no shared view of what was happening or how much risk any of it carried.

 Over several months (February 2026-present), GadellNet helped the firm:

• Publish a plain-language, partner-facing statement explaining how the company uses AI in client work.
• Establish an AI policy, governance practices, usage guidance and organizational guardrails, and just as importantly, shape client-facing transparency language that made clear how AI would and would not be used in client work.
• Document every active AI use case, including who owns it and how much risk it carries. During the discovery and planning activities, the organization identified 11 active AI use cases spanning multiple business functions across multiple teams.
• Begin company-wide training and department-level knowledge sharing

The engagement moved the firm from scattered experimentation toward a more mature and repeatable way of using AI, one that protects client trust without shutting down curiosity.

Industry Context & Business Challenge

In the B2B creative and presentation services industry, AI can add significant value as it can speed up research, content development, image and video ideation, proposal support and production work.  AI adoption also raises real questions about client trust, brand consistency, data sensitivity, legal expectations and how much human review is enough.

Before the engagement, AI use at the firm was uneven. Some teams used advanced tools for specialized creative and production work, while other employees with access to the same tools had not built AI into their daily routines at all. Leadership lacked a single view of who was using which tools, what those tools were being used for, which outputs needed a human check before going to a client and how to explain any of this to partners.

Leaders wanted to encourage innovation, not slow it down. At the same time, they knew AI exploration was already costing real time and money, staff hours, consulting support, software subscriptions and legal review. Without a roadmap, all that investment risked scattering in different directions instead of building toward something the company could reuse.

AI Implementation Strategy

GadellNet used a flexible, phased strategy that worked with where the team was instead of forcing a rigid, one-size-fits-all rollout. The work started with the firm’s most immediate need: a public-facing AI statement that could build trust with partners and set clear boundaries. As the AI adoption process expanded across multiple teams, the firm wanted to ensure clients, employees and stakeholders understood how AI would be used, where human oversight remained essential, and what guardrails governed AI-assisted work.  This outward-facing commitment meant little without the structure to back it up.  As a result, GadellNet quickly pivoted to focus on internal governance, use case documentation, training and tool guidance, the pieces that turned a statement of trust into something real.

A key early decision shaped everything that followed. Rather than naming one approved AI tool for the whole company, GadellNet moved the firm toward a tool-agnostic, usage-based framework. In plain terms, this meant employees could use approved corporate accounts for whichever tool fit the job, for that type of use, covering risk, data handling, ownership and how much human review was required before anything reached a client.

Solution and Implementation

The solution combined governance, training and practical workflow design. GadellNet helped the firm define the outer edge of acceptable AI use first, then worked backward into the systems. Documentation and training needed to support that boundary over time. The firm completed the following steps.

• Developed a partner-facing AI statement: the firm published this language for clients and partners: “We leverage secure, enterprise-grade AI technologies to improve efficiency and insights while protecting confidential information and maintaining rigorous human review standards. AI supports our work, but our experts remain accountable for every final deliverable.”
• Facilitated conversations across teams using AI in different ways, including creative, video, account, copy and art functions, along with executive leadership, operations and internal workflow stakeholders. Together, these conversations helped with the review of AI usage across departments to understand current adoption patterns and identify opportunities for future governance and enablement.
• Supported legal review by drafting language the firm could adapt into formal terms and conditions for AI usage.
• Combined policy, technical controls, training and accountability into one governance approach.
• Shifted from tool-first governance to usage-based governance, allowing creative teams to keep working across a growing set of specialized tools while consolidating business and operations staff onto a single enterprise Copilot account for their core job functions.
• Documented active and emerging AI use cases in a usage matrix that tracks owners, tools, data sensitivity, output impact, risk level, human review needs and approval stage.
• Supported early workflow development, including a shared, tool-agnostic AI workspace to help the account team build bids, quotes and related materials.
• Prepared organization-wide enablement through an all-staff AI update, department-level sharing and employee trainings.

Implementation Realities and Course Corrections

The work did not follow a perfectly straight line, and that is part of what makes it a useful example for other organizations. The firm moved quickly, involved many stakeholders and had departments at very different stages of AI maturity. Rather than slow things down, GadellNet helped leaders build just enough structure to keep progress safe and visible while still supporting experimentation.

• The firm’s first priority was the external AI statement before a full internal governance program was ready. GadellNet supported that need first, then connected it back to internal accountability.
• A single-tool governance model did not fit every specialized creative use case. Because of the nature of creative work, the total number of tools used across the organization actually grew over the following six months, so GadellNet adjusted the framework to focus on approved accounts, use case risk, data sensitivity and output review instead of any one product.
• For business and operations staff, the opposite move made more sense. GadellNet consolidated their day-to-day AI use down to a single enterprise Copilot account, which cut software spend, removed the use of personal accounts and strengthened the firm’s overall security and governance posture
• Teams needed more visibility into what other departments were doing. GadellNet helped build a usage matrix so employees could see what was being built, which tools were involved and which safeguards applied
• The firm learned that AI exploration takes real investment: staff time, consulting support, subscriptions and legal review. GadellNet helped turn that investment into a repeatable roadmap instead of a one-time push

Outcomes

The engagement moved the firm from broad, scattered experimentation toward a more mature and scalable way of operating with AI. The organization keeps exploring new possibilities, but now has clearer boundaries, shared language and a stronger path toward using AI responsibly.

• Clearer partner trust: the firm published a public-facing AI statement that explains how AI supports the work while reinforcing human review, client comfort and brand integrity
• A stronger governance foundation: the firm now has a way to connect policies, approved tools, technical controls, training, risk levels and accountability into one framework. The framework moved through four phases, discovery, strategy, training and governance alignment, all aimed at helping the organization shift from scattered individual AI experimentation to a consistent, scalable model built on clear ownership and training.
• Repeatable framework: at the center of that training was a simple repeatable framework: Goal, context, expectations and source. This framework helped users sharpen their prompts and apply human review before any AI-generated content made it into production.
• Better visibility across teams: the AI usage matrix provides a shared way to document and review use cases, owners, tools, data sensitivity, output impact and expectations.
• More practical enablement: company-wide sharing and targeted training help employees learn from each other, reduce duplicated effort and move good practices out of isolated teams and into broader use.
• Momentum without a hard stop: GadellNet helped the firm continue to explore AI while building the review structures needed to protect both clients and the business.
• Since the work is still unfolding across many parts of the business, it’s too early to put a firm number on total return on investment. Still, one result stands out as an early proof point. With baseline governance in place, the firm felt confident enough to take a workflow that started as an internal experiment and bring it to market, with a soft launch in the second quarter of 2026 and a full launch expected in the third quarter.

The firm’s experience also points to a broader lesson for the market. For a smaller agency competing against larger firms, using AI responsibly can help level the playing field by strengthening internal efficiency and improving the value delivered to clients and prospects.

Conclusion

AI adoption does not succeed through technology alone. This engagement shows how a growing B2B creative services firm pursued AI while still protecting client trust, brand quality and data responsibility. GadellNet helped the organization define its boundaries, organize its use cases and build a governance path that simultaneously supports both innovation and accountability.

 The strongest takeaway is a simple one. Organizations do not have to choose between moving fast and staying safe. With the right partner and people, they can keep experimenting, put practical safeguards in place and turn early AI activity into something built to last.

Key Takeaways

• Meet people where they are: if employees are already experimenting with AI, build guardrails around that momentum instead of pausing it to write a policy from scratch
• Start with the outer edge: define what is off limits and what requires human review
• Govern the use, not just the tool: a framework built around risk, data sensitivity and review needs will hold up better than one tied to a single approved product
• Expect the plan to shift priorities will move as departments reach different stages of AI maturity

A note on process transparency: This case study was drafted with AI assistance, then reviewed and edited by human authors.