How We've Learned to Roll Out AI (And Why It Actually Works)

Last week, Hallian Technologies trained 70 engineers at a Minnesota-based firm on our HallianAI platform.

What made this training different from ones we conducted a year ago wasn't the content. It was the strategy.

When we first began rolling out HallianAI to clients, our approach was straightforward: explain the technology, walk people through the mechanics of AI, answer questions, and hope adoption would follow. It seemed logical. The more people understood how the system worked, the more confident they'd feel using it.

That assumption was partially correct. But it missed something critical.

Over the past year, through dozens of client engagements, we've discovered that understanding the technology is only part of the equation. Adoption happens when you connect theory to concrete examples, demonstrate real solutions that peers are already using, and systematically remove friction from the path to actual usage.

This is what that looks like in practice.

THE CHALLENGE WITH TRADITIONAL TRAINING

Our early rollout approach followed a predictable pattern:

The AI expert from Hallian would stand in front of the room and explain how HallianAI works. Large language models. Retrieval-augmented generation. The architecture of the system. The capabilities and limitations

The training was thorough. Engagement appeared high and companies expressed enthusiasm about getting started, but the results were inconsistent. Some firms quickly became proficient users, identifying new workflows and expanding their use of the platform. Others adopted it slowly, using it sparingly for basic tasks, and rarely exploring its full potential.

The difference wasn't the quality of the platform or the expertise of the trainer. The difference was whether employees felt capable of using the system independently.

When the AI expert is the central authority—the person who explains everything, demonstrates everything, answers all the complex questions—you create a subtle but significant dynamic: dependency rather than ownership.

Employees don't feel equipped to solve problems on their own. They feel they need the expert to return when they want to do something new. And that perception directly inhibits adoption.

THE ECONOMICS OF ADOPTION

The insight that drove our approach change came from examining ROI across our client base.

Adoption correlates directly to return on investment. This isn't surprising, but it's worth stating clearly: if a team doesn't use the tool, they don't save time. They don't uncover workflows specific to their business. They don't realize the value they paid for.

If a team doesn't use the tool, they don't save time. They don't uncover workflows specific to their business. They don't realize the value they paid for.

Conversely, firms where adoption rates are high report significant time savings, identify new operational efficiencies, and find opportunities to apply AI to problems they hadn't initially considered.

This created a clear imperative: We needed to improve our adoption strategy. Not the platform or the training content, but the strategy itself.

We began examining what actually drives people to adopt new tools in professional settings. The research and our own observations pointed to a consistent pattern: professionals adopt tools when they see credible peers using them successfully, when they understand how the tool applies to their specific work, and when they can begin using it in low-stakes ways that don't require expert intervention.

A DIFFERENT APPROACH

The strategy we've developed over the past year now looks like this:

Phase 1: Pre-Deployment Use Case Development

Before we conduct company-wide training, we work directly with the client's leadership team—the construction managers, engineering directors, project leads, and other practitioners who understand their workflows most deeply. Rather than simply teaching them how to use HallianAI, we collaborate to build real use cases that solve actual problems in their business.

This phase typically takes 6-12 weeks. We identify workflows that are currently manual and time-consuming. We create AI assistants and agents inside HallianAI that address those specific pain points. By the time we're ready for company-wide training, we have working examples of how the platform solves real problems.

Phase 2: Peer-Led Company Training

Instead of the AI expert demonstrating the platform, the client's own leaders demonstrate it.

The construction manager shows how a custom AI assistant turns field notes into professional daily reports. The engineering director shows how an AI agent searches years of historical data in seconds. The project manager shows how a custom workflow streamlines a recurring task.

This shift changes everything. Employees see the platform not as abstract technology explained by an outsider, but as a practical tool that their colleagues—people who understand their business—are already using effectively.

Phase 3: Guided Adoption Support

Training doesn't end in the meeting. We follow up with a structured series of personalized communications designed to guide each user from awareness to proficiency.

THE MINNESOTA CASE STUDY

The training we conducted last week demonstrates how this approach works.

The Setup

Over two months, we worked with the firm's leadership team. We identified key workflows that consumed significant time and expertise. We built custom solutions within HallianAI to address those workflows.

Use Case 1: Daily Construction Reports

The firm's construction manager was spending approximately 45 minutes each day converting field notes into structured, professionally formatted daily reports. It's essential work—the reports document site conditions, progress, and decisions—but it's also highly repetitive.

We built an AI workflow that takes field notes as input and generates a complete daily report. The assistant knows the structure required, the terminology the firm uses, and what information needs to be captured. The construction manager still reviews each report before sending it, but the generation and formatting take minutes rather than 45 minutes.

Use Case 2: Historical Report Search

The engineering director needed to quickly access information from years of past projects—previous design decisions, cost data, lessons learned. The firm's archive was extensive but unstructured, making searches time-consuming and incomplete.

We helped build an AI agent that searches the entire document repository instantly, pulling relevant information regardless of how it was originally stored or formatted. An engineer can now ask a question in natural language and get comprehensive answers from the firm's historical knowledge base.

The Training

Seventy (70) employees attended the company-wide training session. The construction manager demonstrated his daily report assistant. He showed the input (raw field notes), walked through the process, and displayed the output (formatted report). He discussed how the tool fits into his workflow and what limitations he's discovered. He answered questions from people who work with him daily.

The engineering director demonstrated the historical search tool. He performed live searches showing the speed and relevance of results. He explained what types of queries work best and what the engineering team has learned about using the system.

Neither demonstration was about HallianAI's capabilities in the abstract. Both were about solving specific problems that employees in that room understood intimately.

WHY THIS APPROACH WORKS

The effectiveness of this strategy rests on several factors:

• Credibility – Peer recommendations carry more weight than expert recommendations. When a respected colleague demonstrates a tool, people believe it can actually work in their context.

• Contextual Clarity – Seeing how a tool applies to actual work is more persuasive than understanding how the technology functions. Employees understand the specific value because they see the specific application.

• Reduced Friction – When employees understand that the tool was built to solve problems they recognize, they're more motivated to start using it. They see an immediate purpose, not an abstract new system to learn.

• Sustained Momentum – A single training event creates awareness. Ongoing guidance—specific tips, walkthroughs, examples—converts awareness into habit. By the third or fourth personalized follow-up message, employees have received guidance appropriate to their role and the tasks they need to accomplish.

STRUCTURED FOLLOW-UP

The training event itself is only the beginning of the adoption process.

Following the 70-person training, we developed a customized email sequence for all participants. Each email targets specific user groups—operations staff, maintenance personnel, project managers, engineers—with role-specific guidance.

The emails cover practical topics: how to upload files, how to craft effective prompts, how to use custom personas, how to build your first workflow. They provide step-by-step instructions for low-stakes activities where learning feels achievable.

Importantly, these follow-ups shift the burden of adoption from the individual ("I need to figure this out") to the organization ("Here's specifically how you use this for your role"). That distinction drives significantly higher engagement rates.

WHAT WE'VE LEARNED

Twelve months and dozens of deployments have reinforced several key principles:

Theory matters, but only when anchored to application.

We still cover how large language models work, how retrieval systems function, and how agents are constructed. But we do so in the context of actual use cases employees will execute. Abstract explanation of technology creates understanding. Explanation connected to their work creates adoption.

Peer authority exceeds expert authority.

Employees trust the judgment of colleagues who share their operational reality more than they trust the judgment of an external expert. This doesn't mean expert guidance is unimportant—it means expert guidance is most effective when it empowers peers to become authorities.

Adoption is a process, not an event.

One training session—no matter how well-designed—doesn't drive adoption. A structured sequence of touchpoints that move people from awareness to confidence to independent capability does.

Low-stakes engagement precedes confident usage.

When people are encouraged to try simple tasks before attempting complex ones, success builds on success. When they're immediately expected to build sophisticated workflows, many become discouraged.

THE BROADER IMPLICATION

These lessons extend beyond training methodology. They reflect a fundamental shift in how Hallian Technologies approaches client relationships.

We don't view ourselves as a software vendor who provides a product and support. We view ourselves as implementation partners responsible for ensuring you achieve the outcomes you purchased the platform to achieve.

That responsibility manifests in how we work with your leadership team before company-wide rollout. It manifests in how we structure training. It manifests in the follow-up guidance we provide. It manifests in how we monitor adoption metrics and identify new opportunities.

Your adoption is our adoption. Your success with HallianAI is our success.

This philosophy has evolved over the past year as we've learned what actually drives adoption in firms like yours. We'll continue to refine our approach as we work with more clients and gather more data about what works.

WHAT TO LOOK FOR IN AN AI VENDOR

If you're evaluating HallianAI or considering AI implementation for your firm, ask potential vendors about their adoption strategy:

• How do you approach initial training? Is it a single event or a structured process?

• Do you work with our leadership team to develop relevant use cases before company-wide rollout?

• What does post-training support look like? How do you ensure adoption continues after the initial training?

• Can you share examples of adoption metrics from comparable firms?

• How do you identify opportunities for expanded usage once initial training is complete?

The vendor who can answer these questions with specific methodologies and examples understands implementation. The vendor who focuses primarily on product features and capabilities may sell you good software, but may not ensure you actually use it.

NEXT STEPS

Hallian Technologies has developed a repeatable, structured approach to HallianAI deployment that prioritizes adoption from day one.

If you'd like to understand how this would work for your firm—what the timeline looks like, what the training process entails, how we work with your leadership team—we're happy to discuss.

The video above shows the real-world application of this methodology. If you'd like to explore how a similar approach could support your firm's AI adoption:

Schedule a conversation with our team →

We believe adoption is the measure of success. Let's talk about what that means for you.