Human-in-the-Loop AI Strategy for Distribution

On March 26, 2025 / By Olga Yurchak

Human-in-the-Loop AI Strategy for Distribution

On March 26, 2025 / By Olga Yurchak

The concept of human-in-the-loop AI and why it matters for distributors

Distribution companies are intrigued by artificial intelligence, but most struggle with implementation approaches that either remove human expertise too quickly or fail to leverage AI capabilities effectively. The “human-in-the-loop” strategy offers a practical middle ground that minimizes risk while delivering immediate value.

This approach, sometimes called “augmented AI,” keeps humans in charge of critical decisions while delegating specific tasks to AI systems. It’s not about replacing workers with technology but rather enhancing human capabilities through automation and streamlining the bottlenecks.

Ashwin Rao, Chief AI Officer at QXO, during the recent MDM AI Summit, explicitly advocated for this method: “Today we should focus on what’s called augmented AI and not autonomous AI. So what augmented AI means, you’re augmenting a human with AI. This means a human should be in charge who delegates simple mundane tasks to AI for efficiencies.

For distributors specifically, this matters because AI implementations often fail when they attempt to automate complex processes without sufficient human oversight. Data quality issues, unexpected scenarios, and customer-specific requirements all benefit from human judgment, and experience. A balanced approach lets distributors capture immediate efficiency gains in areas like inventory management, customer service, and pricing while maintaining control over critical business decisions.

The benefits of human-in-the-loop AI for distributors include:

  • Faster implementation timeframes compared to more complex autonomous systems
  • Lower initial investment with the ability to scale as results prove successful
  • Reduced risk of errors that could damage customer relationships
  • Improved employee adoption as teams learn to work with AI rather than fear replacement by it
  • Building organizational capability and confidence in AI applications over time

As distribution companies navigate their AI journey, this balanced approach provides a practical starting point that aligns with both technological capabilities and business realities.

Augmented AI vs. Autonomous AI: A Framework for Implementation

Understanding the distinction between augmented and autonomous AI provides distributors with a clear implementation framework. This distinction forms the foundation of practical AI integration strategies within distribution operations.

Augmented AI keeps humans firmly in control of key processes while leveraging AI to handle specific tasks that can be delegated. This creates a partnership where human expertise guides the AI rather than being replaced by it. The human remains responsible for critical decisions while the AI handles data processing, pattern recognition, and other supporting functions.

This means a human should be in charge and delegate simple mundane tasks to AI for efficiency. Over time, those simple mundane tasks could become more complex, but this model is very important for humans in charge.

This measured approach contrasts with autonomous AI, where systems operate independently without human intervention. While autonomous AI represents a likely future state for many distribution operations, it remains premature for most applications in today’s distribution environment.

A gradual progression toward greater AI autonomy makes more practical sense. Build your AI systems so that you can gradually progress from augmented AI to autonomous AI in about five to 10 years, or maybe three years, or sooner, if AI is progressing fast and is accurate or your company is comfortable with fuller adoption.

This progression allows distribution companies to develop confidence in AI capabilities through direct experience. During this period, the AI continues learning from human interactions, developing more sophisticated capabilities, and earning greater trust from the organization.

The staged approach aligns with the practical needs of distribution companies, who often cannot afford disruptions to key operational processes during technology transitions. By keeping humans involved with critical decisions, distributors can implement AI without jeopardizing customer relationships, inventory management, or other essential functions.

A particularly important principle within this framework: start by automating less business-critical functions, then gradually expand AI authority as systems prove their reliability. This minimizes risk while still capturing efficiency gains in appropriate areas of the business.

The three-phase implementation approach

A structured implementation approach helps distributors move from concept to practical application of human-in-the-loop AI. This three-phase strategy creates a roadmap for organizations at any stage of AI maturity.

Augmentation focus: Starting with simple delegation

The first phase focuses on identifying specific tasks that can be delegated to AI while maintaining human oversight. These initial implementations typically target repetitive, data-intensive processes where AI can enhance human capabilities.

Keeping a human in the loop and using the human experts on your team to assist with this is really what’s going to help that AI agent get better and better over time.

Distribution companies can start with applications like:

  • Simple product categorization and data cleansing
  • Basic customer inquiry responses with human review
  • Initial demand forecasting that informs but doesn’t automatically control inventory
  • Data analysis that produces insights for human decision-makers
  • Converting PDF and paper documentation into electronic format

These applications generate immediate value while providing a safe testing ground for AI capabilities. They also help build employee comfort with AI tools before expanding to more complex implementations.

Governance structure: Creating oversight mechanisms

As AI applications expand, establishing formal governance becomes essential. This phase focuses on creating clear policies, verification processes, and accountability systems.

First off, from an AI perspective, the old cyber adage: trust but verify,” noted David McKee, Chief Technology and Information Security Officer at Turtle, during the MDM AI Summit. “You don’t give AI complete, absolute autonomy within your data set. You still have to have human oversight. There still has to be governance and guardrails.

Key governance components include:

  • Documented policies on where AI can operate independently versus where human review is required
  • Verification processes to check AI outputs before implementation
  • Clear accountability for AI-assisted decisions
  • Regular auditing of AI performance and impact
  • Security protocols for AI data access

Johnny LeRoy, Senior VP and CTO at Grainger, reinforced this point during the Summit: “Governance is super important. We’ve got a pretty robust governance process, particularly in picking which use cases and which tools and vendors and groups are there.

Expertise evolution: Building team capabilities

The third phase focuses on developing internal capabilities to effectively work with AI systems. This includes both technical skills and judgment abilities. You’ve got to educate your employees upfront and often. This education extends beyond technical training to include critical thinking about AI outputs.

This expertise evolution includes:

  • Training teams to effectively prompt and guide AI systems
  • Developing verification skills to identify potential AI errors
  • Building comfort with the human-AI partnership model
  • Documenting successful patterns for broader organizational adoption
  • Creating feedback loops where human input improves AI performance

As teams develop these capabilities, the organization can gradually expand AI responsibility in appropriate areas while maintaining human judgment where it adds the most value.

Real-world examples from distribution

Distribution companies across the industry spectrum have implemented human-in-the-loop strategies with varying approaches. Their experiences provide valuable insights for organizations at any stage of AI adoption.

Grainger, a leading industrial supply distributor, has developed a multi-layered governance approach that balances innovation with control. An interesting application at Grainger involves their chatbot implementation. Rather than deploying it directly to customers, they first focused on augmenting their own customer service representatives. This approach reflects the human-in-the-loop principle by enhancing human capabilities rather than replacing them, while gaining experience with the technology before expanding to direct customer interactions.

Allegis Corporation uses AI to analyze customer interaction data. They implemented an approach to analyze customer interaction data to understand correlations between customer engagement and revenue outcomes.

Allegis began by pulling extensive data from their ERP system, examining touchpoints over time with specific customers, and correlating this with revenue performance. They anonymized all data to protect customer information while preserving the substantive insights.

This approach incorporates verification as a core practice. After experiencing instances where AI-processed data differed significantly from expected results, the team established protocols requiring verification of AI outputs. This verification-focused methodology includes double-checking work and refining prompting techniques to improve accuracy.

The Allegis approach exemplifies efficient human-AI collaboration. Their team uses AI to process large datasets (sometimes 100,000+ data points) that would otherwise require days of manual analysis or expensive consulting engagements in the tens of thousands of dollars. This lets them explore data relationships interactively, asking follow-up questions and discovering unexpected patterns.

Practical implementation steps for distributors

Moving from concept to implementation requires a structured approach that breaks down the human-in-the-loop strategy into manageable steps. Here’s how distribution companies can build their AI capabilities while maintaining appropriate human oversight.

Start with an AI assessment

Begin with a comprehensive assessment of your current capabilities and opportunities, including:

  • Data quality and accessibility across your organization
  • Current technology infrastructure and integration capabilities
  • Staff expertise and readiness for AI implementation
  • Security and governance requirements (what data cannot be shared under any circumstance, what data must be anonymized, what AI tools are approved for business use, etc)
  • Specific business processes that could benefit from AI augmentation

This assessment creates a foundation for prioritizing use cases based on potential value and implementation feasibility.

Select initial use cases strategically

Distribution companies should select initial AI implementations that balance impact with manageable complexity. 

Good starting points include:

  • Product data cleansing and categorization
  • Basic demand forecasting with human oversight
  • Customer data analysis to identify patterns
  • Customer service agent augmentation

These applications typically deliver measurable results while allowing the organization to build AI experience in controlled environments.

Establish clear governance from the beginning

Even for initial implementations, establishing governance guidelines prevents issues as AI adoption expands. Effective governance includes:

  • Documenting which processes can be fully automated versus requiring human review
  • Creating verification procedures for AI outputs
  • Establishing data security and privacy protocols
  • Defining performance metrics for both AI systems and human-AI collaboration
  • Setting clear expectations for human responsibility in the partnership

These governance structures should evolve as the organization gains experience with AI implementations.

Invest in employee education and training

Successful human-in-the-loop strategies require both technical implementation and human skill development. Effective training includes:

  • General AI awareness for all employees
  • Specific training on tools being implemented in each department
  • Verification techniques for spotting potential AI errors
  • Prompt engineering skills for effectively guiding AI systems
  • Documentation practices for capturing successful patterns

This education should emphasize that AI is designed to augment human capabilities rather than replace them, helping reduce resistance to adoption.

Conclusion: Building foundations now for long-term AI success

The human-in-the-loop approach provides distributors with a pragmatic AI implementation strategy that balances immediate benefits with long-term capability development. This approach acknowledges both the transformative potential of AI and the continued value of human expertise in distribution operations.

Distribution companies that successfully implement this strategy gain several advantages:

  • Faster time-to-value than organizations attempting riskier fully autonomous implementations
  • More reliable outcomes through appropriate human oversight
  • Better employee adoption by positioning AI as an enhancement rather than a replacement
  • Flexibility to evolve with rapidly changing technology
  • Continuous improvement as human expertise and AI capabilities develop in tandem

The research and real-world experiences demonstrate that distribution companies can successfully implement AI without massive teams of specialists. By starting with targeted applications, maintaining appropriate governance, and gradually expanding capabilities, organizations of all sizes can realize tangible benefits.

As technology continues to evolve, the human-in-the-loop strategy provides a solid foundation that can adapt to new capabilities. This approach allows distributors to stay competitive in the short term while positioning themselves for leadership as AI becomes increasingly central to distribution operations.