Bridging the Gap: How Visibility and Conversational AI Drive Enterprise Automation

Darin Patterson serves as the Vice President of Market Strategy at Make, a platform dedicated to AI agents and automation infrastructure. His primary objective is assisting companies in transitioning from sporadic AI testing to tangible, measurable business transformation. This involves integrating autonomous AI agents with robust automation foundations that ensure reliability in production environments. As a frequent keynote speaker and podcast guest, Patterson advocates for a people-first approach to technology adoption. He emphasizes that organizations should invest in their workforce as heavily, if not more so, than in the AI tools themselves.

Through global travel and engagement with business leaders, Patterson gains direct insight into the practical challenges hindering AI transformation. He uses these interactions to identify what strategies are effective and shares best practices with other organizations aiming for meaningful, scalable adoption. A critical barrier he identifies is "Solo AI," where individual employees experiment with AI independently without sharing results or learning from one another. This lack of collaboration prevents the organization from benefiting from successful experiments.

To address this, Make’s AI Adoption Journey research surveyed 540 respondents across 35+ countries and 16 industries. The data revealed that while 40% of respondents use AI multiple times a day, the average organizational AI automation rate remains around 25%. This statistic underscores the need for work visibility. When teams can observe how AI is utilized and understand the dependencies between different automations and agents, they can identify effective practices and continuously improve them. As Patterson notes, if a process cannot be seen, it cannot be managed; if it cannot be managed, it cannot be scaled. Visibility transforms isolated experimentation into something that can be understood, governed, improved, and replicated across the entire enterprise.

Historically, automation required practitioners to master complex syntaxes and manage messy data structures, which excluded those with valuable business insights but limited coding skills from building solutions. Generative AI has shifted this dynamic by translating natural language into programmatic automations. Users can now describe their desired outcomes in the same manner they would communicate with a colleague, rather than learning technical steps. Conversational AI interprets this intent and generates the underlying logic required to automate processes with consistency and reliability.

This shift expands participation in automation to those closest to the business problems but lacking technical expertise. It allows individuals to focus on problem-solving rather than the mechanics of construction. However, for adoption to spread, AI must become understandable to its users. Many perceive AI as a "black box," creating fear due to a lack of understanding. This uncertainty breeds hesitation. Visibility addresses this by providing both technical and organizational context. Employees need to understand why AI is being introduced, what the organization aims to achieve, how their roles might evolve, and where they fit into the journey.

When people comprehend both the technology’s function and its strategic purpose, they gain confidence and control. This transparency encourages engagement rather than resistance. Users can begin with small use cases, observe AI behavior, and gradually apply it to more complex challenges. As understanding deepens, willingness to experiment and share successful approaches increases.

Maia, Make’s AI assistant, is designed to meet users at their current skill level, removing friction from various stages of the automation process. For complete beginners, Maia significantly reduces the time between idea generation and execution. This allows users to dedicate more effort to understanding their business problems rather than the structural specifics of automation systems.

For experienced users, Maia accelerates workflows by enabling faster optimization of existing processes, adaptation to changing customer needs, or comprehension of complex automations built by others. By bringing generative AI closer to the automation process, it empowers experts to iterate quickly and efficiently.

To ensure automations created by non-technical employees remain reliable, auditable, and scalable for enterprise use, organizations must provide accessibility without sacrificing structure, visibility, and governance. While there may be hundreds of ways to automate a single process, only a fraction are genuinely scalable. Clear guidance and best practices help users build effective solutions without needing to master every technical consideration. Make’s visual approach allows teams to understand what has been built, see how processes interact, and maintain oversight as automation scales. This enables organizations to empower more employees while retaining the controls necessary for confident scaling.

The primary reason employees hesitate to adopt AI tools is overwhelm. The rapid emergence of new tools and capabilities creates anxiety about learning new skills. Employees are often not resistant to learning but are paralyzed by the volume of information and uncertainty about where to start. Patterson advises starting with small, manageable problems to create opportunities for experiential learning and gradual confidence building.

Real-time visual logic supports this by creating a continuous learning loop. As users see the logic behind their automations in real time, they gain clarity on how the system operates. This visibility demystifies the process, reducing fear and encouraging iterative improvement. By combining accessible tools with clear visual feedback, organizations can foster an environment where AI adoption is both sustainable and empowering for all skill levels.