Chloe Maraina stands at the intersection of big data and practical business application, bringing a visionary perspective to how organizations handle their most valuable information. As a Business Intelligence expert with a deep-seated passion for data science, she has dedicated her career to transforming complex datasets into compelling visual stories that drive executive decision-making. Her expertise is particularly relevant today as companies struggle to move beyond the experimental phase of artificial intelligence. By focusing on the seamless integration of data management and no-code automation, Chloe provides a roadmap for businesses looking to harness the power of AI without sacrificing the stability of their core operations.
In this conversation, we explore the high failure rate of modern AI initiatives and the strategies required to move these tools into active production. We discuss how empowering frontline workers through no-code environments can bridge the gap between technical potential and daily utility, particularly in field service and industrial settings. Chloe also addresses the risks of “shadow AI” and the importance of maintaining strict data governance within a centralized platform. Through her insights, we gain a clearer understanding of how to build AI-enhanced workflows that are not only efficient and safe but also sustainable for long-term organizational growth.
With nearly 95% of AI projects failing to produce a return on investment, what specific operational missteps cause these tools to remain stuck in the experimental phase? How can leaders ensure new technology doesn’t disrupt the brittle workflows that employees rely on for their daily tasks?
The primary reason we see such a staggering failure rate is that many AI tools are introduced in a vacuum, completely disconnected from the actual operational workflows that keep a business running. According to recent MIT research, only about 5% of these projects reach production because they suffer from a lack of contextual learning and are fundamentally misaligned with day-to-day tasks. When a workflow is “brittle,” even a minor technological shift can cause a total breakdown in productivity, leading employees to abandon the tool almost immediately. Leaders must realize that they don’t need to completely reimagine their entire business model to see value; instead, they should focus on embedding AI directly into the existing steps of a process. Success comes when the technology acts as a supportive layer that helps a user perform their specific job more effectively, rather than forcing them to learn an entirely new, unproven way of working.
Frontline employees often have the clearest perspective on where automation is actually needed to improve their efficiency. How does moving AI development into a no-code environment change the implementation process, and what specific guardrails should be in place to keep sensitive company data safely contained?
Shifting to a no-code environment like Flowfinity democratizes the innovation process because it puts the power of AI into the hands of the people who actually understand the problems being solved. These frontline workers can configure applications to automate repetitive data entry or summarize long documents without needing a team of developers, which drastically speeds up the deployment cycle. However, this freedom must be balanced with centralized control to ensure that sensitive company data doesn’t leak into the public domain. By using a dedicated no-code platform, an organization can create built-in guardrails that keep all data interactions within a secure, managed ecosystem. This ensures that while the employee is building a custom solution to a niche problem, the underlying data remains governed by the company’s strict security standards and privacy protocols.
In field service environments, AI can assist with troubleshooting by summarizing service records or formatting customer reports. What are the step-by-step requirements for integrating these capabilities into a technician’s mobile workflow, and how do you measure the resulting impact on their reliability and speed?
Integrating AI into a technician’s mobile workflow requires a highly tactical approach that mirrors their physical movement through a job site. First, the system should allow the technician to pull up past service history, using AI to summarize years of inspection records for a specific machine into a few actionable bullet points for troubleshooting. Second, as the technician identifies the issue, the platform should offer potential solutions in context, providing direct links to repair instructions or necessary next steps. Third, once the work is done, the AI should automatically compile voice notes and form entries into a clean, formatted customer report, which removes one of the most tedious parts of the job. We measure the success of this integration by looking at the reduction in “time-to-fix” and the increased accuracy of the final reports, ensuring the technician feels more reliable while working significantly faster.
Many workers currently turn to external, unapproved AI assistants because their native workflows lack these capabilities. What are the primary risks of this “shadow AI” behavior, and how can organizations successfully embed AI directly into existing platforms to provide more contextual, reliable assistance?
The rise of “shadow AI” is a direct response to employees feeling that their company-provided tools are outdated or inefficient, but it creates a massive security liability. When an employee pastes proprietary data or customer information into an external, unapproved AI assistant, the organization loses all control over where that data goes or how it is used to train future models. To combat this, organizations must provide “native” AI assistance—meaning the intelligence is built directly into the platforms the employees already use for their daily tasks. By embedding these capabilities, you ensure the AI has the necessary context to be truly helpful, such as knowing the specific equipment a technician is looking at. This provides a safe, reliable alternative to external tools, keeping data within the corporate firewall while giving employees the modern assistance they crave.
Maintaining control over data is a significant hurdle when deploying intelligence tools at scale. How can a centralized platform balance the need for employee-led innovation with strict security standards, and what specific metrics indicate that an AI-enhanced workflow is truly sustainable for long-term growth?
A centralized platform achieves balance by providing a “sandbox” of approved tools and data sets where employees can innovate without risking the integrity of the core infrastructure. This architecture allows a manufacturing or utility company to scale AI across various departments—from facilities management to engineering—while maintaining a single point of oversight for security and compliance. To determine if these workflows are sustainable, we look for metrics beyond just immediate speed; we track the “usefulness” and “reliability” of the AI outputs over time to ensure they aren’t producing errors that require human cleanup. A truly sustainable workflow is one where the AI assistance consistently reduces the cognitive load on the employee, allowing the business to grow its operations without a linear increase in administrative overhead.
What is your forecast for the future of AI in business processes?
I believe we are moving away from the era of “AI for the sake of AI” and toward a period of extreme pragmatism where the most successful companies are those that prioritize useful, reliable, and safe integration. In the next few years, the 95% failure rate we see today will drop significantly as organizations stop trying to build massive, standalone AI systems and instead focus on small, impactful improvements within their current no-code platforms. We will see AI become an invisible but essential part of the workforce, handling everything from speech-to-text conversion in the field to complex data mining in the back office. Ultimately, the future of business process automation isn’t about replacing the human element, but about using embedded intelligence to remove the friction from daily work, allowing people to focus on higher-level problem solving and innovation.
