Kognitos is attending ITC Vegas, October 14-16 in Las Vegas. See us at booth #454 or join us for dinner at Momofuku.
Kognitos is attending ITC Vegas, October 14-16 in Las Vegas. See us at booth #454 or join us for dinner at Momofuku.
The first 24 hours after a workplace injury are the most critical. Yet for most carriers, this period is defined by a slow, manual process that delays claim registration by an average of 7-14 days. This initial bottleneck is more than just an inconvenience; it’s a massive financial drain. Inefficient manual processing adds an average of $2.3K in administrative costs to every single claim. When the National Safety Council reports that the average cost of a medically consulted injury is already tens of thousands of dollars, an extra $2,300 in pure administrative waste is unacceptable.
These delays and costs stem from a broken intake process. With initial claim submission error rates hitting 40% , skilled claims teams are forced to spend 60-70% of their time on low-value administrative work instead of managing claims. This hurts the carrier’s bottom line and, most importantly, delays an injured employee’s access to care.
The traditional journey for a workers’ compensation claim is a chain of manual handoffs and reviews.
Each step is a potential point of failure, introducing errors and delays that prevent the claim from reaching the right handler quickly.
Kognitos transforms this inefficient workflow by automating the entire process, turning days of work into minutes. It’s a seamless, three-step journey powered by agentic AI.
This direct connection to your core systems ensures a smooth, error-free flow of information, eliminating the manual bottlenecks that cause backlogs.
Automating the claims intake process creates a powerful positive impact across the board.
For the Injured Employee: The most significant benefit is speed. Faster claim registration leads to quicker confirmation of benefits and faster access to necessary medical care. This reduces the stress and uncertainty for an employee during a difficult time, creating a vastly improved and more compassionate experience.
For the Carrier: The operational and financial benefits are transformative. By eliminating the $2.3K in excess costs per claim and cutting the 40% error rate, the return on investment is immediate. More importantly, it frees your skilled claims handlers from administrative burdens. Instead of chasing paperwork, they can focus on strategic claim management, helping employees with return-to-work plans, and mitigating complex claim escalations. This boosts efficiency, improves outcomes, and increases employee satisfaction.
For an insurance carrier, the agent distribution channel is the engine of growth. But that engine can’t run if new agents are stuck in neutral. The current process for agent onboarding is a time-intensive journey that often takes weeks, creating a direct and damaging delay in revenue generation. Every day an agent waits for approval is a day of lost sales opportunities, reduced market penetration, and a tangible competitive disadvantage.
This delay is caused by a reliance on outdated, manual processes to handle complex and sensitive documents. From handwritten forms to verifying multiple licenses and compliance certificates, the manual workload creates bottlenecks, strains resources, and puts carriers at a disadvantage in the race to attract top talent.
The traditional agent onboarding workflow is a multi-step process that grinds to a halt when it hits the verification and approval stages. While the initial steps of expressing interest and filling out forms are straightforward, the process quickly becomes a manual burden.
Agents are required to submit a host of critical documents, including:
The final three steps- Document Verification, Final Approval, and System Entry—are where carriers lose precious time. Staff must manually verify every detail against an onboarding checklist before an agent is finally approved and their data is manually keyed into the system. This manual verification is the primary source of administrative overhead and error-prone processes.
Kognitos uses AI to transform the most time-consuming parts of this workflow, automating the verification, approval, and data entry stages. This allows carriers to shrink the onboarding timeline from weeks to mere days.
Here’s how it works:
By targeting the exact steps where manual processing creates delays, Kognitos allows agents to become productive and start generating revenue faster than ever before.
Shrinking the onboarding timeline delivers a powerful competitive edge and a clear return on investment.
For the Carrier: The primary benefit is accelerated time-to-revenue. By activating agents weeks sooner, you can increase market penetration and gain an edge over slower competitors. Automating the process also dramatically reduces the administrative overhead and compliance risks associated with manual verification, freeing up your internal teams to focus on growth-oriented activities.
For the Agent: A fast, seamless, and professional onboarding experience is a powerful recruiting tool. Agents are eager to start selling, and a carrier that enables them to generate commissions faster will naturally attract more high-performing talent. This creates a positive first impression and builds a foundation of loyalty from day one.
Commercial P&C underwriters are highly trained knowledge workers, with median salaries often exceeding $80,000 and top earners making well over $120,000 annually. They are paid to be analytical, strategic thinkers who can accurately assess complex risks. Yet, a significant portion of their expensive time is spent on work that requires none of that expertise. A McKinsey study found that underwriters spend up to 40 percent of their time on purely administrative tasks.
This is the central problem creating the submission backlogs that plague the industry. Critical, revenue-generating work gets stuck behind a wall of manual document checks, data entry, and endless email chains chasing missing information. Carriers are paying expert salaries for administrative work, and it’s a losing proposition.
The initial wave of automation, RPA (Robotic Process Automation), was tried in the insurance space and largely failed to solve this problem. RPA relies on brittle bots designed to mimic human clicks in a stable environment. But insurance submissions are the opposite of stable.
An RPA bot can’t adapt when a broker sends a supplemental questionnaire in a new PDF format. It can’t read and comprehend the unstructured text in a multi-page loss history report. It breaks when confronted with the dozens of variations in ACORD forms, endorsements, and invoices that make up a single submission. This inability to handle variability and unstructured data meant RPA could only ever automate the most trivial, repetitive tasks, leaving the core underwriting workload untouched.
The business of insurance is conducted in human language, contained within contracts, reports, and emails. A truly transformative automation solution must be fluent in that language. Kognitos is built on this principle, using agentic AI to automate processes in plain English.
Instead of programming a rigid bot, you instruct Kognitos on your underwriting process as you would a person. The system can:
This is a system that works with the complexity of insurance, not against it.
Many carriers have turned to specialized insuretech point solutions to solve the underwriting intake problem. While these tools can offer temporary relief, they contribute to a larger strategic issue: a bloated and fragmented tech stack and increased technical debt. Major analyst firms like Boston Consulting group also show evidence that the insurance industry as a whole has been shown to be an early leader in overall AI adoption and solution piloting. This raises concerns not only for additional sprawl of point solutions but increased risk for so-called “shadow AI.” Insurance CIOs are now actively trying to consolidate vendors and create unified platforms. A tool that only handles underwriting submissions becomes another silo to manage, integrate, and maintain.
Kognitos provides a single, generative AI platform that can automate underwriting submissions and extend to other critical business processes like claims processing, premium auditing, and financial operations. This approach delivers immediate value for underwriting while providing a scalable solution for enterprise-wide automation, reducing total cost of ownership and simplifying the IT landscape. Even major carriers like Chubb emphasize that digital transformation is key to improving experiences for brokers and clients, a goal best served by a cohesive platform rather than a patchwork of tools.
By eliminating the administrative backlog, agentic automation creates a powerful ripple effect of benefits for both the underwriter and the carrier.
For the Underwriter: The daily grind of chasing paper is replaced by high-value, satisfying work. Underwriters are freed to focus on deep risk analysis, building stronger broker relationships, and mentoring junior team members. This leads directly to higher job satisfaction, skill development, and a significant reduction in employee burnout and churn.
For the Carrier: The entire business operates at a higher level.
Ultimately, automating the submission process allows carriers to unlock the full potential of their most valuable asset: their people.
Your most valuable patient support team members were hired for their problem-solving skills and empathy in working with your patients. They are the human voice of your health system, capable of navigating complex patient concerns with care and expertise. So why do they spend most of their day acting as human ticket routers?
The constant flow of digital requests from patient portals like MyChart, for password resets, appointment scheduling, or prescription refills, has turned skilled professionals into digital traffic cops. They triage, categorize, and forward tickets, a repetitive process that burns out top talent and slows down patient care.
For a mid-sized health system, the volume is staggering. One Kognitos customer fielded approximately 8,500 digital support tickets per month through a single email address. With no intelligent triage in place, they faced lengthy patient wait times and a nearly 3% ticket abandonment rate, meaning hundreds of patients each month simply gave up on their requests.
That 3% represents a direct failure in the patient experience. It’s a patient who couldn’t access their lab results, a guardian who couldn’t schedule a child’s appointment, or an individual who couldn’t refill a critical prescription. When your digital front door is overwhelmed, patient trust erodes rapidly.
The challenge of intelligent triage isn’t new, but previous attempts at a solution have consistently fallen short. Both legacy automation tools and the native features within existing healthcare platforms lack the cognitive ability to handle the complexity of human conversation.
Instead of forcing humans to work like machines, the solution is to empower them with machines that think more like humans. Kognitos is an enterprise automation platform that uses the latest capabilities of generative AI to understand and act on business processes described in plain English.
For healthcare support, the process is transformed:
This is much more than a drag-and-drop low-code workflow tool or a simple chatbot. We’re talking about AI that has the ability to reason, taking on the cognitive load of triage so your people can focus on care. Organizations can reduce the ticket abandonment rate to less than 1% by providing instant, accurate responses and freeing human personnel to handle the exceptions.
By automating the triage, you aren’t removing the human element from patient support. You are restoring it. When your best people are freed from the monotonous task of routing, they can invest their time in resolving complex patient issues, providing empathetic guidance, and improving the very processes that create friction in the patient journey.
This is a strategic investment in both short-term efficiency and long-term patient loyalty. You eliminate a major source of employee burnout while building a more responsive, resilient, and human-centric support system. The choice is simple: continue burning out your best people with low-value work, or empower them with AI that handles the noise so they can deliver the care your patients deserve.
For sales and finance leaders, the promise of AI for Sales Forecasting has been monumental. The vision of a system that can accurately predict quarterly revenue and identify at-risk deals is a top priority for any B2B business. Yet, for all the investment in advanced analytics and machine learning models, most organizations are still plagued by a fundamental and frustrating problem: the forecasts are wrong.
The issue isn’t the algorithm; it’s the data. Every AI-powered forecasting tool suffers from the same vulnerability: “garbage in, garbage out.” A forecast is only as reliable as the underlying CRM data, which is often stale, incomplete, or manually entered incorrectly by busy sales reps.
This article is a guide for leaders on how to solve the root cause of inaccurate forecasting. The true value of AI is not just in analyzing the data, but in automating the very processes that create and act upon that data. It’s time to move beyond passive prediction and build a closed-loop, self-improving forecasting ecosystem that doesn’t just guess the future, but actively works to make it better.
The Achilles’ heel of any AI for Sales Forecasting initiative is data quality. Your CRM is the source of truth, but that truth is often murky. Sales representatives are hired to build relationships and close deals, not to be meticulous data entry clerks. As a result, critical information often lives in their email inboxes, meeting notes, and call summaries, but not in the CRM fields that your AI model relies on.
This leads to:
When your forecasting model is fed this unreliable data, it produces an unreliable prediction. The problem isn’t the AI’s ability to analyze; it’s the lack of a clean, consistent data source.
A truly intelligent approach to AI for Sales Forecasting requires a shift in thinking. Instead of focusing only on the prediction algorithm, leaders need to build a complete ecosystem that automates both the input and the output of the forecast.
This requires a new class of natural language process automation that acts as the missing operational engine. This engine is an intelligent AI agent that:
The first step to a reliable forecast is to solve the “garbage in” problem without burdening your sales team. A natural language automation platform like Kognitos can act as an intelligent assistant that works in the background.
For example, an AI agent can read a sales rep’s email exchange with a prospect. By understanding the natural language, it can identify key context, such as “The customer has requested a formal quote for 500 units by Friday.” The agent can then autonomously:
This is a practical application of generative AI for sales. The CRM is updated in real-time with accurate information, directly from the source of the activity, ensuring the data fed into your AI for Sales Forecasting model is as clean and reliable as possible.
A forecast that simply identifies a problem without initiating a solution is only doing half the job. The second part of a closed-loop system is to automate the actions that should follow from the forecast’s insights.
For example, your AI for Sales Forecasting model might downgrade a high-value deal’s probability of closing from 80% to 40% because it has detected a lack of recent communication. In a traditional setup, this would require a manager to notice the change and manually follow up.
In a closed-loop system, this prediction triggers an AI agent to:
This turns your forecast from a passive, historical report into a proactive, forward-looking action engine. It’s the key to not just predicting the future, but actively influencing it.
The future of AI for Sales Forecasting is not a better crystal ball; it is an intelligent, autonomous revenue engine. By solving the data integrity problem at the source and automatically translating predictive insights into decisive action, leaders can finally move beyond a culture of reactive course-correction. This closed-loop approach creates a system that is not only more accurate in its predictions but is actively working to improve outcomes, transforming the sales forecast from a static report into the dynamic, reliable pulse of your business.
In the modern enterprise, customer service sentiment analysis has become the essential smoke detector. It’s an incredibly effective tool for hearing the first signs of trouble, alerting you the moment a customer interaction begins to smolder with frustration. Your dashboards track the customer sentiment score like a sensitive alarm system, giving you unprecedented insight into the health of your customer relationships. But here lies the critical question: what happens after the alarm sounds?
For most organizations, the answer is a slow, manual bucket brigade. An alert creates a ticket. The ticket lands in a queue. An agent eventually picks it up and begins the time-consuming work of finding the fire. This delay between insight and action is where customer loyalty is lost.
This article presents a new playbook for leaders who understand that an alarm system is useless without an automated response. We will explore how to move beyond passively measuring sentiment and build an automated sprinkler system for your customer service operations. It’s time to connect the trigger to the action and transform your customer service sentiment analysis from a reactive warning into a proactive resolution engine.
The core flaw in most approaches to customer service sentiment analysis is not the technology itself, but the operational model it feeds into. When a negative sentiment is flagged, it kicks off a sequence of manual, inefficient steps that drain resources and frustrate customers.
An agent must manually:
This manual investigation is the bottleneck. It’s a slow, costly process that undermines the very purpose of real-time sentiment analysis. Your technology is moving at the speed of light, but your operations are moving at the speed of human clicks.
The solution is to build an intelligent, automated first responder. This requires a new class of natural language process automation that acts as the engine connecting the sentiment alarm directly to the resolution workflow. This is about creating a system that doesn’t just report on the problem but actively works to solve it.
This intelligent agent is designed to:
Imagine a customer emails your company, clearly frustrated. Their high-value shipment is late, and the tracking information is unclear.
The fire is out before the customer even knows the full extent of the problem. This is the power of moving beyond just measuring sentiment to automating the response.
The choice for leaders is clear. You can continue to invest in better fire alarms while relying on a manual bucket brigade to handle the fallout, or you can build a truly responsive enterprise. The future of customer service is not about simply measuring sentiment faster; it’s about closing the loop from insight to action in minutes. By connecting your customer service sentiment analysis to an intelligent automation engine, you’re not just putting out fires—you’re building a system that turns moments of potential crisis into powerful demonstrations of your commitment to your customers.
For decades, the core of inventory management has been the barcode scan—a simple, digital confirmation that an item is here, or it is there. While essential, this approach is a 20th-century solution struggling to keep pace with the complexities of 21st-century supply chains. The real challenge today isn’t just tracking what’s in stock; it’s managing the relentless stream of exceptions—supplier disruptions, shipping discrepancies, damaged goods, and unexpected costs—that break rigid, traditional automations and cause chaos in the warehouse and on the balance sheet.
The promise of a modern inventory management AI is not just a more predictive dashboard or a fancier report. The true transformation lies in moving beyond static data analytics to build truly self-learning business processes. It requires transforming your inventory system from a passive database into an intelligent, operational engine.
This guide is for leaders who want to build a resilient and autonomous supply chain. We will explore a new paradigm that goes beyond simple tracking. The goal is to demonstrate how a new class of inventory management AI can automate end-to-end workflows, intelligently handle exceptions by collaborating with human experts, and learn from their guidance to automatically refine and improve processes over time. This is about creating an ecosystem that doesn’t just report on what happened, but actively learns and adapts to what’s happening now.
Most companies still rely on a patchwork of systems that, while digital, are fundamentally static. Traditional retail inventory management software and enterprise ERP modules are excellent systems of record. They can tell you the expected quantity of an item in a specific location. However, they are not systems of action. They cannot reason, adapt, or independently resolve the messy, real-world problems that occur between the purchase order and the final stocking.
To bridge this gap, many have turned to Robotic Process Automation (RPA). But these rule-based bots are notoriously brittle. They are programmed to follow a script, and when a supplier updates their web portal or a shipping partner introduces a new type of document, the bot breaks. This leaves teams scrambling to manually fix the process, negating the very efficiency the automation was meant to create. This is a common failure point for any legacy retail inventory tracking system.
The cost of these exceptions is enormous. It’s measured in wasted hours of manual reconciliation, delayed shipments, inaccurate financial forecasts, and the risks of stockouts or overstocking. A truly effective inventory management AI must be designed to handle this chaos, not fail because of it.
The term “self-learning” in the context of inventory management AI is often conflated with predictive forecasting. While predicting demand is valuable, it is only one piece of the puzzle. A truly self-learning system is an operational one. It is a system that learns the process of inventory management, not just the data.
This new paradigm of inventory management AI is defined by four key capabilities:
This intelligent, adaptive capability is powered by a new class of inventory management AI built on two core technological pillars:
This combination creates a collaborative environment where the technology handles the repetitive work, and the human experts handle the novel exceptions, all while continuously making the system smarter. This is the foundation of any effective retail inventory tracking system.
Let’s consider a practical example for a multi-store retailer. Their retail inventory management software is proficient at tracking stock, but the process of receiving goods is fraught with exceptions. Here’s how a self-learning inventory management AI transforms that process.
This example of AI inventory optimization showcases a system that is not just automated but is also resilient and intelligent. This is the standard for any modern inventory management software for retailers.
For CIOs and finance leaders, the shift to a self-learning inventory management AI delivers profound strategic benefits that go far beyond the warehouse floor.
The era of managing inventory through static reports and brittle bots is over. The future of the supply chain will not be defined by how well a business can track what it has, but by how intelligently its processes can adapt to the unpredictable reality of what happens next. The move from a passive system of record to a dynamic, self-learning operational engine is the single most important transformation leaders can make. By embracing an inventory management AI that learns from your experts and automates the chaos of exceptions, you are not just optimizing a function; you are building a truly autonomous and resilient supply chain ready to face any disruption.
For CIOs and IT leaders, the rise of AIOps automation has been a game-changer. The ability to use artificial intelligence to create self-healing infrastructure—systems that can automatically detect, diagnose, and remediate technical issues—has moved from a distant vision to a practical reality. We’ve become incredibly adept at applying AI ops monitoring to the health of our servers, networks, and applications. But this is only half of the equation for a truly autonomous enterprise.
A perfectly stable server running a broken, manual business process still results in a broken business. The value of a resilient infrastructure stops at the data center door if the critical workflows running on it—procure-to-pay, financial reporting, compliance checks—are still brittle, slow, and dependent on human intervention.
This article is a definitive guide for leaders on how to expand the powerful principles of AIOps automation beyond the traditional boundaries of IT. We will demonstrate how the AIOps philosophy of detect, diagnose, and remediate can be translated from the world of server logs to the world of invoices and compliance reports. It’s time to build a new blueprint for the autonomous enterprise—one that combines resilient infrastructure with equally resilient operations.
To understand the gap in most enterprise automation strategies, it helps to think of your organization as two distinct, but connected, factories.
The problem is that traditional AIOps automation has no visibility into the business factory. It can tell you if a server is down, but it can’t tell you if an invoice approval is stuck, a compliance check has failed, or a financial reconciliation is inaccurate. The current state of AI ops monitoring is focused entirely on the health of the machines, not the health of the work itself. This is the missing link in true IT operations automation.
A truly resilient enterprise requires a new approach: applying the core principles of AIOps automation to the business factory. This Business AIOps doesn’t replace traditional AIOps; it complements it, creating a complete, end-to-end intelligent system.
Here’s how the philosophy translates:
This advanced form of AIOps automation for business is powered by a new class of technology: natural language process automation. Instead of relying on complex code or brittle bots, business experts can build and manage their own automations simply by describing the process in plain English.
This approach makes the automation transparent, auditable, and instantly adaptable. When a regulation or business policy changes, a compliance or finance professional can update the workflow in minutes, without a lengthy IT project. This is the key to creating the agile, self-healing processes that a Business AIOps framework requires.
Here is a clear AI ops example in a business context: An accounts payable process is automated in English. When an invoice arrives from a new vendor with an unexpected “sustainability fee,” the system, instead of failing, initiates a remediation workflow. It understands it has encountered a new variable, pauses the process, and asks the AP manager for guidance. The manager instructs it to map the new fee to a specific GL code. The system learns this new rule and applies it to all future invoices from that vendor, effectively healing the process. This is the power of true AIOps automation for the enterprise.
The journey towards the autonomous enterprise cannot be completed by focusing on infrastructure alone. A self-healing IT factory is a monumental achievement, but its value is only fully realized when the business factory operating within it is equally intelligent and resilient. The principles of AIOps automation are too powerful to be confined to the data center. By adopting a dual strategy—combining traditional AI ops monitoring for systems with a natural language-based platform for business processes—leaders can finally bridge the gap between operational stability and true business agility, creating an enterprise that doesn’t just run without interruption, but thrives with intelligence.
Search for a list of the top uses of AI in banking, and you will find a predictable collection of ideas. For years, these lists have been dominated by customer-facing chatbots and high-level descriptions of fraud detection algorithms. While important, these applications barely scratch the surface of what’s possible. They overlook the massive, complex operational engine where the real work of a financial institution gets done—and where the most significant transformation is waiting to happen.
The true impact of artificial intelligence in banking is not found at the customer-facing edge; it’s realized in the core processes that form the central nervous system of the bank. This article redefines the Top 10 by focusing on the critical, process-oriented use cases that deliver true, enterprise-wide value.
We will explore how a new class of agentic automation is moving beyond simple tasks to power end-to-end workflows in the most challenging areas of banking. This is a new blueprint for leaders—one that shifts the focus from surface-level applications to building an intelligent, autonomous operational core. This is the definitive guide to the most impactful use of AI in banking today.
The reason these deeper use cases are now possible is due to a fundamental shift in automation technology. First-generation tools like RPA were designed to automate simple, repetitive tasks—mimicking clicks and keystrokes. They were brittle and incapable of handling the complex, exception-driven nature of core banking operations.
The new paradigm is agentic automation, where an intelligent AI agent can understand instructions in natural language, reason through multi-step processes, and autonomously orchestrate workflows across multiple systems. This is the key that unlocks the true potential of banking and AI. It’s the difference between a bot that can copy-paste data and an AI agent that can manage an entire loan origination process.
Here are the top 10 use cases where agentic AI is making the most significant impact, transforming the operational fabric of modern financial institutions. Each banking automation use case represents a move from manual drudgery to intelligent automation.
These ten use cases share a common thread: they are not isolated point solutions. They are the building blocks of an intelligent, autonomous operational backbone for the entire institution. The true power of banking and AI is realized when these intelligent agents work together, sharing information and orchestrating processes across departmental silos.
The future of AI in the banking sector is not about having a collection of disparate tools, but about building a unified, intelligent core that drives efficiency, ensures compliance, and frees human talent to focus on what matters most. The era of AI in banking is finally moving beyond the hype and into the operational heart of the enterprise.
The race to implement AI in real production use cases is on, but most companies are being sold a lie. You’re told that you need to choose between the high priesthood of traditional code or the polished simplicity of low-code, no-code interfaces. The problem is that both of these paths lead to the exact same destination: a black box.
It’s an automation system where the core logic is completely unreadable to the people who own the outcomes. This forces critical questions every leader should be asking. “Why would I trust a system I can’t understand? What is being hidden from me behind the curtain of vibe coding or complex diagrams?”
The truth is, any system that doesn’t speak your language, plain English (or natural language writ large), is asking for your blind faith in its programming. In the era of AI and just like your mentor told you, hope is not a strategy.
The only way to automate at scale, safely and effectively, is to build on a language that every stakeholder in your business already speaks.
Low-code platforms emerged with a powerful promise to democratize automation. They replaced code with visual blocks, making it seem like anyone could build a robust process. But for any real business process, this simplicity is a mirage.
As soon as you introduce conditional logic and exception handling, the neat flowchart devolves into “visual spaghetti,” a tangled mess just as opaque as the code it was meant to simplify. You’ve simply traded one black box for another, more colorful one.
The majority of enterprise-scale low-code applications eventually require significant refactoring by professional developers, negating the initial speed advantage. They hit a wall. Breaking through it means reverting to the old, broken model.
For decades, the biggest obstacle to effective automation has been the communication gap between business experts and technical teams. The business describes a need, it gets translated into a spec document, which is then translated again into code. Every translation is a point of failure, a “game of telephone” where critical intent is lost by what comes out of the other end.
Low-code was supposed to solve this, but it just introduced a new, clunkier translator. Now the business expert explains the process to a low-code developer who manipulates the interface. The core problem remains. A six-to-eight-figure consulting engagement just papers over the same cracks.
English-as-code eliminates the translation layer entirely.
When the business process described in plain English is the code, there is nothing to translate. The expert’s intent is captured directly as executable logic. This is how you achieve true alignment.
As AI takes on critical tasks like financial reporting and customer communications, the demand for transparency has become a C-suite mandate and is likely to become a legal one. You wouldn’t let an accountant manage your books in a language you couldn’t read. So why would you let an AI run your business operations that way?
This is all about governance. A 2024 McKinsey research report confirmed this, showing that 91% of respondents do not believe their organization is set up to scale AI use safely and responsibly. Leaders intuitively know that if you can’t read it, you can’t govern it, and you certainly can’t trust it. The same McKinsey report writes, “To capture the full potential value of AI, organizations need to build trust…Trust in AI comes via understanding the outputs of AI-powered software and how—at least at a high level—they are created.” It can’t be written in computer languages like Python.
The danger is real. A 2025 Anagram Security survey found that now approximately 78% of employees use AI tools, often without clear company policies or governance. Furthermore, 58% revealed that they’ve provided LLMs with sensitive data.. This “Shadow AI” creates massive holes in security and compliance. The only way to fight it is with a platform that brings automation into the light. A platform that is inherently transparent because its logic is written in plain English with governance tooling provided to IT.
The choice you make for your automation platform defines your company’s future. You can choose a black box and accept the risk, the bottlenecks, and the constant need for translators. Or you can choose a new path.
Demand that your automation speaks your language. English-as-code is not a feature. It is the foundation for a resilient, governable, and truly intelligent enterprise. It’s time to stop building systems that hide their logic and start building systems that declare it, clearly and openly, for all to see.
Ready to build with a language you can trust? Learn more about the Kognitos natural language platform.
