The conversation about AI in ERP is at a critical juncture. For years, leaders have been told that the future lies in a “smarter ERP,” with promises of built-in machine learning and predictive analytics. While these vendor-supplied features offer incremental value, they fundamentally miss the real challenge: your ERP, as powerful as it is, is a rigid system of record. But real, complex business processes don’t happen neatly within the confines of an ERP module. They happen in the chaotic, unstructured “white space” between your ERP, emails, spreadsheets, and a dozen other applications.
This is the fundamental disconnect that holds back true transformation. The value of artificial intelligence in ERP is not found by adding a few more features inside the box; it’s found in the intelligent orchestration layer that wraps around it. This article is a guide for business and IT leaders on how to bridge that gap. It’s time to move beyond brittle, custom-coded integrations and the limitations of embedded ERP AI.
We will explore a new approach that uses natural language to empower your finance, supply chain, and HR teams to automate their own end-to-end workflows. This is about transforming your ERP from a passive database into the dynamic, automated core of your enterprise. It’s about building a secure, auditable, and agile “system of action” on top of your system of record, finally delivering the intelligence and responsiveness that your business demands from any modern AI in ERP solution.
Enterprise Resource Planning (ERP) systems are the undisputed backbone of the modern enterprise. They are the central source of truth for financial, supply chain, and human resources data. This role as a “system of record” is their greatest strength. However, it is also the source of their greatest weakness. By design, ERP systems are built for stability and integrity, which makes them inherently rigid.
Customizing an ERP workflow is a slow, expensive process that requires highly specialized developers and long project cycles. This creates a significant lag between the evolving needs of the business and the capabilities of its core technology. Early attempts to automate ERP systems with technologies like Robotic Process Automation (RPA) provided a temporary workaround. These bots could mimic human data entry, but they were incredibly brittle. A minor change to the ERP’s user interface could break an entire automation, creating a constant cycle of maintenance and failure.
This brittleness highlights a core problem: these first-generation tools could not truly reason or handle exceptions. They were not a true application of artificial intelligence in ERP systems. They were simply a fragile layer of mimicry on top of a rigid core. To achieve a real breakthrough in AI in ERP, a more intelligent and flexible approach is required.
To understand the limitations of a traditional AI in ERP strategy, one only needs to trace a single, critical business process from start to finish. Consider the procure-to-pay cycle. It may be recorded in your ERP, but the process itself is a sprawling, multi-system affair.
The ERP is involved, but it’s only one stop on a long journey. The real work- the communication, the exception handling, the unstructured data processing- happens in the “white space” between applications. This is where processes break down and where the most significant opportunities for ERP AI can be found. Any AI ERP system that cannot operate in this messy, cross-application environment will fail to deliver transformative value. The future of ERP system automation is not about a better ERP; it’s about conquering this white space.
The solution is not to replace the ERP, but to augment it with an intelligent orchestration layer that can manage these end-to-end processes. This new approach to AI in ERP is built on natural language process automation, a paradigm that empowers your business experts to become the architects of their own automated workflows.
Instead of relying on IT to write complex code or build fragile bots, your finance and supply chain teams can automate their own processes simply by describing them in English. This is the core of an agile and responsive AI ERP system. A senior accountant can define the rules for handling invoice discrepancies in plain language, and the system understands and executes that logic. This fundamentally changes the dynamic of business process management.
This is made possible by a neurosymbolic AI architecture that combines the power of large language models with a symbolic reasoning engine. This is a crucial differentiator for any serious ERP with AI. The reasoning engine ensures that business rules are followed with logical precision, eliminating the risk of AI hallucinations. When the system encounters an exception it hasn’t seen before, it can loop in a human expert for guidance, learn from their decision, and apply that new knowledge to future situations. This allows you to safely automate ERP systems that are central to your financial and operational health. This is the new standard for artificial intelligence in ERP.
When you wrap your ERP in this intelligent orchestration layer, you transform it from a passive system of record into a dynamic system of action. The benefits of AI in ERP become tangible and profound. Let’s look at some examples of artificial intelligence in ERP systems.
An intelligent automation can monitor a sales inbox, read an incoming purchase order from a customer’s email (regardless of its format), extract the relevant information, and validate it against inventory levels and customer data in your ERP. It can then generate a sales order in the ERP, create an invoice, and send it to the customer, all without human intervention unless a specific exception is flagged. This application of ERP AI dramatically accelerates cash flow.
The financial close process is a perfect example of a workflow that spans the ERP and countless spreadsheets. An intelligent orchestration layer can automate ERP systems’ most tedious tasks by pulling data from various sub-ledgers and external systems, performing reconciliations, identifying anomalies, and preparing journal entries. This allows the finance team to shift its focus from manual data wrangling to strategic analysis. This is a high-value use case for AI in ERP.
When a supply chain disruption occurs, speed is critical. A natural language-based automation can monitor for alerts, automatically query inventory and supplier data in the ERP, identify alternative suppliers, and even draft communications to stakeholders. This turns your ERP with AI into a proactive, resilient nerve center for your supply chain.
Looking ahead, the future of ERP system automation will see a clear separation of duties. The ERP will perfect its role as the secure, stable core for transactional data—the ultimate system of record. Meanwhile, the intelligent orchestration layer will handle all the dynamic, cross-application, and exception-driven work—the system of action.
This model provides the best of both worlds: the stability of a traditional ERP combined with the agility and intelligence of a modern AI platform. This is the pragmatic and powerful path forward for AI in ERP. Companies that embrace this two-layer approach will be able to adapt to changing market conditions faster, operate more efficiently, and unlock new levels of innovation. This is the true destination for any AI in ERP journey.
The narrative surrounding AI in pharma is captivating. We hear stories of algorithms discovering novel drug candidates in weeks and genomic analysis platforms identifying therapeutic targets at a speed once unimaginable. This focus on the lab is both exciting and justified, but it is dangerously incomplete. It overlooks the immense operational and regulatory friction that exists outside the R&D environment—the place where brilliant scientific discoveries can stall for years in a quagmire of manual processes, overwhelming documentation, and complex compliance mandates.
This is where the real, immediate transformation for AI in pharma is waiting. The most significant gains in speed-to-market and operational efficiency are not found in replacing scientists, but in empowering the clinical, regulatory, and pharmacovigilance teams who navigate the labyrinthine path from trial to patient. This guide is for pharmaceutical and biotech leaders who are ready to look beyond the lab and re-engineer the core operational engine of their business.
It’s time to move past the failures of brittle automation and the unacceptable risks of generic AI. A new paradigm, built on natural language process automation, allows your most valuable experts to automate their most complex workflows simply by describing them in English. This isn’t about the science of finding the next molecule; it’s about building the secure, compliant, and intelligent engine that brings it to the world.
Beneath the surface of every clinical breakthrough is a mountain of operational work. This work-spanning clinical trials, regulatory affairs, and post market surveillance- is the circulatory system of any pharmaceutical company. For decades, it has been managed through a combination of manual effort and first-generation automation technologies that are ill-suited for the dynamic, high-stakes environment of pharma. This is the central challenge that the strategic use of AI in the pharmaceutical industry must solve.
Traditional Robotic Process Automation (RPA) was an early attempt at a solution. However, these bots are fundamentally rigid. They are programmed to follow a script of clicks and keystrokes, making them incredibly fragile. A minor update to a clinical trial management system (CTMS) or a regulatory portal can break an entire workflow, creating a constant maintenance burden on IT. This is not a sustainable model for artificial intelligence in pharma.
More recently, generic AI platforms have emerged, but they introduce a critical risk: hallucination. In an industry where precision and accuracy are paramount, an AI that can fabricate data or misinterpret a safety report is a non-starter. This makes the adoption of generic AI in pharmaceuticals for core processes a significant compliance risk. These limitations have made it clear that a new approach is needed—one that combines intelligence with absolute governance.
The next evolution of AI in pharma is not about writing more robust code; it’s about eliminating the need for traditional programming altogether. The future is natural language process automation, where the deep process knowledge of your clinical, regulatory, and safety experts is captured and executed in plain English.
Imagine a clinical operations manager automating the complex process of site payments by simply describing the validation rules and approval workflows. Picture a regulatory affairs specialist creating an automated process to assemble a submission dossier by listing the required documents and data sources. This is the core principle behind a new class of enterprise AI.
This isn’t just a user-friendly interface layered on top of old technology. It is a fundamentally different approach, powered by a neurosymbolic AI architecture that combines the contextual understanding of large language models with a symbolic reasoning engine. This is a critical distinction for artificial intelligence in pharma. The reasoning engine ensures that processes are followed with logical precision, by design, eliminating the risk of AI hallucinations. This creates a system that is not only intelligent but also auditable, verifiable, and secure—the table stakes for any serious application of AI in pharma.
A true strategy for AI in pharma must focus on the high-friction, document-intensive processes that create the most significant bottlenecks. By applying natural language automation to these core functions, organizations can unlock immediate and substantial gains in efficiency and compliance.
The administrative burden of clinical trials is immense. A natural language automation platform can transform this landscape. For example, it can automate the validation of data from various clinical trial sites, cross-referencing information in unstructured documents like patient records and lab reports against the trial protocol. This intelligent document processing goes far beyond simple OCR, understanding context and flagging discrepancies for human review. This is a prime example of the practical use of AI in the pharmaceutical industry.
The path to regulatory approval is paved with documentation. Assembling a dossier for submission to the FDA or EMA is a monumental task. The use of artificial intelligence in pharma here is transformative. An automation described in English can orchestrate the entire process, pulling trial data from the CTMS, sourcing manufacturing records from the ERP, and compiling safety reports, ensuring everything is correctly formatted and cross-referenced.
Crucially, because the automation is written in English, it serves as its own audit trail. Regulators and internal compliance teams can read the process in plain language and understand its logic, creating a level of transparency that is impossible with traditional code. This is one of the most powerful benefits of AI pharma.
Post-market safety monitoring requires sifting through vast amounts of data from various sources to identify adverse events. This is another area where AI in pharmaceuticals can have a profound impact. An intelligent system can monitor intake channels—from emails to call center logs—and automatically identify, classify, and route potential adverse event reports to the correct safety physician for review. This accelerates response times and ensures a complete, auditable record of every reported event, a cornerstone of effective AI in pharma.
Adopting this new model of AI in pharma delivers strategic advantages that resonate directly with the priorities of C-suite leaders and CIOs. The benefits of AI pharma extend far beyond simple cost savings.
This operational excellence even extends to commercial functions. For example, the principles of AI in pharma marketing can be applied to automate the rigorous review and approval process for promotional materials, ensuring every claim is cross-referenced against approved clinical data and regulatory guidelines. The application of artificial intelligence in pharma is truly enterprise-wide.
The future of AI in pharma will be defined by two distinct but connected tracks of innovation: the scientific and the operational. While the search for the next breakthrough molecule continues in the lab, the race to build a more efficient, compliant, and intelligent business engine is happening now. Leaders who pivot their focus to re-engineering their core operational backbone with a new generation of secure, auditable, and business-friendly artificial intelligence in pharma will not only accelerate their current pipeline but also build a lasting competitive advantage for the decade to come.
For years, business leaders have been told a consistent story about technology adoption: change is hard, and you must manage your employees through the inevitable friction. When it comes to artificial intelligence, this narrative is amplified. The conventional wisdom is that AI resistance is a natural, almost unavoidable human problem—a standard case of resistance to change in the workplace that requires a heavy dose of top-down change management. But what if this diagnosis is fundamentally wrong?
What if the widespread employee resistance to AI isn’t an irrational fear of the future? What if it’s a perfectly rational response to the tools being implemented? Employees are not resisting efficiency or innovation. They are resisting complex, opaque, “black box” technologies that are done to them, not built for them. The friction isn’t with the idea of AI; it’s with the experience of using it.
This article offers a new playbook for leaders. It’s a guide to dissolving AI resistance by design, simply by choosing a different class of AI. The most effective strategy isn’t a communications plan to change your people’s minds; it’s the adoption of a platform that was built to empower them from the very start. The problem of AI resistance is not a people problem; it’s a technology problem.
When a new AI initiative is met with skepticism or pushback, the typical response is to roll out a classic change management campaign. Leaders hold town halls, send newsletters, and emphasize the benefits of the new technology, all in an effort to overcome what they perceive as an emotional barrier to progress. This approach to overcoming employee resistance often fails because it treats the symptom—the resistance—without ever addressing the underlying cause.
The employee resistance to AI that most organizations face is not an emotional reaction; it’s a logical one, rooted in three legitimate concerns created by first-generation AI and automation tools:
No amount of change management can fix a tool that is fundamentally not built for the person who is supposed to use it. This is the core reason why so many AI initiatives stall, failing to move beyond the pilot stage. The AI resistance is a signal that the technology itself is the problem.
The most effective strategy for overcoming employee resistance is not to force a better adoption process, but to choose a better, more human-centric technology from the outset. The antidote to the problems of inaccessibility, opacity, and adversarial positioning is a new class of AI platform built on a foundation of natural language.
When you allow business users to build and manage automations simply by describing them in plain English, you fundamentally alter the dynamic of AI adoption. The fear of the unknown dissipates because the tool operates in the language of the user. The distrust from “black box” systems is replaced by the clarity of human-readable processes. The threat of replacement evolves into a partnership.
This approach effectively dissolves the root causes of AI resistance before they can even take hold. It proves that the challenge of employee resistance to AI is not an inevitability to be managed, but a design flaw to be avoided. This is a crucial insight for leaders planning any automation initiative. Addressing staff resistance to automation is a function of choosing the right tool.
To bypass the entire cycle of AI resistance, leaders should evaluate potential platforms against three core pillars. These pillars are the foundation of a system that fosters advocacy, not animosity, and ensures that your investment in AI empowers your team rather than alienating them. This is the modern playbook for tackling employees’ resistance to automation.
The most significant barrier to AI adoption is complexity. By choosing a platform that uses English as its code, you eliminate this barrier. Your finance, HR, and operations experts no longer need to become quasi-developers. They can leverage their deep subject matter expertise to build powerful automations simply by describing the steps. This accessibility is the first and most critical step in preventing AI resistance. It turns a potentially intimidating technology into a familiar and manageable tool. This is the key to overcoming employee resistance before it starts.
You cannot have adoption without trust. A platform that allows users to see, understand, and verify the logic of an automation is inherently trustworthy. When a process is written in plain English, it becomes its own documentation. Anyone on the team can read it and understand exactly what it does and why. This is a radical departure from the opaque nature of traditional automation. This transparency is further enhanced by a neurosymbolic AI architecture that is designed to eliminate AI hallucinations, ensuring that the system operates with precision and reliability. This clarity is a powerful antidote to employee resistance to AI.
The final pillar is to reframe the relationship between the human and the AI as a partnership. This is achieved through a human-in-the-loop design. When the AI encounters an exception or a scenario it has not seen before, it doesn’t just fail. It proactively engages the correct human expert, explains the problem, and asks for guidance. This collaborative model, a core feature of platforms like Kognitos, does two powerful things: it reinforces the value of human expertise and it creates a system that learns and improves over time. This reframes the AI as a co-worker, not a replacement, which is essential for overcoming the deep-seated employee resistance to AI.
The conversation about AI resistance needs a fundamental reset. It is not a challenge to be overcome with persuasion, but a problem to be solved with a better technology choice. The persistent resistance to change in the workplace that so many leaders face when implementing new technologies is often a direct result of the tools themselves.
By choosing an AI platform that is accessible, transparent, and collaborative by design, you are not just buying a better piece of software; you are investing in a more successful and frictionless adoption journey. You are creating an environment where your team members become the champions of automation, not its biggest detractors. The path from AI resistance to advocacy is not about changing your people’s minds; it’s about choosing a technology that was built to empower them from the very beginning.
The conversation around AI in retail banking has, for the most part, been focused on the front lines. We’ve seen the rise of AI-powered chatbots, personalized mobile banking apps, and robo-advisors. While these customer-facing innovations are valuable, they represent only the surface of a much deeper transformation. The true, game-changing potential of artificial intelligence in retail banking lies not in the customer interface, but in the operational core of the institution itself.
For too long, the back office of retail banking has been a complex web of manual processes, legacy systems, and brittle automation. This operational friction is what dictates a bank’s efficiency, its ability to remain compliant, and, ultimately, its capacity to deliver on the promises made by its front-end technology. This article serves as a guide for banking leaders ready to shift the focus. It’s time to move beyond the limitations of traditional automation and the risks of generic AI to build an operational foundation that is intelligent, transparent, and resilient. The future of AI in retail banking will be defined by how well institutions re-engineer their core engine, not just by how they polish the hood ornament.
This transformation requires a new approach, one built on natural language that empowers banking and compliance professionals—not just programmers—to automate the critical workflows that drive the business. It’s about creating a secure and auditable operational backbone that turns the promise of AI in retail banking into a practical, powerful reality.
Beneath the sleek mobile apps and modern branch designs, many banks operate on a foundation of decades-old processes. Key functions like loan origination, Know Your Customer (KYC) compliance, and dispute resolution are often manually intensive, slow, and prone to error. The first wave of retail banking automation attempted to address this with Robotic Process Automation (RPA).
RPA was a step in the right direction, but it was a tactical fix, not a strategic solution. These systems are essentially “screen scrapers” that mimic human keystrokes and mouse clicks. They are rigid and procedural. When a software interface is updated or a step in the process changes, the bot breaks, creating a maintenance backlog for IT and disrupting operations. This is not the robust framework needed for the strategic application of artificial intelligence in retail banking.
More recently, generic AI and low-code platforms have entered the market. While powerful, they introduce a different set of risks. Large language models can “hallucinate,” generating incorrect or fabricated information, a catastrophic liability in a regulated environment like banking. Low-code platforms, while more flexible than RPA, still require a developmental mindset and often lack the enterprise-grade governance and auditability that financial institutions demand. The impact of AI in retail banking cannot be fully realized if the technology introduces new, unacceptable risks.
To truly unlock the benefits of AI in retail banking, we need to change the fundamental way we interact with technology. The next generation of AI in retail banking is not about programming bots; it’s about teaching AI to understand business processes described in plain, natural language. This is the core of a new approach that makes retail banking using AI accessible to the people who know the processes best: the banking professionals themselves.
Imagine a compliance officer automating a new regulatory check by simply writing out the steps in English. Or a loan manager adjusting underwriting criteria without needing to file a ticket with IT. This is the power of a platform that uses English as its code. It democratizes automation, bridging the long-standing gap between business intent and technical execution.
This paradigm shift is enabled by a more advanced neurosymbolic AI architecture. Unlike purely generative models, this approach combines the language understanding of large models with a logical, reasoning framework. This is crucial for artificial intelligence in retail banking because it ensures processes are followed with precision and eliminates the risk of AI hallucinations. When the system can reason, it can handle exceptions intelligently, a key failure point for older automation. This makes retail banking using AI not just more efficient, but also safer and more reliable.
A successful strategy for AI in retail banking rests on three foundational pillars: operational intelligence, bulletproof governance, and a unified system of record. Building on these pillars ensures that automation is not just a series of isolated projects, but a cohesive, enterprise-wide capability that drives lasting value.
The goal of retail banking automation should be to create seamless, end-to-end workflows, not just to automate piecemeal tasks. A natural language-based platform excels here.
For any bank, auditability is non-negotiable. One of the most significant advantages of using English as the automation language is that the process is the documentation. Auditors and regulators can read the automation in plain English and understand exactly what the process does, creating a level of transparency that is impossible with traditional code or visual builders. This is a critical component for any retail banking AI platform. This inherent explainability, combined with an architecture that prevents hallucinations, provides the robust governance that is a prerequisite for the deep integration of artificial intelligence in retail banking.
Every bank runs on a massive amount of “tribal knowledge”—the unwritten rules and process nuances that live inside the heads of experienced employees. A major benefit of retail banking using AI with natural language is that it extracts this knowledge and codifies it into a dynamic, searchable, and permanent system of record. When an employee describes how to handle a specific type of customer dispute, that knowledge is captured as part of the automated process. This preserves institutional wisdom and ensures that operations are consistent, transparent, and continuously improving.
Embarking on a true transformation with AI in retail banking requires a strategic and deliberate approach. It’s about building a capability, not just buying a tool.
By following these best practices, leaders can ensure their investment in artificial intelligence in retail banking builds a truly resilient and intelligent operational foundation.
For years, business leaders have been caught in a difficult position. The promise of AI in business transformation has been immense, yet the reality has often fallen short. The landscape is crowded with solutions that offer incremental improvements but fail to deliver the fundamental change organizations need. Many platforms, born from the era of Robotic Process Automation (RPA), are brittle, breaking with the slightest change to a system or process. Others are generic, low-code builders that, while flexible, place the burden of creating complex logic and maintaining governance squarely on IT departments. This is not the strategic AI business transformation that leaders were sold.
The goal of this discussion is to cut through the noise. True, impactful business transformation is not achieved with yesterday’s tools. It requires a new approach, one that moves beyond simple task execution and embraces AI reasoning. This guide is for leaders who are ready to move past frustrating pilot projects and achieve scalable, intelligent automation. It’s about building a dynamic system of record for operations, empowering your teams, and making AI a practical, powerful engine for growth. The future of AI in business transformation is not about replacing humans with rigid bots; it’s about augmenting human intelligence with a new class of enterprise AI.
This shift requires a new understanding of what’s possible. It involves leveraging natural language to make automation accessible to the business experts who understand the processes best. This is the core of a successful AI business transformation: creating a system that is intelligent, governable, and fundamentally collaborative.
The initial wave of automation was driven by a simple idea: if a human can perform a repetitive, rules-based task on a computer, a software “bot” can do it faster and more consistently. This led to the rise of RPA, which specializes in screen scraping and mimicking human clicks and keystrokes. While it provided some initial value in automating simple tasks, its limitations became apparent as businesses tried to scale. This approach was never true AI in business transformation.
These first-generation tools are inherently fragile. An update to a software application’s user interface can break an entire workflow, requiring technical experts to step in and fix the script. They struggle with unstructured data—the invoices, emails, and documents that make up the bulk of real-world business processes. They are procedural, not intelligent; they follow a script but cannot reason through an exception or understand the intent behind a process. This created a cycle of dependency on IT and a growing backlog of broken or outdated automations, a far cry from the promised efficiency gains of AI business transformation.
Low-code and no-code platforms emerged as a response, offering more flexibility through visual, drag-and-drop interfaces. However, they introduced their own set of challenges. While they lowered the technical barrier to entry, they still required users to think like developers, mapping out complex logic flows and decision trees. This approach did not solve the core problem. It merely shifted the medium from code to a visual builder, failing to empower the actual business users who hold the process knowledge. The result was often “shadow IT” and a lack of centralized governance, posing significant risks to any enterprise-level AI in business transformation initiatives. These platforms lack the deep reasoning capabilities required for a genuine AI business process automation strategy.
To unlock the full potential of AI in business transformation, a fundamental shift in thinking is required. The next generation of automation is not about writing better scripts or designing more intuitive visual builders. It is about changing how humans and machines interact. This new paradigm is built on natural language process automation, where business users can describe their processes in plain English, and the AI understands, executes, and learns from those instructions.
This is the very essence of AI for business process automation. It moves the focus from the how (the specific clicks and scripts) to the what (the business outcome). When a finance expert can simply state, “If an invoice is over $10,000 and is not from a preferred vendor, route it to the department head for approval,” the system should understand and execute that logic. This is not science fiction; it is the power of a modern AI architecture that combines the strengths of large language models with symbolic reasoning.
By using English as the universal language for automation, Kognitos bridges the communication gap between business and IT. This isn’t just a friendlier user interface; it’s a completely different way of building and managing automations. The process itself becomes the documentation, creating a dynamic system of record that is always up-to-date and easily understood by everyone in the organization. This level of clarity and accessibility is critical for any successful AI business transformation. It’s the key to making business process automation with AI a reality for the entire enterprise. This approach to AI for business automation is what finally delivers on the original promise.
The journey of AI in business transformation is more than just innovative technology. It requires a strategic approach built on a foundation of unification, empowerment, and trust. Leaders who focus on these three pillars are the ones who will successfully move beyond isolated projects to achieve enterprise-wide, sustainable change. This is the difference between simply buying a tool and implementing a lasting AI business transformation strategy.
Many organizations find themselves managing a patchwork of specialized AI tools, RPA bots, and custom scripts. This “tool sprawl” is costly, inefficient, and creates data silos. A successful AI in business transformation strategy requires a unified platform that can handle diverse back-office processes, from finance and legal to HR and operations. This consolidation reduces complexity and allows the organization to develop a single, coherent automation strategy. A single platform that handles both structured and unstructured data enables the expansion of business process automation AI across endless use cases, breaking down departmental barriers and creating a more interconnected enterprise.
The McKinsey concept of “superagency“—empowering people with AI—is central to modern transformation. The individuals closest to a business process are the ones who best understand its nuances, exceptions, and opportunities for improvement. A successful AI business transformation puts the power of automation directly into their hands. By leveraging natural language, platforms like Kognitos enable finance and accounting experts to become citizen automators. They can build, manage, and refine their own workflows without waiting in an IT queue. This not only accelerates the pace of automation but also leads to more robust and effective solutions. It is the most effective form of AI business process automation.
For any Fortune 1000 company, trust and governance are non-negotiable. A significant barrier to AI adoption has been the “black box” problem, where the reasoning behind an AI’s decision is unclear. Modern AI in business transformation must be built on a foundation of transparency and control. This starts with using a neurosymbolic AI architecture, like that of Kognitos, which eliminates AI hallucinations by design. Every step of an automated process is auditable and explainable. Furthermore, a human-in-the-loop system, such as a Guidance Center, ensures that any exception or deviation from the standard process automatically pulls in human expertise. The system then learns from this guidance, continuously refining and improving the process. This creates a trustworthy and resilient framework for business process automation with AI.
The theoretical benefits of AI in business transformation become concrete when applied to real-world business challenges, particularly in finance and accounting. This is where the limitations of older systems become most apparent and where the power of an intelligent, unified platform delivers the most significant ROI. The goal of AI business transformation is to turn cost centers into strategic assets.
Consider the accounts payable process. With traditional automation, processing an invoice might involve a bot that uses optical character recognition (OCR) to scrape data and enter it into an ERP system. But what happens when the invoice is a poorly scanned PDF, or the line items don’t match the purchase order exactly? The bot fails, creating an exception that a human must manually resolve.
With true business process automation with AI solutions, the process is transformed. Advanced document processing capabilities, built directly into the platform, can intelligently read and understand any invoice format. The AI can perform a three-way match, and if it finds a discrepancy, it can reason through the problem. It might check the vendor’s past payment history, review the initial contract terms stored in another system, and then either approve the payment based on learned tolerance levels or route the exception to the correct person with a summarized explanation of the issue. This is a practical example of AI business process automation in action.
This same intelligence can be applied across the finance department. For the financial close process, an AI can automate reconciliations, consolidate data from disparate subsidiaries, and generate variance analysis reports, highlighting anomalies that require human attention. It transforms AI for business automation from a simple task-doer into a strategic partner for the finance team. This is the tangible result of a well-executed AI in business transformation.
Successfully integrating AI in business transformation requires a clear and strategic roadmap. For CIOs and heads of IT, the focus should be on finding AI transformation services and platforms that are built for scale, governance, and business empowerment, not just task automation. The journey begins with moving away from a project-based mindset to a capability-based one.
The first step is to identify a high-impact, complex process that has been a persistent bottleneck for the organization. This is where you can prove the value of a new approach to AI business process automation. Instead of a small pilot that automates a minor task, choose a challenge that, if solved, will deliver clear and significant business value. This success will build the momentum needed for a broader rollout.
Next, focus on the platform’s ability to create a unified system. Does it offer pre-built workflows that can be quickly deployed or customized? Can it integrate seamlessly with your existing legacy applications without relying on brittle APIs? Kognitos, for example, offers hundreds of pre-built workflows and browser automation for easy legacy app integration. These are the kinds of features that distinguish genuine AI transformation services from simple automation tools.
Finally, prioritize the human element. The success of your AI in business transformation initiative will depend on its adoption by your business users. Choose a platform that they can use and understand. Natural language is the key. When your teams can build and manage automations in English, you are not just implementing a new technology; you are building a new, more efficient culture of work. This is the ultimate goal of AI business transformation.
The conversation around AI in business transformation is at an inflection point. Leaders are no longer satisfied with incremental gains from fragile bots. They are looking for a strategic platform that can deliver scalable, intelligent automation and empower their teams. The future lies in natural language process automation, which makes the power of AI accessible to everyone.
By focusing on a unified platform, empowering business users, and ensuring robust governance, organizations can finally move beyond the hype and achieve the transformative promise of AI. This new approach creates a dynamic and intelligent system of record for business operations, turning automation into a true competitive advantage and completing the journey of AI business transformation.
For the better part of a decade, the narrative around Customer Service Automation has been dominated by two technologies: the chatbot and the ticketing system. The vision was compelling—a world where simple queries are deflected by bots, and complex issues are neatly organized for human agents, leading to massive efficiency gains and happier customers. Spurred by this vision, enterprises have invested billions in a vast ecosystem of SaaS platforms and AI-powered tools.
And yet, for most organizations, the promised revolution never arrived. While front-end interactions have become more structured, the underlying work of actually solving a customer’s problem remains a stubbornly manual, inefficient, and chaotic process. Our agents are more organized, but they are no less overwhelmed. The “automation” we purchased has turned out to be a sophisticated system for managing conversations, not a system for resolving issues.
This is the great failure of modern customer service automation: it has optimized the front door while ignoring the messy, labyrinthine factory behind it. To truly transform the customer experience and unlock real operational efficiency, CIOs and Heads of Customer Experience must look beyond the chatbot and confront the true source of friction: the complex, manual, cross-system work that happens after the initial ticket is created.
The fundamental flaw in most customer service automation software is that it is blind to the actual work your agents do. A ticketing system can categorize a problem, but it cannot solve it. The real work—the investigation, the data gathering, the cross-departmental coordination—happens outside the system, in a manual “swivel-chair” process that is the true enemy of productivity.
To understand how to automate customer service process work effectively, consider the “simple” request of “Where’s my refund?”:
This is not an automated process. It is a series of fragmented, manual tasks held together by an expensive human agent acting as a human API. This is the reality that traditional customer service automation ignores, and it is a colossal drain on resources and a primary driver of poor customer satisfaction (CSAT). The challenge of automating customer service is solving this entire workflow.
To break this cycle of manual inefficiency, leaders need a new approach. Agentic AI represents a fundamental paradigm shift for Customer Service Automation. It moves beyond managing tickets to providing an intelligent engine that can execute the entire end-to-end resolution process, based on instructions provided in plain English.
This is the key to unlocking true customer service automation. An AI agent can be instructed to perform the entire investigative workflow autonomously. A Head of Customer Experience, without writing a single line of code, can define the process:
“When a ticket is created with the subject ‘Refund Status’, extract the order number. Query our ERP to find the associated RMA. Check the logistics system to confirm the return was received. If it was received, check the finance system for the refund transaction ID. If no refund has been processed, create a high-priority task for the Accounts Payable team and update the ticket with all findings for the agent to review.”
The AI agent then uses its reasoning capabilities to navigate the different applications—the ERP, the logistics portal, the finance system—to get the job done. Crucially, it’s built for the real world. When an exception occurs—the RMA is missing, or the logistics system is down—the agent doesn’t just fail. It can be taught how to handle the exception or pause and escalate the ticket with all the context it has gathered, allowing a human agent to step in with perfect information. This creates a system for automating customer service that is not just automated, but truly autonomous and resilient.
Kognitos is the industry’s first neurosymbolic AI platform, purpose-built to deliver this new, intelligent model of automation. It is not another chatbot or a better ticketing system. It is the autonomous operational backbone that connects your service desk to the rest of your enterprise, automating the complex back-office work required to actually solve your customers’ problems.
The power of Kognitos lies in its unique neurosymbolic architecture. This technology combines the language understanding of modern AI with the logical precision required for enterprise-grade operations. This is a non-negotiable for any process that touches financial or customer data. It means every action the AI takes is grounded in verifiable logic, is fully auditable, and is completely free from the risk of AI “hallucinations.”
With Kognitos, you can finally achieve true Customer Service Automation:
This is the new standard for customer service automation software.
When you move from conversation management to true resolution automation, the benefits are strategic and profound. This is how to automate customer service process work to drive real business value.
The industry’s obsession with customer service automation has been narrowly focused on the front-end conversation for far too long. We have built sophisticated systems to talk to our customers, while leaving our agents to manually navigate the complex back-office labyrinth to actually solve their problems. This is a fundamentally broken model that creates friction, burns out employees, and erodes customer trust.
The future of a world-class customer experience is not a slightly more human-sounding chatbot. It is an autonomous operational backbone that empowers your human agents with instant, accurate information. By automating the entire end-to-end resolution process, you don’t just create efficiency; you transform the very nature of your service team. They are elevated from manual data-fetchers to true customer advocates. The path to exceptional customer service automation is not through a better ticketing system, but through a smarter, self-running enterprise that works tirelessly for your customer in the background.
AI agents are hitting production supporting enterprise automation use cases and it’s reshaping the automation industry. The shift is so profound that Gartner predicts by 2028, the demand for AI agents from business users will outpace all traditional development combined.
This presents a massive challenge. How can CIOs and IT departments meet this incredible demand with already strained resources, without losing control and creating a “Shadow AI” crisis as the next in a line of failures linked to citizen development?
The answer lies in a new category of technology that Gartner calls No-Code Agent Builders (NCABs). When implemented correctly, these platforms forge a powerful partnership between business experts and IT. But be warned: a wave of “agent-washing” is flooding the market, where vendors rebrand older automation technologies and workflow tools as agentic AI. These legacy platforms lack the intelligence, governance, and collaborative features required for this new era, creating risk while failing to deliver on the transformative promise of AI.
This article cuts through much of the market hype. We’ll define what an NCAB is, outline the critical capabilities that distinguish an enterprise-grade platform, and show you how to enable business-led innovation without sacrificing IT governance.
Gartner defines an AI agent platform as a cohesive set of technologies designed to facilitate the creation, deployment, and management of AI agents. An NCAB is a specific type of this platform, offering an integrated environment to build, publish, and manage AI-powered agents with direct coding or code manipulation. These platforms are designed to abstract away technical complexity, allowing non-developers to create autonomous or semi-autonomous software entities that can perceive their environment, make decisions, and take actions to achieve goals.
While business user empowerment is not new, successful execution of this ideal has been elusive. NCABs are fundamentally different from the automation tools you might be familiar with.
While the goal of all NCABs is to simplify agent creation, the approach they take makes all the difference between creating business value and creating the next generation of Shadow AI. A truly enterprise-grade NCAB must possess these three capabilities.
Many NCABs try to abstract code through a visual drag-and-drop interface. While this works for simple workflows, it fails when modeling real-world business processes, quickly becoming an unmanageable web of boxes and arrows. It’s a visual representation of code, and it still requires a technical mindset that excludes the true process experts.
A superior approach is needed—one that enables a new, governed model of citizen programming. In this model, business experts program the agent by defining its logic in a language they already master: natural language.
Kognitos is built on this principle. By using English as the programming language, we provide a radically accessible interface for business users to articulate their process needs directly. This allows them to define the “what” and “why” of an automation, which is then executed on a platform fully governed and authorized by IT. It creates a perfect bridge between business intent and IT control.
All NCABs use AI to power their agents, but many operate as a “black box,” making it impossible to audit their decisions or understand why they took a specific action. This lack of transparency is unacceptable in an enterprise environment where trust, compliance, and reliability are paramount.
An enterprise-grade NCAB must provide transparent and explainable AI reasoning.
At Kognitos, our platform is built for this transparency. Every step an agent takes is grounded in the plain-English process defined by the user. The reasoning is self-documenting and auditable by design. This fulfills our core belief in Transparency and Safety, building the trust required for automating mission-critical operations.
Gartner explicitly warns that unrestrained agent development will lead to “agent anarchy”—a state of conflicting automations, security gaps, and spiraling operational risk.
This risk is highest with purely “goal-driven” agents that operate without strong guardrails. A superior NCAB enables process-driven agents that are grounded in an explicit, human-readable business process.
Kognitos is founded on this Process-Centric Design. By anchoring every agent to a clear, auditable process written in English, we provide the ultimate guardrail. This combines the dynamic intelligence of AI with the safety and control IT leaders need to prevent Shadow AI and manage automation at scale.
The greatest value of AI agents lies in automating “core work”—the complex, value-generating processes at the heart of the business.
Imagine a finance coordinator using Kognitos to scope an agent that automates the entire accounts payable process—from interpreting unstructured invoices to handling exceptions with vendors. IT reviews the agent’s logic (written in plain English), authorizes its access to necessary systems, and deploys it, knowing it’s fully governed.
This is the strategic power of a true NCAB. It empowers business experts to solve their own high-value problems within a secure IT framework. By building an army of intelligent, process-driven agents for core functions, organizations create an adaptable and resilient foundation for future innovation, truly Investing in Optionality.
The future of automation is business-led and IT governed. But achieving that future requires a new kind of platform. A true enterprise-grade NCAB achieves this through an accessible interface like natural language, a foundation in transparent AI reasoning, and a design centered on process-driven governance.
Don’t settle for “agent-washed” RPA or siloed workflow tools that threaten to create a new generation of Shadow AI. The goal is to create ways for AI to work for and with us, in our language that nearly everyone can understand.
Ready to move past the hype and build real AI agents? See how Kognitos is leading the NCAB revolution.
For the past five years, a powerful narrative has taken hold in the enterprise: technology will solve the challenge of Environmental, Social, and Governance (ESG) reporting. We’ve been promised a future of seamless data flows and push-button reports. Spurred by this vision, companies have invested millions in sophisticated ESG platforms, GRC tools, and data warehouses.
And yet, what is the reality for most finance, technology, and sustainability leaders? The annual reporting cycle is still a frantic, all-consuming fire drill. Teams spend hundreds of hours manually chasing down data from dozens of disconnected sources, living in a nightmare of spreadsheets, PDF invoices, and endless email chains. The powerful dashboards we bought are essentially empty vessels, waiting for a flood of manually collected data to give them meaning.
This is the great disconnect in the world of ESG and AI: we have built beautiful systems for storing data, but we have completely failed to automate the complex, chaotic, cross-system work of getting that data. We have been sold a dashboard, but what we desperately need is an engine. The future of AI in ESG is not about a better chart; it’s about building an autonomous engine that can reliably gather, validate, and report on data with minimal human intervention.
The fundamental flaw in most ESG platforms is that they don’t see the real work. They are the final destination, blind to the arduous journey the data takes to get there. To truly appreciate the problem, you must look at the “last mile” of data collection—the invisible, manual processes that consume your team’s time.
Consider the “simple” task of gathering Scope 2 emissions data (indirect emissions from purchased electricity) for a global company with 500 locations. A real strategy for using AI for sustainability must solve this entire workflow:
This is not automation. It is a series of fragmented, brittle, and soul-crushing manual tasks. This is the reality that most ESG AI strategies have completely failed to address. The real challenge of AI in ESG is not analysis; it’s acquisition.
To solve this deep-seated operational problem, leaders need a new class of technology. Agentic AI represents a fundamental paradigm shift for ESG AI. It moves beyond dashboards to provide an intelligent engine that can execute entire end-to-end business processes, based on instructions provided in plain English.
This is the key to solving the last-mile problem. An AI agent can be instructed to perform the entire data collection workflow autonomously. A sustainability manager, without writing a single line of code, can define the process:
“On the 5th of each month, log into our list of 500 utility provider portals. Download the latest electricity invoice PDF. Extract the total kWh consumed and the billing period. Enter this data into our ESG data warehouse, flagging any locations where consumption increased by more than 15% month-over-month.”
The AI agent then uses its reasoning capabilities to navigate the different portals, read the PDF documents, and execute the workflow. Crucially, it’s built for the real world of messy data. When a utility provider changes their invoice format, the agent doesn’t just fail. It can be taught how to handle the new format, learning from human guidance to become more resilient over time. This is how ESG data artificial intelligence moves from a concept to a practical reality. This is the future of AI in ESG.
Kognitos is the industry’s first neurosymbolic AI platform, purpose-built to deliver this new, intelligent model of automation. It is the autonomous engine that powers your entire ESG data lifecycle, automating your most critical and complex compliance and reporting processes using plain English.
The power of Kognitos lies in its unique neurosymbolic architecture. This technology combines the language understanding of modern AI with the logical precision required for enterprise-grade audit and compliance processes. This is non-negotiable for any CFO or Chief Sustainability Officer. It means every action the AI takes is grounded in verifiable logic, is fully auditable, and is completely free from the risk of AI “hallucinations.” This ensures the absolute integrity of your ESG data.
With Kognitos, you can finally achieve true ESG AI automation:
This is the new standard for AI in ESG and the only way to achieve a state of “always-on” audit readiness. The combination of ESG and AI is powerful when done right.
When you move from a passive dashboard to an active automation engine, the benefits of using AI for sustainability become strategic, not just operational.
The conversation around ESG AI has been fixated on the finish line—the final report—while ignoring the brutal, manual marathon required to get there. The future of sustainability reporting will not be defined by a more beautiful dashboard or a slightly faster analytics engine. It will be defined by the elimination of the manual, soul-crushing work that currently underpins the entire process.
By shifting the focus from the report to the workflow, and from the analyst to the autonomous agent, leaders can finally solve the “last mile” problem of data collection. The goal of AI in ESG should not be to create a better tool for your team to use, but to create an intelligent system your team can delegate to. This is how you move beyond the illusion of automation and build a truly resilient, audit-ready, and strategic sustainability program. The future isn’t just about reporting on your ESG performance; it’s about creating an autonomous operational foundation that actively improves it.
The conversation around AI in workplaces has reached a fever pitch, and at the heart of it is a single, compelling promise: a massive boost in employee productivity. We are inundated with a new generation of AI-powered tools—personal assistants, content generators, and data analyzers—all designed to make individual workers faster, smarter, and more efficient. And they do. An employee equipped with these tools can undoubtedly write a report or analyze a dataset faster than one without.
But this has created a dangerous paradox. While individual employees are getting faster at their specific tasks, the overall velocity of the business is not keeping pace. The financial close still takes weeks. Compliance audits are still a frantic fire drill. Supply chains are still brittle. The reason is simple: we have been hyper-focused on optimizing the worker, while ignoring the work.
The most significant drain on organizational productivity is not the speed at which an employee can type; it is the chaotic, manual, and cross-system business processes that form the operational backbone of the enterprise. True artificial intelligence increases productivity not by making one person’s work 10% faster, but by eliminating the thousands of hours of manual work that happen in the gaps between your systems. It’s time to elevate the conversation from personal productivity hacks to true process autonomy.
The current generation of AI in workplaces suffers from a “last mile” problem. They are excellent at starting a process or analyzing data, but they cannot see a complex business process through to its conclusion.
Consider the limitations of common tools:
These tools are useful, but they only take the work so far. They leave the most complex, judgment-intensive “last mile” for your most expensive human talent to handle manually. This is not a sustainable model for AI increasing productivity.
To understand how AI improves productivity at an enterprise level, you must first see the invisible web of manual work that is the true bottleneck. This work doesn’t live in a single application; it lives in the manual “swivel-chair” interfaces between them.
Let’s look at a common, critical process: the quarterly user access review for SOX compliance. This is a massive drain on employee productivity across the entire organization.
This is the reality of using AI for work in most large companies today. We have sophisticated systems, but the processes that connect them are entirely manual, held together by spreadsheets and heroic human effort. This is a colossal waste of time and talent. This is the problem that true AI in workplaces must solve.
To achieve a true step-change in organizational velocity, we need to shift our focus from making individual employees faster to making our core business processes autonomous. This requires a new class of technology. Agentic AI represents a fundamental paradigm shift in how AI improves efficiency.
Unlike a simple bot or a personal assistant, an AI agent is an intelligent entity that can manage an entire end-to-end business process. It can be instructed in plain English to execute complex, multi-step, cross-system workflows that require reasoning and judgment.
This is the key to unlocking real productivity. Instead of giving an accountant an AI tool to help them do the reconciliation faster, you give them an AI agent that they can delegate the entire reconciliation process to. This moves the human employee from being a “doer” of manual tasks to a “manager” of an autonomous digital workforce. This is how AI improves productivity at a strategic level.
The ultimate goal of AI in workplaces should be to create what the McKinsey Global Institute calls “superagency“—a state where employees are empowered to work at their full potential, augmented by AI. This is the core philosophy behind the Kognitos platform.
Kognitos is the industry’s first neurosymbolic AI platform, purpose built to deliver this new model of autonomous work. We are not another personal productivity tool. We are a comprehensive platform that automates your most critical and complex back office processes using plain English.
The power of Kognitos lies in its unique approach:
When you shift from task automation to process autonomy, the benefits extend far beyond simple time savings. This is what a true strategy for using ai for work delivers.
The debate over how AI improves productivity has been sidetracked by a focus on personal tools and individual speed. While these have their place, they do not address the fundamental friction that slows down an enterprise. The future of work will not be defined by how quickly an employee can write an email, but by how autonomously the business can execute its most critical operations.
By shifting the focus from the worker to the work, and from the task to the process, leaders can unlock a new level of organizational velocity. The goal of AI in workplaces should not be to create slightly faster employees, but to empower them with “superagency”—the ability to delegate entire workflows to intelligent agents they control. This is how you eliminate the drag of manual work, unleash the strategic potential of your best talent, and build a business that is not just more productive, but truly autonomous and resilient. The future of employee productivity is not a better assistant; it’s a smarter, self-running enterprise.
For over a decade, finance and technology leaders have been sold a powerful vision of order-to-cash automation. The promise was transformative: a seamless, touchless process from the moment a customer places an order to the moment the cash is in the bank. We invested millions in RPA, sophisticated ERP modules, and a patchwork of specialized software, all aimed at accelerating cash flow and freeing our teams for more strategic work.
Yet, what is the reality in most large enterprises? The O2C process remains a fragmented, high-friction ordeal. Teams are still buried in manual data entry, endless email chains, and the painstaking work of reconciling data across a dozen different systems. The “automation” we purchased has turned out to be an illusion. It has automated simple tasks, but it has completely failed to automate the complex, end-to-end business process.
This is the great failure of traditional Order to cash automation: it has placed digital bandaids on a fundamentally broken workflow. To truly solve this, CIOs and CFOs must challenge the limitations of their current tools and embrace a new, more intelligent paradigm.
The core flaw in most O2C automation strategies is that they ignore the invisible, manual work that happens between the systems. Your ERP might log an order, but it can’t read the customer’s PDF purchase order to check for special terms. Your invoicing software can send a bill, but it can’t understand the customer’s emailed remittance advice.
This is the reality of modern order to cash processing. Consider the “simple” lifecycle of a single customer order:
This is not an automated process. It is a series of disjointed manual tasks that create friction, delay revenue recognition, and frustrate both your employees and your customers. This is the core challenge that any real Order to cash automation solution must solve.
To conquer this deep-seated operational chaos, leaders need a new class of technology. Agentic AI represents a fundamental paradigm shift for the O2C process. It moves beyond dashboards and rigid bots to provide an intelligent engine that can execute entire end-to-end business processes, based on instructions provided in plain English.
Instead of just logging an order, an AI agent can be instructed to manage the entire workflow. A finance manager, without writing any code, can define the process:
“When a purchase order is received via email, extract the line items and create a sales order in our ERP. Run a credit check on the customer. If they are within their limit, release the order for fulfillment. Once shipped, generate the invoice and send it to the customer’s AP contact. Monitor for payment and apply the cash upon receipt.”
The AI agent then uses its reasoning capabilities to navigate the different applications—your ERP, your email, your credit provider’s portal—to get the job done. Crucially, it’s built for the real world. When an exception occurs—the customer’s PO format is new, or they send a partial payment—the agent doesn’t just fail. It can be taught to handle the exception or pause and ask a human expert for guidance. This creates an Order to cash processing system that is not just automated, but truly autonomous and resilient. This is the only way to succeed with Order to cash processes case automating.
Kognitos is the industry’s first neurosymbolic AI platform, purpose-built to deliver this new, intelligent model of automation. Kognitos is not another RPA tool or a better dashboard. It is a comprehensive platform that automates your most critical and complex back-office processes using plain English.
The power of Kognitos lies in its unique neurosymbolic architecture. This technology combines the language understanding of modern AI with the logical precision required for enterprise-grade financial processes. This is non-negotiable for any CFO. It means every action the AI takes, from creating an invoice to applying cash, is grounded in verifiable logic, is fully auditable, and is completely free from the risk of AI “hallucinations.” This ensures the absolute integrity of your financial data.
With Kognitos, you can finally achieve true Order to cash automation:
When you move from task automation to intelligent process automation, the Order-to-cash (O2C) automation benefits become strategic, not just operational.
A More Strategic Finance Team: By freeing your team from the endless cycle of manual data entry and exception handling, you empower them to focus on high-value work: analyzing customer profitability, improving cash flow forecasting, and acting as true strategic partners to the business.
The path to a truly efficient O2C process is not paved with more bots or another piece of siloed software. It requires a fundamental shift in thinking—away from automating isolated tasks and toward orchestrating the entire, end-to-end business workflow. The operational friction that delays your cash flow and burns out your team doesn’t live within a single application; it lives in the manual gaps between them.
By empowering your finance and operations teams with intelligent agents that can reason, adapt, and learn from exceptions, you can finally move beyond the illusion of automation and build a system that is truly autonomous. This is not just about improving payment collection or accelerating the close; it’s about creating a resilient, data-driven, and strategic finance function that can power the growth of the enterprise. The future of Order to cash automation has arrived, and it’s not a better bot—it’s a smarter process.
