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Operationalizing enterprise AI: Strategic investments for 2026

nuno-borges

Nuno Borges August 04, 2026 • 4 min read

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It’s been a year of major shifts in AI, and it’s not even over yet. Anticipating artificial intelligence trends, allocating resources, and planning strategically are among the biggest pressures tech leaders are feeling. In 2025, low-stakes experimentation and isolated pilot projects have given way to questions about when enterprise AI initiatives will yield measurable operational value.

According to Gartner’s research report, “Critical Technology Markets for Software Engineering 2026” (Brad Dayley, Charles Smulders, July 6, 2026), software engineering organizations have an evolving imperative: By 2030, AI technologies will enable the software development life cycle (SDLC) to transform into a unified AI-driven, human-guided workflow. We believe this means operationalizing AI needs to be the focus of any initiative, no matter how lofty.

So, how can engineering leaders transform high-level corporate ambition to scalable, production-grade AI and application systems? They start by making deliberate, foundation-level technology investments. Gartner highlights ten critical technology markets that enable organizations to accelerate delivery and scale AI across the enterprise as they race to the next decade.

Among these top strategic markets, Gartner identifies Enterprise Low-Code Application Platforms, identifying OutSystems as a key platform in this space.

Here is how a platform like OutSystems can serve as the core infrastructure required to operationalize your enterprise AI strategy.

The operational gap: Why enterprise AI strategies stall

Designing a compelling AI implementation strategy on paper is relatively straightforward. Executing it in a complex, legacy-laden enterprise architecture is where most organizations hit friction. Without unified platforms to support deployment and lifecycle management, engineering teams run into predictable barriers:

  • Siloed AI experiments: Building standalone AI agents or models without robust integration into core enterprise business processes limits their impact and increases maintenance overhead.
  • Entrenched technical debt: Legacy application architectures often lack the APIs, modularity, or data accessibility required to support modern generative AI capabilities.
  • Governance issues: AI sprawl and the use of multiple tools to build solutions create separate architectures and context, making it difficult for solutions to work together.
  • Security risks: Deploying non-deterministic AI solutions without centralized architectural controls introduces severe compliance, cost, and security exposure.

Operationalizing enterprise AI is a software engineering challenge. It requires an enterprise-grade execution layer that connects AI logic directly to workflows, underlying data, and user interfaces.

3 pillars for operationalizing AI in software engineering

To turn strategy into execution, technology leaders are prioritizing platforms that combine lifecycle automation, enterprise integration, and structured guardrails to deliver the following benefits.

1. Seamless enterprise integration and data access

An AI agent is only as effective as the enterprise context it can access. Modern application platforms that integrate with existing architectures and legacy systems can ensure that AI capabilities interact securely with core enterprise databases, ERPs, and internal workflows.

2. Embedded AI lifecycle automation

High-performance application platforms embed generative AI capabilities and agentic workflows directly into the developer experience. Developers use AI assistants for automated code generation, architecture checks, and refactoring, all of which accelerate application delivery while maintaining strict quality standards.

3. Accelerated legacy modernization

You can’t build an AI-driven enterprise on fragile, legacy foundations. Modern application platforms provide the tools necessary to refactor monolithic applications into modern, cloud-native architectures. This clears technical debt and creates the clean, API-driven foundation required for enterprise AI integration.

OutSystems: The execution layer for enterprise AI

Recognized in Gartner's report as a leader in the enterprise low-code application platforms market, OutSystems provides the open, unified, and governed platform required to take enterprise AI strategies from concept to production.

OutSystems bridges the gap between AI innovation and enterprise-grade execution. With OutSystems, engineering teams can:

  • Deploy mission-critical applications: Rapidly build secure web and mobile applications powered by embedded AI capabilities.
  • Systematically eliminate tech debt: Execute legacy application modernization strategies safely without disrupting core business operations.
  • Orchestrate AI workflows: Integrate AI agents, large language models (LLMs), and enterprise data sources under unified governance controls.
  • Enforce enterprise security: Maintain strict architectural compliance, role-based access, and lifecycle guardrails across every application deployed.

Moving from strategy to execution

A successful enterprise AI strategy requires sophisticated models and a modern software engineering foundation that delivers secure, scalable applications at velocity. Equipping your development teams with the right application platform is the single most effective way to turn AI ambition into operational reality.

Accelerate your AI strategy with a unified platform. Visit the OutSystems website to learn how our agentic systems platform makes it possible, or explore our introductory guide to agentic systems engineering.

Gartner Disclaimer

Gartner, Critical Technology Markets for Software Engineering 2026, Brad Dayley, Charles Smulders, July 6, 2026.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

nuno-borges

Nuno Borges

Nuno Borges has worked in IT for more than 20 years in executive roles. His experience spans the manufacturing sector supporting the engineering and production of Financial Services products and solutions, professional services and retail. From 2016, he led the IT Delivery organization transformation of a major retailer and its digital initiatives. From E-Commerce platforms, B2E mobile apps, scaling legacy via APIs, to the launch on a B2C OutSystems app with more than 400,000 active users, Nuno has both deep and broad experience across a variety of innovative technology initiatives.

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Operationalizing enterprise AI: Strategic investments for 2026

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Nuno Borges\r\n\tSeptember 30, 2025 • 7 min read\r\n

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Agentic AI is moving quickly

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The conversation around agentic AI is moving quickly from “how would we use agents?” to “what couldn’t we do with agents?” Take TravelEssence, for example. After experimenting with agentic AI and implementing two agents, the company realized that agents can address more use cases than they first thought. Now it has adopted OutSystems Agent Workbench and is creating a “swarm of AI agents” to accelerate the delivery of tailored travel recommendations for its clients.

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What does this mean for IT leaders who now must manage a proliferation of autonomous workers and dynamic agentic processes in their portfolio? Remember, agents are software too, with all the same potential for errors, security gaps, and governance concerns (but exponentially magnified). As with any accelerated and automated process, from traditional manufacturing to smart factories or human-led customer service to virtual agents, for example, we have new and far-reaching responsibilities to manage the full lifecycle of agentic apps and systems.

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Here are some strategies we are offering our customers to plan a successful and sustainable agentic transformation.

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Navigating the journey to the agentic AI enterprise

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In an agentic AI enterprise, AI removes obstacles that slow human teams down. When grunt work, context switching, disparate communications, and unnecessary complexity are shifted to agents, everyone has more time to ideate, iterate, and innovate. This creates a positive cycle where more efficient processes and better customer and employee experiences lead to new insights, creating opportunities for even more innovation.

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Simply adopting agentic AI is not a straight path to becoming an agentic enterprise, however. Today, most organizations are adding agents to an already fragmented landscape of apps and data. Many everyday productivity apps and SaaS platforms include AI agents now and some invite nontechnical employees to build their own. Marketing builds chatbots on one platform, operations uses a different framework for process automation, and customer service deploys agents through yet another vendor. Soon you have a collection of isolated AI experiments that can't share data, can't be governed consistently, and create more problems than they solve.

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IT leaders should resist the temptation to restrict agentic AI experimentation. Instead, they should establish a system for full portfolio visibility and governance that works with this reality and assumes that it will continue to evolve in unpredictable ways. The goal should be to design agentic systems where humans and AI complement each other, with human-on-the-loop flows that give people final control. AI handles the heavy lifting on data processing and routine decisions. Humans provide additional strategic insight on patterns that AI has not encountered and oversight for complex or sensitive situations.

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Ultimately, the heroes of the agentic AI enterprise will be the strategic leaders who build a foundation for rapid prototyping and iteration not as a siloed R&D initiative, but as a fully integrated innovation engine within their current operations. This means empowering teams to experiment with different LLMs and build AI agents that connect to your existing applications, workflows, and data. In this way, the agentic enterprise is an organic, living ecosystem that evolves for the demands of the future by using and improving on what is already working.

\n

Land immediate agentic AI impact, then expand

\n

Companies are holding off on enterprise-wide AI deployment because they believe that making agentic AI a core part of business and IT is a major undertaking. But waiting has an opportunity cost, not just in terms of the inevitable AI sprawl, but also the risk of disruption by their competitors who use agentic AI to launch new products and services faster. Forward-thinking leaders are taking an iterative approach: identifying specific use cases where AI agents can make an immediate impact, prototyping and testing, then expanding from there.

\n

Another belief is that agentic AI demands we leave behind the applications and data already in place. In reality, your legacy systems, CRM, ERP, and custom applications are valuable assets that significantly increase the value of your AI agents. The technology already exists to harmonize historical and real-time data stored in different repositories and formats to be used by agents, while also making sure that the surface exposed to AI is limited to the context of a given agent interaction.

\n

Fears about runaway costs with agentic AI are also misplaced. The cost equation is straightforward when you approach it correctly. You track how many tokens each agent consumes, understand the cost per token based on your AI provider contracts, and measure that against the outcomes the agent delivers. It's similar to traditional resource management, just with a new type of resource.

\n

Use a unified platform for the full lifecycle of agentic AI

\n

It is early days in the agentic software revolution, and with the technology evolving at astonishing speed, many organizations are experiencing runaway agent sprawl. IT teams are having to determine how to manage a heterogeneous portfolio of agentic apps that includes custom-built apps and agents. While several AI vendors are offering agent orchestration layers, they are limited to those built with their products.

\n

What is needed is a platform that provides a unified view of all agents, their lineage, and their decisions. When five linked agents are handling their tasks, a change to the first one could have exponential impacts on the fifth. Or consider the situation where an agent depends on data from a specific system and then the data structure changes or evolves. It’s possible that, with such a brittle system, the change could break the input the agent expects. You need full visibility across agents, experiences and data, in a lifecycle that supports continuous change so that no agents or systems are broken. A unified platform can provide that view, while tracing the chain from end to end to make sure you always understand and improve the full process.

\n

The same principles that made low-code software development platforms a strategic foundation for agile enterprises apply to the agentic AI enterprise:

\n\n

The powerful combination of accelerated AI-driven development with guaranteed performance is what makes Agent Workbench stand out in a crowded field of AI app and agent generation tools.

\n

Support your transformation to an agentic enterprise with OutSystems

\n

At OutSystems, we’ve built Agent Workbench to simplify the path to becoming an agentic AI enterprise. Part of our unified approach to application and agent development, Agent Workbench makes it easy for any IT team to create, deploy, and manage AI agents that integrate with existing systems and deliver measurable business value.

\n

The OutSystems AI-powered low-code platform handles the complexity of agent lifecycle management, from development and testing to deployment and monitoring. Teams can create agents that connect to any data source, work with any AI model, and integrate into existing applications and workflows.

\n

Most importantly, it's designed to work with what you already have. Your data, your applications, your team's existing skills. You're not starting from scratch or learning entirely new development approaches.

\n

Your agentic AI future is here

\n

The agentic enterprise isn't a distant future concept. Nor is it about AI that can write an email for you. It's about empowering AI to solve problems so you don’t have to write emails to address or explain them. OutSystems has brought your data, your systems, your people, and AI together in a platform you trust.

\n

Join us as we walk together towards this future.

\n

Discover how innovative organizations are transforming with agentic AI in “Agents of Change,” and get started with Agent Workbench.

\r\n
\r\n","8d1a84eefb8143b08c37f06fb416a0e1|DynamicPlaceholderId=3":"\r\n\r\n
\r\n
\r\n

When enterprise IT leaders first began to embrace low-code software development back in the mid-2000s, they measured the time savings in hours per developer. Today, they are measuring improvements in time to market, in business agility, and in customer experience. How much more could your organization do if you could get a decade’s worth of software to market in one week? In one day?

\n

Welcome to the “agentic AI enterprise,” where developers and other creators can build autonomous AI agents for every task and customer interaction, and agents actively participate in business processes, reason, and collaborate with humans. The agentic enterprise is an intelligent ecosystem that capitalizes on the strengths of people and machines, without the constraints on capacity that have historically limited innovation.

\n

If you think this is some kind of future state, think again. In a 2025 survey of 1000 U.S. business executives, one in five executives report using AI agents to automate customer support, streamline supply chain operations, and personalize customer experiences at scale.

\n

But this is just the beginning.

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\r\n"},"e37ecfcd4dd5418fa241c1bd9390f5bf":{"47e4ffcc55a147c989e582df18d4d035|FieldNames=%7bE4C727C4-B2BE-4FCF-ADED-E3EFB82954BD%7d":"\r\n\r\n\r\n \r\n\r\n\r\n\r\n","a1229f9c4733483db4a9ab1aa674c911|FieldNames=%7bD21FF54B-AA84-4B4D-A6D2-F9B6B55F5B6B%7d":"\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"e3e3f4840c2742cc9f36e2d33430c8cd":{"00000000000000000000000000000000|":"\r\n \r\n"},"e5a66b8f5f114b3582315f5119f58189":{"00000000000000000000000000000000|FieldNames=%7bCEDAA056-0DCA-4293-986E-E225BA0FD620%7d":"\r\n
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\r\n \r\n \r\n\t \r\n \"nuno-borges\"\r\n \r\n\t \r\n \r\n
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\r\n \r\n\t \r\n

Nuno Borges

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After ten years of hardcore development, Gon\u00e7alo got bored with the repetitive tasks that are required project after project. So, he adventured into finding the best way to bring automation and productivity to the rest of us.

\r\n See All Posts From this author\r\n
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\r\n\r\n\tPerspectives\r\n\r\n\r\n\t

Operationalizing enterprise AI: Strategic investments for 2026

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\r\n \r\n \r\n \"nuno-borges\" \r\n \r\n \r\n

Nuno Borges\r\n\tSeptember 30, 2025 • 7 min read\r\n

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\"hero-bp-operationalizing-enterprise-ai\"\r\n
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Table of contents:

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Agentic AI is moving quickly

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The conversation around agentic AI is moving quickly from “how would we use agents?” to “what couldn’t we do with agents?” Take TravelEssence, for example. After experimenting with agentic AI and implementing two agents, the company realized that agents can address more use cases than they first thought. Now it has adopted OutSystems Agent Workbench and is creating a “swarm of AI agents” to accelerate the delivery of tailored travel recommendations for its clients.

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What does this mean for IT leaders who now must manage a proliferation of autonomous workers and dynamic agentic processes in their portfolio? Remember, agents are software too, with all the same potential for errors, security gaps, and governance concerns (but exponentially magnified). As with any accelerated and automated process, from traditional manufacturing to smart factories or human-led customer service to virtual agents, for example, we have new and far-reaching responsibilities to manage the full lifecycle of agentic apps and systems.

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Here are some strategies we are offering our customers to plan a successful and sustainable agentic transformation.

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Navigating the journey to the agentic AI enterprise

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In an agentic AI enterprise, AI removes obstacles that slow human teams down. When grunt work, context switching, disparate communications, and unnecessary complexity are shifted to agents, everyone has more time to ideate, iterate, and innovate. This creates a positive cycle where more efficient processes and better customer and employee experiences lead to new insights, creating opportunities for even more innovation.

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Simply adopting agentic AI is not a straight path to becoming an agentic enterprise, however. Today, most organizations are adding agents to an already fragmented landscape of apps and data. Many everyday productivity apps and SaaS platforms include AI agents now and some invite nontechnical employees to build their own. Marketing builds chatbots on one platform, operations uses a different framework for process automation, and customer service deploys agents through yet another vendor. Soon you have a collection of isolated AI experiments that can't share data, can't be governed consistently, and create more problems than they solve.

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IT leaders should resist the temptation to restrict agentic AI experimentation. Instead, they should establish a system for full portfolio visibility and governance that works with this reality and assumes that it will continue to evolve in unpredictable ways. The goal should be to design agentic systems where humans and AI complement each other, with human-on-the-loop flows that give people final control. AI handles the heavy lifting on data processing and routine decisions. Humans provide additional strategic insight on patterns that AI has not encountered and oversight for complex or sensitive situations.

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Ultimately, the heroes of the agentic AI enterprise will be the strategic leaders who build a foundation for rapid prototyping and iteration not as a siloed R&D initiative, but as a fully integrated innovation engine within their current operations. This means empowering teams to experiment with different LLMs and build AI agents that connect to your existing applications, workflows, and data. In this way, the agentic enterprise is an organic, living ecosystem that evolves for the demands of the future by using and improving on what is already working.

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Land immediate agentic AI impact, then expand

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Companies are holding off on enterprise-wide AI deployment because they believe that making agentic AI a core part of business and IT is a major undertaking. But waiting has an opportunity cost, not just in terms of the inevitable AI sprawl, but also the risk of disruption by their competitors who use agentic AI to launch new products and services faster. Forward-thinking leaders are taking an iterative approach: identifying specific use cases where AI agents can make an immediate impact, prototyping and testing, then expanding from there.

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Another belief is that agentic AI demands we leave behind the applications and data already in place. In reality, your legacy systems, CRM, ERP, and custom applications are valuable assets that significantly increase the value of your AI agents. The technology already exists to harmonize historical and real-time data stored in different repositories and formats to be used by agents, while also making sure that the surface exposed to AI is limited to the context of a given agent interaction.

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Fears about runaway costs with agentic AI are also misplaced. The cost equation is straightforward when you approach it correctly. You track how many tokens each agent consumes, understand the cost per token based on your AI provider contracts, and measure that against the outcomes the agent delivers. It's similar to traditional resource management, just with a new type of resource.

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Use a unified platform for the full lifecycle of agentic AI

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It is early days in the agentic software revolution, and with the technology evolving at astonishing speed, many organizations are experiencing runaway agent sprawl. IT teams are having to determine how to manage a heterogeneous portfolio of agentic apps that includes custom-built apps and agents. While several AI vendors are offering agent orchestration layers, they are limited to those built with their products.

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What is needed is a platform that provides a unified view of all agents, their lineage, and their decisions. When five linked agents are handling their tasks, a change to the first one could have exponential impacts on the fifth. Or consider the situation where an agent depends on data from a specific system and then the data structure changes or evolves. It’s possible that, with such a brittle system, the change could break the input the agent expects. You need full visibility across agents, experiences and data, in a lifecycle that supports continuous change so that no agents or systems are broken. A unified platform can provide that view, while tracing the chain from end to end to make sure you always understand and improve the full process.

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The same principles that made low-code software development platforms a strategic foundation for agile enterprises apply to the agentic AI enterprise:

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The powerful combination of accelerated AI-driven development with guaranteed performance is what makes Agent Workbench stand out in a crowded field of AI app and agent generation tools.

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Support your transformation to an agentic enterprise with OutSystems

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At OutSystems, we’ve built Agent Workbench to simplify the path to becoming an agentic AI enterprise. Part of our unified approach to application and agent development, Agent Workbench makes it easy for any IT team to create, deploy, and manage AI agents that integrate with existing systems and deliver measurable business value.

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The OutSystems AI-powered low-code platform handles the complexity of agent lifecycle management, from development and testing to deployment and monitoring. Teams can create agents that connect to any data source, work with any AI model, and integrate into existing applications and workflows.

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Most importantly, it's designed to work with what you already have. Your data, your applications, your team's existing skills. You're not starting from scratch or learning entirely new development approaches.

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Your agentic AI future is here

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The agentic enterprise isn't a distant future concept. Nor is it about AI that can write an email for you. It's about empowering AI to solve problems so you don’t have to write emails to address or explain them. OutSystems has brought your data, your systems, your people, and AI together in a platform you trust.

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Join us as we walk together towards this future.

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Discover how innovative organizations are transforming with agentic AI in “Agents of Change,” and get started with Agent Workbench.

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When enterprise IT leaders first began to embrace low-code software development back in the mid-2000s, they measured the time savings in hours per developer. Today, they are measuring improvements in time to market, in business agility, and in customer experience. How much more could your organization do if you could get a decade’s worth of software to market in one week? In one day?

\n

Welcome to the “agentic AI enterprise,” where developers and other creators can build autonomous AI agents for every task and customer interaction, and agents actively participate in business processes, reason, and collaborate with humans. The agentic enterprise is an intelligent ecosystem that capitalizes on the strengths of people and machines, without the constraints on capacity that have historically limited innovation.

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If you think this is some kind of future state, think again. In a 2025 survey of 1000 U.S. business executives, one in five executives report using AI agents to automate customer support, streamline supply chain operations, and personalize customer experiences at scale.

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But this is just the beginning.

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\r\n \r\n \r\n\t \r\n \"nuno-borges\"\r\n \r\n\t \r\n \r\n
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Nuno Borges

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After ten years of hardcore development, Gon\u00e7alo got bored with the repetitive tasks that are required project after project. So, he adventured into finding the best way to bring automation and productivity to the rest of us.

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\r\n"}}},"data":{},"fields":{"blogpagecontenttitle":"Operationalizing Enterprise AI Strategy in 2026 | OutSystems","deferjsperpage":"Global","speedyenabled":"1","resourcecardtitle":"Operationalizing enterprise AI: Strategic investments for 2026","defercssandcriticalperpage":"Global","enableloadcss":"LoadCSS","resourcecarddescription":"Meet the \u201cagentic AI enterprise\u201d and find out how you can be one.","resourcehorizontalcardimage":{"vspace":"","hspace":"","width":"700","height":"280","class":"","mediaid":"f204b945-cdc0-42b4-830b-8bdf79a3bb51","alt":"hcard-bp-how-to-build-agentic-ai-enterprise","url":"/cw-static-assets-local/posts/operationalizing-enterprise-ai/hcard-bp-operationalizing-enterprise-ai.png?updated=20250929101409"},"body css class":"blog-category-page blog-category-page--article","changefrequency":"{D23B4654-53A5-4589-8B1B-5665A763D144}","image":{"vspace":"","hspace":"","width":"800","height":"800","class":"","mediaid":"41b54805-8c1c-4885-9554-fa3f9694d99f","alt":"hero-bp-operationalizing-enterprise-ai","url":"/cw-static-assets-local/posts/operationalizing-enterprise-ai/hero-bp-operationalizing-enterprise-ai.png?updated=20250929101414"},"opengraphtitle":"Operationalizing enterprise AI: Strategic investments for 2026","page design":"{4C44109E-CFA4-4C4B-9FB6-808DB58D174D}","postinfotitle":"Operationalizing enterprise AI: Strategic investments for 2026","related resource title":"Operationalizing enterprise AI: Strategic investments for 2026","resourceverticalcardimage":{"vspace":"","hspace":"","width":"740","height":"740","class":"","mediaid":"86121a9e-62a2-4387-8ff0-3746eed39123","alt":"vcard-bp-how-to-build-agentic-ai-enterprise","url":"/cw-static-assets-local/posts/operationalizing-enterprise-ai/vcard-bp-operationalizing-enterprise-ai.png?updated=20250929101424"},"sxatags":"{3BF49CC8-5480-46C0-B70F-81EA0C9E67E4}|{37B813A6-7AE2-4719-B0B1-F2E70B12D6AD}|{0B2ACAEC-AD3C-4511-B544-4A47160419A3}|{7BC44272-9E00-4926-8D98-1E78F867BDA5}|{D1A95AE9-12F1-45E7-8FB3-6A3FD34363EC}|{42379571-4400-4072-8649-4DCD3E9F3A67}|{FD532796-F481-4244-8F30-4D2FE5A909DE}|{8B9457F1-91CF-43C6-BEE8-909DEE1AF0C6}|{6663D446-4BB1-4373-B495-CE2F1A153EF6}|{26DD08B6-3DAC-4923-89AC-4AD2E126CEC4}|{9A6E3E5B-22B8-4F94-BE29-396DA7119CB9}","twittertitle":"Operationalizing enterprise AI: Strategic investments for 2026","category":"perspectives","horizontal card image":{"vspace":"","hspace":"","width":"1494","height":"836","class":"","mediaid":"4c15cb18-4af0-4f1d-a767-7f9dd3fbb970","alt":"card-bp-how-to-build-agentic-ai-enterprise","url":"/cw-static-assets-local/posts/operationalizing-enterprise-ai/card-bp-agentic-ai-strategies-that-work.png?updated=20250929101404"},"metadescription":"Welcome to the \u201cagentic AI enterprise,\u201d where autonomous AI agents are built for every task and interaction. 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Welcome to the \u201cagentic AI enterprise.\u201d","resourcehovercardcolor":"#000","author":"/blog/author/nuno-borges","twitterimage":{"vspace":"","hspace":"","width":"1200","height":"628","class":"","mediaid":"62c9865d-9d5a-4eac-a5e9-a3b5c32dd4cc","alt":"social-bp-how-to-build-agentic-ai-enterprise","url":"/cw-static-assets-local/posts/operationalizing-enterprise-ai/social-bp-operationalizing-enterprise-ai.png?updated=20250929101418"},"personalizationproductsku":"blog-posts-agentic-ai-strategies-that-work-abe5560b90","time to read":"7","related posts":"{30267BE9-4C24-47F3-9EA0-9EEEF41B6E92}|{E0F36716-2CB0-4D8F-8474-ECA9FAA64ED4}|{54AAAACC-FE8F-4512-A1F1-AC37CC9297EC}","date":"2026-03-31T09:24:00Z","resourcecardactionlabel":"Read article","twittercardtype":"{32F6A4E4-93DD-4A9C-AA60-CDEDB0BEDABB}","card description":"Meet the \u201cagentic AI enterprise\u201d and find out how you can be one.","resourcecardactionstyle":"Link Blue CTA Internal","resourcecardactionsize":"Small","resourcecardcategory":"{1DC12CBF-5500-4FF1-BA04-E9EC1722E208}","resourcedate":"2025-09-29T00:00:00Z","resourceshowtags":"1","resourcecardcontentpage":" Table of contents: Agentic AI is moving quickly Navigating the journey to the agentic AI enterprise Land immediate agentic AI impact, then expand Use a unified platform for the full lifecycle of agentic AI Support your transformation to an agentic enterprise with OutSystems Your agentic AI future is here Table of contents: Agentic AI is moving quickly Navigating the journey to the agentic AI enterprise Land immediate agentic AI impact, then expand Use a unified platform for the full lifecycle of agentic AI Support your transformation to an agentic enterprise with OutSystems Your agentic AI future is here Table of contents: Agentic AI is moving quickly Navigating the journey to the agentic AI enterprise Land immediate agentic AI impact, then expand Use a unified platform for the full lifecycle of agentic AI Support your transformation to an agentic enterprise with OutSystems Your agentic AI future is here When enterprise IT leaders first began to embrace low-code software development \u00a0back in the mid-2000s, they measured the time savings in hours per developer. Today, they are measuring improvements in time to market, in business agility, and in customer experience. How much more could your organization do if you could get a decade\u2019s worth of software to market in one week? In one day? Welcome to the \u201cagentic AI enterprise,\u201d where developers and other creators can build autonomous AI agents for every task and customer interaction, and agents actively participate in business processes, reason, and collaborate with humans. The agentic enterprise is an intelligent ecosystem that capitalizes on the strengths of people and machines, without the constraints on capacity that have historically limited innovation. If you think this is some kind of future state, think again. In a 2025 survey of 1000 U.S. business executives , one in five executives report using AI agents to automate customer support, streamline supply chain operations, and personalize customer experiences at scale. But this is just the beginning. Agentic AI is moving quickly The conversation around agentic AI is moving quickly from \u201chow would we use agents?\u201d to \u201cwhat couldn\u2019t we do with agents?\u201d Take TravelEssence , for example. After experimenting with agentic AI and implementing two agents, the company realized that agents can address more use cases than they first thought. Now it has adopted OutSystems Agent Workbench \u00a0and is creating a \u201cswarm of AI agents\u201d to accelerate the delivery of tailored travel recommendations for its clients. What does this mean for IT leaders who now must manage a proliferation of autonomous workers and dynamic agentic processes in their portfolio? Remember, agents are software too, with all the same potential for errors, security gaps , and governance concerns (but exponentially magnified). As with any accelerated and automated process, from traditional manufacturing to smart factories or human-led customer service to virtual agents, for example, we have new and far-reaching responsibilities to manage the full lifecycle of agentic apps and systems. Here are some strategies we are offering our customers to plan a successful and sustainable agentic transformation. Navigating the journey to the agentic AI enterprise In an agentic AI enterprise, AI removes obstacles that slow human teams down. When grunt work, context switching, disparate communications, and unnecessary complexity are shifted to agents, everyone has more time to ideate, iterate, and innovate. This creates a positive cycle where more efficient processes and better customer and employee experiences lead to new insights, creating opportunities for even more innovation. Simply adopting agentic AI \u00a0is not a straight path to becoming an agentic enterprise, however. Today, most organizations are adding agents to an already fragmented landscape of apps and data. Many everyday productivity apps and SaaS platforms include AI agents \u00a0now and some invite nontechnical employees to build their own. Marketing builds chatbots on one platform, operations uses a different framework for process automation, and customer service deploys agents through yet another vendor. Soon you have a collection of isolated AI experiments that can't share data, can't be governed consistently, and create more problems than they solve. IT leaders should resist the temptation to restrict agentic AI experimentation. Instead, they should establish a system for full portfolio visibility and governance that works with this reality and assumes that it will continue to evolve in unpredictable ways. The goal should be to design agentic systems where humans and AI complement each other, with human-on-the-loop flows that give people final control. AI handles the heavy lifting on data processing and routine decisions. Humans provide additional strategic insight on patterns that AI has not encountered and oversight for complex or sensitive situations. Ultimately, the heroes of the agentic AI enterprise will be the strategic leaders who build a foundation for rapid prototyping and iteration not as a siloed R&D initiative, but as a fully integrated innovation engine within their current operations. This means empowering teams to experiment with different LLMs and build agents that connect to your existing applications, workflows, and data. In this way, the agentic enterprise is an organic, living ecosystem that evolves for the demands of the future by using and improving on what is already working. Land immediate agentic AI impact, then expand Companies are holding off on enterprise-wide AI deployment because they believe that making agentic AI \u00a0a core part of business and IT is a major undertaking. But waiting has an opportunity cost, not just in terms of the inevitable AI sprawl, but also the risk of disruption by their competitors who use agentic AI to launch new products and services faster. Forward-thinking leaders are taking an iterative approach: identifying specific use cases where AI agents can make an immediate impact, prototyping and testing, then expanding from there. Another belief is that agentic AI demands we leave behind the applications and data already in place. In reality, your legacy systems, CRM, ERP, and custom applications are valuable assets that significantly increase the value of your AI agents. The technology already exists to harmonize historical and real-time data stored in different repositories and formats to be used by agents, while also making sure that the surface exposed to AI is limited to the context of a given agent interaction. Fears about runaway costs with agentic AI are also misplaced. The cost equation is straightforward when you approach it correctly. You track how many tokens each agent consumes, understand the cost per token based on your AI provider contracts, and measure that against the outcomes the agent delivers. It's similar to traditional resource management, just with a new type of resource. Use a unified platform for the full lifecycle of agentic AI It is early days in the agentic software revolution, and with the technology evolving at astonishing speed, many organizations are experiencing runaway agent sprawl. IT teams are having to determine how to manage a heterogeneous portfolio of agentic apps that includes custom-built apps and agents. While several AI vendors are offering agent orchestration layers, they are limited to those built with their products. What is needed is a platform that provides a unified view of all agents, their lineage, and their decisions. When five linked agents are handling their tasks, a change to the first one could have exponential impacts on the fifth. Or consider the situation where an agent depends on data from a specific system and then the data structure changes or evolves. It\u2019s possible that, with such a brittle system, the change could break the input the agent expects. You need full visibility across agents, experiences and data, in a lifecycle that supports continuous change so that no agents or systems are broken. A unified platform can provide that view, while tracing the chain from end to end to make sure you always understand and improve the full process. The same principles that made low-code software development platforms a strategic foundation for agile enterprises apply to the agentic AI enterprise: Visual, model-driven tools that simplify and speed up the creation phase Modular components that can be reused and updated Capabilities that fully trace and monitor performance Workflows that enable accountability Tools for filtering, restricting, and securing data The powerful combination of accelerated AI-driven development with guaranteed performance is what makes Agent Workbench \u00a0stand out in a crowded field of AI app and agent generation tools. Support your transformation to an agentic enterprise with OutSystems At OutSystems, we\u2019ve built Agent Workbench to simplify the path to becoming an agentic AI enterprise. Part of our unified approach to application and agent development , Agent Workbench makes it easy for any IT team to create, deploy, and manage AI agents that integrate with existing systems and deliver measurable business value. The OutSystems AI-powered low-code platform handles the complexity of agent lifecycle management, from development and testing to deployment and monitoring. Teams can create agents that connect to any data source, work with any AI model, and integrate into existing applications and workflows. Most importantly, it's designed to work with what you already have. Your data, your applications, your team's existing skills. You're not starting from scratch or learning entirely new development approaches. Your agentic AI future is here The agentic enterprise isn't a distant future concept. Nor is it about AI that can write an email for you. It's about empowering AI to solve problems so you don\u2019t have to write emails to address or explain them. OutSystems has brought your data, your systems, your people, and AI together in a platform you trust. Join us as we walk together towards this future. Discover how innovative organizations are transforming with agentic AI in \u201c Agents of Change ,\u201d and get started with Agent Workbench . 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Operationalizing enterprise AI: Strategic investments for 2026

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Nuno Borges\r\n\tSeptember 30, 2025 • 7 min read\r\n

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Agent Workbench is here: Agentic AI for enterprise innovation
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AI agent orchestration for true enterprise transformation
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Agentic AI is moving quickly

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The conversation around agentic AI is moving quickly from “how would we use agents?” to “what couldn’t we do with agents?” Take TravelEssence, for example. After experimenting with agentic AI and implementing two agents, the company realized that agents can address more use cases than they first thought. Now it has adopted OutSystems Agent Workbench and is creating a “swarm of AI agents” to accelerate the delivery of tailored travel recommendations for its clients.

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What does this mean for IT leaders who now must manage a proliferation of autonomous workers and dynamic agentic processes in their portfolio? Remember, agents are software too, with all the same potential for errors, security gaps, and governance concerns (but exponentially magnified). As with any accelerated and automated process, from traditional manufacturing to smart factories or human-led customer service to virtual agents, for example, we have new and far-reaching responsibilities to manage the full lifecycle of agentic apps and systems.

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Here are some strategies we are offering our customers to plan a successful and sustainable agentic transformation.

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Navigating the journey to the agentic AI enterprise

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In an agentic AI enterprise, AI removes obstacles that slow human teams down. When grunt work, context switching, disparate communications, and unnecessary complexity are shifted to agents, everyone has more time to ideate, iterate, and innovate. This creates a positive cycle where more efficient processes and better customer and employee experiences lead to new insights, creating opportunities for even more innovation.

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Simply adopting agentic AI is not a straight path to becoming an agentic enterprise, however. Today, most organizations are adding agents to an already fragmented landscape of apps and data. Many everyday productivity apps and SaaS platforms include AI agents now and some invite nontechnical employees to build their own. Marketing builds chatbots on one platform, operations uses a different framework for process automation, and customer service deploys agents through yet another vendor. Soon you have a collection of isolated AI experiments that can't share data, can't be governed consistently, and create more problems than they solve.

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IT leaders should resist the temptation to restrict agentic AI experimentation. Instead, they should establish a system for full portfolio visibility and governance that works with this reality and assumes that it will continue to evolve in unpredictable ways. The goal should be to design agentic systems where humans and AI complement each other, with human-on-the-loop flows that give people final control. AI handles the heavy lifting on data processing and routine decisions. Humans provide additional strategic insight on patterns that AI has not encountered and oversight for complex or sensitive situations.

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Ultimately, the heroes of the agentic AI enterprise will be the strategic leaders who build a foundation for rapid prototyping and iteration not as a siloed R&D initiative, but as a fully integrated innovation engine within their current operations. This means empowering teams to experiment with different LLMs and build AI agents that connect to your existing applications, workflows, and data. In this way, the agentic enterprise is an organic, living ecosystem that evolves for the demands of the future by using and improving on what is already working.

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Land immediate agentic AI impact, then expand

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Companies are holding off on enterprise-wide AI deployment because they believe that making agentic AI a core part of business and IT is a major undertaking. But waiting has an opportunity cost, not just in terms of the inevitable AI sprawl, but also the risk of disruption by their competitors who use agentic AI to launch new products and services faster. Forward-thinking leaders are taking an iterative approach: identifying specific use cases where AI agents can make an immediate impact, prototyping and testing, then expanding from there.

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Another belief is that agentic AI demands we leave behind the applications and data already in place. In reality, your legacy systems, CRM, ERP, and custom applications are valuable assets that significantly increase the value of your AI agents. The technology already exists to harmonize historical and real-time data stored in different repositories and formats to be used by agents, while also making sure that the surface exposed to AI is limited to the context of a given agent interaction.

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Fears about runaway costs with agentic AI are also misplaced. The cost equation is straightforward when you approach it correctly. You track how many tokens each agent consumes, understand the cost per token based on your AI provider contracts, and measure that against the outcomes the agent delivers. It's similar to traditional resource management, just with a new type of resource.

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Use a unified platform for the full lifecycle of agentic AI

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It is early days in the agentic software revolution, and with the technology evolving at astonishing speed, many organizations are experiencing runaway agent sprawl. IT teams are having to determine how to manage a heterogeneous portfolio of agentic apps that includes custom-built apps and agents. While several AI vendors are offering agent orchestration layers, they are limited to those built with their products.

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What is needed is a platform that provides a unified view of all agents, their lineage, and their decisions. When five linked agents are handling their tasks, a change to the first one could have exponential impacts on the fifth. Or consider the situation where an agent depends on data from a specific system and then the data structure changes or evolves. It’s possible that, with such a brittle system, the change could break the input the agent expects. You need full visibility across agents, experiences and data, in a lifecycle that supports continuous change so that no agents or systems are broken. A unified platform can provide that view, while tracing the chain from end to end to make sure you always understand and improve the full process.

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The same principles that made low-code software development platforms a strategic foundation for agile enterprises apply to the agentic AI enterprise:

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The powerful combination of accelerated AI-driven development with guaranteed performance is what makes Agent Workbench stand out in a crowded field of AI app and agent generation tools.

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Support your transformation to an agentic enterprise with OutSystems

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At OutSystems, we’ve built Agent Workbench to simplify the path to becoming an agentic AI enterprise. Part of our unified approach to application and agent development, Agent Workbench makes it easy for any IT team to create, deploy, and manage AI agents that integrate with existing systems and deliver measurable business value.

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The OutSystems AI-powered low-code platform handles the complexity of agent lifecycle management, from development and testing to deployment and monitoring. Teams can create agents that connect to any data source, work with any AI model, and integrate into existing applications and workflows.

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Most importantly, it's designed to work with what you already have. Your data, your applications, your team's existing skills. You're not starting from scratch or learning entirely new development approaches.

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Your agentic AI future is here

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The agentic enterprise isn't a distant future concept. Nor is it about AI that can write an email for you. It's about empowering AI to solve problems so you don’t have to write emails to address or explain them. OutSystems has brought your data, your systems, your people, and AI together in a platform you trust.

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Join us as we walk together towards this future.

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Discover how innovative organizations are transforming with agentic AI in “Agents of Change,” and get started with Agent Workbench.

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When enterprise IT leaders first began to embrace low-code software development back in the mid-2000s, they measured the time savings in hours per developer. Today, they are measuring improvements in time to market, in business agility, and in customer experience. How much more could your organization do if you could get a decade’s worth of software to market in one week? In one day?

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Welcome to the “agentic AI enterprise,” where developers and other creators can build autonomous AI agents for every task and customer interaction, and agents actively participate in business processes, reason, and collaborate with humans. The agentic enterprise is an intelligent ecosystem that capitalizes on the strengths of people and machines, without the constraints on capacity that have historically limited innovation.

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If you think this is some kind of future state, think again. In a 2025 survey of 1000 U.S. business executives, one in five executives report using AI agents to automate customer support, streamline supply chain operations, and personalize customer experiences at scale.

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But this is just the beginning.

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Nuno Borges

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After ten years of hardcore development, Gon\u00e7alo got bored with the repetitive tasks that are required project after project. So, he adventured into finding the best way to bring automation and productivity to the rest of us.

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Today, they are measuring improvements in time to market, in business agility, and in customer experience. How much more could your organization do if you could get a decade\u2019s worth of software to market in one week? In one day? Welcome to the \u201cagentic AI enterprise,\u201d where developers and other creators can build autonomous AI agents for every task and customer interaction, and agents actively participate in business processes, reason, and collaborate with humans. The agentic enterprise is an intelligent ecosystem that capitalizes on the strengths of people and machines, without the constraints on capacity that have historically limited innovation. If you think this is some kind of future state, think again. In a 2025 survey of 1000 U.S. business executives , one in five executives report using AI agents to automate customer support, streamline supply chain operations, and personalize customer experiences at scale. But this is just the beginning. Agentic AI is moving quickly The conversation around agentic AI is moving quickly from \u201chow would we use agents?\u201d to \u201cwhat couldn\u2019t we do with agents?\u201d Take TravelEssence , for example. After experimenting with agentic AI and implementing two agents, the company realized that agents can address more use cases than they first thought. Now it has adopted OutSystems Agent Workbench \u00a0and is creating a \u201cswarm of AI agents\u201d to accelerate the delivery of tailored travel recommendations for its clients. What does this mean for IT leaders who now must manage a proliferation of autonomous workers and dynamic agentic processes in their portfolio? Remember, agents are software too, with all the same potential for errors, security gaps , and governance concerns (but exponentially magnified). As with any accelerated and automated process, from traditional manufacturing to smart factories or human-led customer service to virtual agents, for example, we have new and far-reaching responsibilities to manage the full lifecycle of agentic apps and systems. Here are some strategies we are offering our customers to plan a successful and sustainable agentic transformation. Navigating the journey to the agentic AI enterprise In an agentic AI enterprise, AI removes obstacles that slow human teams down. When grunt work, context switching, disparate communications, and unnecessary complexity are shifted to agents, everyone has more time to ideate, iterate, and innovate. This creates a positive cycle where more efficient processes and better customer and employee experiences lead to new insights, creating opportunities for even more innovation. Simply adopting agentic AI \u00a0is not a straight path to becoming an agentic enterprise, however. Today, most organizations are adding agents to an already fragmented landscape of apps and data. Many everyday productivity apps and SaaS platforms include AI agents \u00a0now and some invite nontechnical employees to build their own. Marketing builds chatbots on one platform, operations uses a different framework for process automation, and customer service deploys agents through yet another vendor. Soon you have a collection of isolated AI experiments that can't share data, can't be governed consistently, and create more problems than they solve. IT leaders should resist the temptation to restrict agentic AI experimentation. Instead, they should establish a system for full portfolio visibility and governance that works with this reality and assumes that it will continue to evolve in unpredictable ways. The goal should be to design agentic systems where humans and AI complement each other, with human-on-the-loop flows that give people final control. AI handles the heavy lifting on data processing and routine decisions. Humans provide additional strategic insight on patterns that AI has not encountered and oversight for complex or sensitive situations. Ultimately, the heroes of the agentic AI enterprise will be the strategic leaders who build a foundation for rapid prototyping and iteration not as a siloed R&D initiative, but as a fully integrated innovation engine within their current operations. This means empowering teams to experiment with different LLMs and build agents that connect to your existing applications, workflows, and data. In this way, the agentic enterprise is an organic, living ecosystem that evolves for the demands of the future by using and improving on what is already working. Land immediate agentic AI impact, then expand Companies are holding off on enterprise-wide AI deployment because they believe that making agentic AI \u00a0a core part of business and IT is a major undertaking. But waiting has an opportunity cost, not just in terms of the inevitable AI sprawl, but also the risk of disruption by their competitors who use agentic AI to launch new products and services faster. Forward-thinking leaders are taking an iterative approach: identifying specific use cases where AI agents can make an immediate impact, prototyping and testing, then expanding from there. Another belief is that agentic AI demands we leave behind the applications and data already in place. In reality, your legacy systems, CRM, ERP, and custom applications are valuable assets that significantly increase the value of your AI agents. The technology already exists to harmonize historical and real-time data stored in different repositories and formats to be used by agents, while also making sure that the surface exposed to AI is limited to the context of a given agent interaction. Fears about runaway costs with agentic AI are also misplaced. The cost equation is straightforward when you approach it correctly. You track how many tokens each agent consumes, understand the cost per token based on your AI provider contracts, and measure that against the outcomes the agent delivers. It's similar to traditional resource management, just with a new type of resource. Use a unified platform for the full lifecycle of agentic AI It is early days in the agentic software revolution, and with the technology evolving at astonishing speed, many organizations are experiencing runaway agent sprawl. IT teams are having to determine how to manage a heterogeneous portfolio of agentic apps that includes custom-built apps and agents. While several AI vendors are offering agent orchestration layers, they are limited to those built with their products. What is needed is a platform that provides a unified view of all agents, their lineage, and their decisions. When five linked agents are handling their tasks, a change to the first one could have exponential impacts on the fifth. Or consider the situation where an agent depends on data from a specific system and then the data structure changes or evolves. It\u2019s possible that, with such a brittle system, the change could break the input the agent expects. You need full visibility across agents, experiences and data, in a lifecycle that supports continuous change so that no agents or systems are broken. A unified platform can provide that view, while tracing the chain from end to end to make sure you always understand and improve the full process. The same principles that made low-code software development platforms a strategic foundation for agile enterprises apply to the agentic AI enterprise: Visual, model-driven tools that simplify and speed up the creation phase Modular components that can be reused and updated Capabilities that fully trace and monitor performance Workflows that enable accountability Tools for filtering, restricting, and securing data The powerful combination of accelerated AI-driven development with guaranteed performance is what makes Agent Workbench \u00a0stand out in a crowded field of AI app and agent generation tools. Support your transformation to an agentic enterprise with OutSystems At OutSystems, we\u2019ve built Agent Workbench to simplify the path to becoming an agentic AI enterprise. Part of our unified approach to application and agent development , Agent Workbench makes it easy for any IT team to create, deploy, and manage AI agents that integrate with existing systems and deliver measurable business value. The OutSystems AI-powered low-code platform handles the complexity of agent lifecycle management, from development and testing to deployment and monitoring. Teams can create agents that connect to any data source, work with any AI model, and integrate into existing applications and workflows. Most importantly, it's designed to work with what you already have. Your data, your applications, your team's existing skills. You're not starting from scratch or learning entirely new development approaches. Your agentic AI future is here The agentic enterprise isn't a distant future concept. Nor is it about AI that can write an email for you. It's about empowering AI to solve problems so you don\u2019t have to write emails to address or explain them. OutSystems has brought your data, your systems, your people, and AI together in a platform you trust. Join us as we walk together towards this future. Discover how innovative organizations are transforming with agentic AI in \u201c Agents of Change ,\u201d and get started with Agent Workbench . 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Operationalizing enterprise AI: Strategic investments for 2026

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Nuno Borges\r\n\tSeptember 30, 2025 • 7 min read\r\n

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Agent Workbench is here: Agentic AI for enterprise innovation
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AI agent orchestration for true enterprise transformation
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Agentic AI is moving quickly

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The conversation around agentic AI is moving quickly from “how would we use agents?” to “what couldn’t we do with agents?” Take TravelEssence, for example. After experimenting with agentic AI and implementing two agents, the company realized that agents can address more use cases than they first thought. Now it has adopted OutSystems Agent Workbench and is creating a “swarm of AI agents” to accelerate the delivery of tailored travel recommendations for its clients.

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What does this mean for IT leaders who now must manage a proliferation of autonomous workers and dynamic agentic processes in their portfolio? Remember, agents are software too, with all the same potential for errors, security gaps, and governance concerns (but exponentially magnified). As with any accelerated and automated process, from traditional manufacturing to smart factories or human-led customer service to virtual agents, for example, we have new and far-reaching responsibilities to manage the full lifecycle of agentic apps and systems.

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Here are some strategies we are offering our customers to plan a successful and sustainable agentic transformation.

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Navigating the journey to the agentic AI enterprise

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In an agentic AI enterprise, AI removes obstacles that slow human teams down. When grunt work, context switching, disparate communications, and unnecessary complexity are shifted to agents, everyone has more time to ideate, iterate, and innovate. This creates a positive cycle where more efficient processes and better customer and employee experiences lead to new insights, creating opportunities for even more innovation.

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Simply adopting agentic AI is not a straight path to becoming an agentic enterprise, however. Today, most organizations are adding agents to an already fragmented landscape of apps and data. Many everyday productivity apps and SaaS platforms include AI agents now and some invite nontechnical employees to build their own. Marketing builds chatbots on one platform, operations uses a different framework for process automation, and customer service deploys agents through yet another vendor. Soon you have a collection of isolated AI experiments that can't share data, can't be governed consistently, and create more problems than they solve.

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IT leaders should resist the temptation to restrict agentic AI experimentation. Instead, they should establish a system for full portfolio visibility and governance that works with this reality and assumes that it will continue to evolve in unpredictable ways. The goal should be to design agentic systems where humans and AI complement each other, with human-on-the-loop flows that give people final control. AI handles the heavy lifting on data processing and routine decisions. Humans provide additional strategic insight on patterns that AI has not encountered and oversight for complex or sensitive situations.

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Ultimately, the heroes of the agentic AI enterprise will be the strategic leaders who build a foundation for rapid prototyping and iteration not as a siloed R&D initiative, but as a fully integrated innovation engine within their current operations. This means empowering teams to experiment with different LLMs and build AI agents that connect to your existing applications, workflows, and data. In this way, the agentic enterprise is an organic, living ecosystem that evolves for the demands of the future by using and improving on what is already working.

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Land immediate agentic AI impact, then expand

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Companies are holding off on enterprise-wide AI deployment because they believe that making agentic AI a core part of business and IT is a major undertaking. But waiting has an opportunity cost, not just in terms of the inevitable AI sprawl, but also the risk of disruption by their competitors who use agentic AI to launch new products and services faster. Forward-thinking leaders are taking an iterative approach: identifying specific use cases where AI agents can make an immediate impact, prototyping and testing, then expanding from there.

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Another belief is that agentic AI demands we leave behind the applications and data already in place. In reality, your legacy systems, CRM, ERP, and custom applications are valuable assets that significantly increase the value of your AI agents. The technology already exists to harmonize historical and real-time data stored in different repositories and formats to be used by agents, while also making sure that the surface exposed to AI is limited to the context of a given agent interaction.

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Fears about runaway costs with agentic AI are also misplaced. The cost equation is straightforward when you approach it correctly. You track how many tokens each agent consumes, understand the cost per token based on your AI provider contracts, and measure that against the outcomes the agent delivers. It's similar to traditional resource management, just with a new type of resource.

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Use a unified platform for the full lifecycle of agentic AI

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It is early days in the agentic software revolution, and with the technology evolving at astonishing speed, many organizations are experiencing runaway agent sprawl. IT teams are having to determine how to manage a heterogeneous portfolio of agentic apps that includes custom-built apps and agents. While several AI vendors are offering agent orchestration layers, they are limited to those built with their products.

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What is needed is a platform that provides a unified view of all agents, their lineage, and their decisions. When five linked agents are handling their tasks, a change to the first one could have exponential impacts on the fifth. Or consider the situation where an agent depends on data from a specific system and then the data structure changes or evolves. It’s possible that, with such a brittle system, the change could break the input the agent expects. You need full visibility across agents, experiences and data, in a lifecycle that supports continuous change so that no agents or systems are broken. A unified platform can provide that view, while tracing the chain from end to end to make sure you always understand and improve the full process.

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The same principles that made low-code software development platforms a strategic foundation for agile enterprises apply to the agentic AI enterprise:

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The powerful combination of accelerated AI-driven development with guaranteed performance is what makes Agent Workbench stand out in a crowded field of AI app and agent generation tools.

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Support your transformation to an agentic enterprise with OutSystems

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At OutSystems, we’ve built Agent Workbench to simplify the path to becoming an agentic AI enterprise. Part of our unified approach to application and agent development, Agent Workbench makes it easy for any IT team to create, deploy, and manage AI agents that integrate with existing systems and deliver measurable business value.

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The OutSystems AI-powered low-code platform handles the complexity of agent lifecycle management, from development and testing to deployment and monitoring. Teams can create agents that connect to any data source, work with any AI model, and integrate into existing applications and workflows.

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Most importantly, it's designed to work with what you already have. Your data, your applications, your team's existing skills. You're not starting from scratch or learning entirely new development approaches.

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Your agentic AI future is here

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The agentic enterprise isn't a distant future concept. Nor is it about AI that can write an email for you. It's about empowering AI to solve problems so you don’t have to write emails to address or explain them. OutSystems has brought your data, your systems, your people, and AI together in a platform you trust.

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Join us as we walk together towards this future.

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Discover how innovative organizations are transforming with agentic AI in “Agents of Change,” and get started with Agent Workbench.

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When enterprise IT leaders first began to embrace low-code software development back in the mid-2000s, they measured the time savings in hours per developer. Today, they are measuring improvements in time to market, in business agility, and in customer experience. How much more could your organization do if you could get a decade’s worth of software to market in one week? In one day?

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Welcome to the “agentic AI enterprise,” where developers and other creators can build autonomous AI agents for every task and customer interaction, and agents actively participate in business processes, reason, and collaborate with humans. The agentic enterprise is an intelligent ecosystem that capitalizes on the strengths of people and machines, without the constraints on capacity that have historically limited innovation.

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If you think this is some kind of future state, think again. In a 2025 survey of 1000 U.S. business executives, one in five executives report using AI agents to automate customer support, streamline supply chain operations, and personalize customer experiences at scale.

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But this is just the beginning.

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Nuno Borges

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After ten years of hardcore development, Gon\u00e7alo got bored with the repetitive tasks that are required project after project. So, he adventured into finding the best way to bring automation and productivity to the rest of us.

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Today, they are measuring improvements in time to market, in business agility, and in customer experience. How much more could your organization do if you could get a decade\u2019s worth of software to market in one week? In one day? Welcome to the \u201cagentic AI enterprise,\u201d where developers and other creators can build autonomous AI agents for every task and customer interaction, and agents actively participate in business processes, reason, and collaborate with humans. The agentic enterprise is an intelligent ecosystem that capitalizes on the strengths of people and machines, without the constraints on capacity that have historically limited innovation. If you think this is some kind of future state, think again. In a 2025 survey of 1000 U.S. business executives , one in five executives report using AI agents to automate customer support, streamline supply chain operations, and personalize customer experiences at scale. But this is just the beginning. Agentic AI is moving quickly The conversation around agentic AI is moving quickly from \u201chow would we use agents?\u201d to \u201cwhat couldn\u2019t we do with agents?\u201d Take TravelEssence , for example. After experimenting with agentic AI and implementing two agents, the company realized that agents can address more use cases than they first thought. Now it has adopted OutSystems Agent Workbench \u00a0and is creating a \u201cswarm of AI agents\u201d to accelerate the delivery of tailored travel recommendations for its clients. What does this mean for IT leaders who now must manage a proliferation of autonomous workers and dynamic agentic processes in their portfolio? Remember, agents are software too, with all the same potential for errors, security gaps , and governance concerns (but exponentially magnified). As with any accelerated and automated process, from traditional manufacturing to smart factories or human-led customer service to virtual agents, for example, we have new and far-reaching responsibilities to manage the full lifecycle of agentic apps and systems. Here are some strategies we are offering our customers to plan a successful and sustainable agentic transformation. Navigating the journey to the agentic AI enterprise In an agentic AI enterprise, AI removes obstacles that slow human teams down. When grunt work, context switching, disparate communications, and unnecessary complexity are shifted to agents, everyone has more time to ideate, iterate, and innovate. This creates a positive cycle where more efficient processes and better customer and employee experiences lead to new insights, creating opportunities for even more innovation. Simply adopting agentic AI \u00a0is not a straight path to becoming an agentic enterprise, however. Today, most organizations are adding agents to an already fragmented landscape of apps and data. Many everyday productivity apps and SaaS platforms include AI agents \u00a0now and some invite nontechnical employees to build their own. Marketing builds chatbots on one platform, operations uses a different framework for process automation, and customer service deploys agents through yet another vendor. Soon you have a collection of isolated AI experiments that can't share data, can't be governed consistently, and create more problems than they solve. IT leaders should resist the temptation to restrict agentic AI experimentation. Instead, they should establish a system for full portfolio visibility and governance that works with this reality and assumes that it will continue to evolve in unpredictable ways. The goal should be to design agentic systems where humans and AI complement each other, with human-on-the-loop flows that give people final control. AI handles the heavy lifting on data processing and routine decisions. Humans provide additional strategic insight on patterns that AI has not encountered and oversight for complex or sensitive situations. Ultimately, the heroes of the agentic AI enterprise will be the strategic leaders who build a foundation for rapid prototyping and iteration not as a siloed R&D initiative, but as a fully integrated innovation engine within their current operations. This means empowering teams to experiment with different LLMs and build agents that connect to your existing applications, workflows, and data. In this way, the agentic enterprise is an organic, living ecosystem that evolves for the demands of the future by using and improving on what is already working. Land immediate agentic AI impact, then expand Companies are holding off on enterprise-wide AI deployment because they believe that making agentic AI \u00a0a core part of business and IT is a major undertaking. But waiting has an opportunity cost, not just in terms of the inevitable AI sprawl, but also the risk of disruption by their competitors who use agentic AI to launch new products and services faster. Forward-thinking leaders are taking an iterative approach: identifying specific use cases where AI agents can make an immediate impact, prototyping and testing, then expanding from there. Another belief is that agentic AI demands we leave behind the applications and data already in place. In reality, your legacy systems, CRM, ERP, and custom applications are valuable assets that significantly increase the value of your AI agents. The technology already exists to harmonize historical and real-time data stored in different repositories and formats to be used by agents, while also making sure that the surface exposed to AI is limited to the context of a given agent interaction. Fears about runaway costs with agentic AI are also misplaced. The cost equation is straightforward when you approach it correctly. You track how many tokens each agent consumes, understand the cost per token based on your AI provider contracts, and measure that against the outcomes the agent delivers. It's similar to traditional resource management, just with a new type of resource. Use a unified platform for the full lifecycle of agentic AI It is early days in the agentic software revolution, and with the technology evolving at astonishing speed, many organizations are experiencing runaway agent sprawl. IT teams are having to determine how to manage a heterogeneous portfolio of agentic apps that includes custom-built apps and agents. While several AI vendors are offering agent orchestration layers, they are limited to those built with their products. What is needed is a platform that provides a unified view of all agents, their lineage, and their decisions. When five linked agents are handling their tasks, a change to the first one could have exponential impacts on the fifth. Or consider the situation where an agent depends on data from a specific system and then the data structure changes or evolves. It\u2019s possible that, with such a brittle system, the change could break the input the agent expects. You need full visibility across agents, experiences and data, in a lifecycle that supports continuous change so that no agents or systems are broken. A unified platform can provide that view, while tracing the chain from end to end to make sure you always understand and improve the full process. The same principles that made low-code software development platforms a strategic foundation for agile enterprises apply to the agentic AI enterprise: Visual, model-driven tools that simplify and speed up the creation phase Modular components that can be reused and updated Capabilities that fully trace and monitor performance Workflows that enable accountability Tools for filtering, restricting, and securing data The powerful combination of accelerated AI-driven development with guaranteed performance is what makes Agent Workbench \u00a0stand out in a crowded field of AI app and agent generation tools. Support your transformation to an agentic enterprise with OutSystems At OutSystems, we\u2019ve built Agent Workbench to simplify the path to becoming an agentic AI enterprise. Part of our unified approach to application and agent development , Agent Workbench makes it easy for any IT team to create, deploy, and manage AI agents that integrate with existing systems and deliver measurable business value. The OutSystems AI-powered low-code platform handles the complexity of agent lifecycle management, from development and testing to deployment and monitoring. Teams can create agents that connect to any data source, work with any AI model, and integrate into existing applications and workflows. Most importantly, it's designed to work with what you already have. Your data, your applications, your team's existing skills. You're not starting from scratch or learning entirely new development approaches. Your agentic AI future is here The agentic enterprise isn't a distant future concept. Nor is it about AI that can write an email for you. It's about empowering AI to solve problems so you don\u2019t have to write emails to address or explain them. OutSystems has brought your data, your systems, your people, and AI together in a platform you trust. Join us as we walk together towards this future. Discover how innovative organizations are transforming with agentic AI in \u201c Agents of Change ,\u201d and get started with Agent Workbench . 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