Editorial
In 2006, Jeanne Ross, Peter Weill and David Robertson introduced the notion of enterprise architecture as a strategic enabler in their seminal book, “Enterprise Architecture as Strategy.” They argued that well-integrated organizations execute better because they have a solid foundation to support their operations. By embedding technology into their core processes, these companies achieve greater efficiency and reliability, enabling them to outperform competitors.
However, despite the passage of nearly two decades since this insight, many organizations remain stuck in what Ross’ colleagues later described in “Future Ready” as "Silos and Spaghetti" — fragmented systems and processes that hinder agility and innovation.
The MIT-CISR research that was the foundation of this latter book found that 51% of organizations are still trapped in this state. On the other hand, companies that successfully industrialize their integrations and fix customer experience achieve business advantages: revenue growth 17.3% higher and margins 14% greater than their industry peers. These "Future Ready" organizations prove the financial and operational benefits of modernizing and aligning their enterprise architecture.
Table of Contents
- Questions Around GenAI Still Remain
- How AI Addresses Integration Challenges
- Where GenAI Cuts Costs and Complexity
- Changing the Corporate Operating Model With AI
- The Business Impact of AI-Enhanced Integration Processes
- Navigating the AI Landscape: Integration and Beyond
- Parting Words: Harnessing AI for Next-Level Integration
Questions Around GenAI Still Remain
As we enter the era of generative AI, the stakes and opportunities have only grown. GenAI introduces new transformative potential, enabling organizations to accelerate their journey toward industrialization by automating processes, improving decision-making and creating innovative customer experiences.
Yet, the questions remain: how can organizations leverage GenAI to drive transformation, and what foundational steps are necessary to ensure its successful adoption?
These are the questions I posed to Rich Waldron, co-founder and CEO of Tray.ai, a company at the forefront of automation and integration. Waldron’s perspective sheds light on how organizations can navigate this new frontier and harness the power of generative AI to achieve both operational excellence and competitive advantage.
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How AI Addresses Integration Challenges
How can AI accelerate fixes around and the deployment of AI-based solutions?
Waldron argued, “AI can augment the process of building integrations by speeding up integration development and recognizing trends to suggest optimizations within the integrations themselves. This can significantly reduce the time and effort required for integration projects. As a result, IT can complete projects intended to break down these silos and unwind the spaghetti to better achieve their digital transformation and customer experience goals.”
He added, “AI can help organizations transition from siloed systems to a more composable architecture. By using AI to understand data context and relationships, organizations can more easily switch data sources and adapt processes as needs change, reducing technical debt from rigid point-to-point integrations.”
Why have IT orgs been let down by how they integrate the business and create digital apps?
Historically, Waldron said, “Enterprise integration has been a major challenge due to the constant evolution of software stacks and data within organizations. As soon as one critical system is integrated, the next version or iteration needs to be addressed, leading to a never-ending cycle of tech debt.
“The proliferation of siloed point solutions across departments has exacerbated the problem, resulting in hundreds of disconnected applications and data sources that require significant effort to integrate, connect, transform and utilize the data effectively. This complexity has made it difficult for organizations to keep up with the pace of change and digital transformation initiatives.”
Where GenAI Cuts Costs and Complexity
How much money and effort is spent on old ways of integration? How do these approaches impact creating an agile, integrated business?
In “Enterprise Architecture as Strategy,” the authors mention a company where 80% of their coding was hand-constructed integrations — without question this fact slowed their transformation.
According to Waldron, “The dollars and effort in most IT organizations are spent on legacy integration approaches and maintaining outdated technology stacks. Given the opportunity costs of time and resources spent on technical debt, it is almost impossible to quantify the costs, other than to say, very high.”
With organizations in silos and spaghetti, Waldron claimed, “It is common to have hundreds of siloed applications and data services across different departments, with a lion's share of effort going towards integrating, connecting, transforming and utilizing data across these disparate systems. These legacy approaches create tech debt and hinder an organization's ability to be agile and integrated.
In some cases, Waldron said, “It is as bad as the case study within “Enterprise Architecture as Strategy.” Maintaining these complex, point-to-point integration projects makes it challenging to adapt to changing business needs and impedes digital transformation initiatives. Here, clearly, is where AI can help.”
How will GenAI impact the cost and labor of integrating the business including marketing organizations?
GenAI, says Waldron, “will reduce the costs involved in integrating business systems. For example: AI can automate many tedious and time-consuming integration tasks like data mapping, transformation, and testing that previously required extensive manual effort. This will drastically speed up integration projects. AI can analyze existing processes and data flows to suggest optimal integration approaches, reducing the need for extensive upfront planning and design. AI-powered process mining can identify inefficiencies and opportunities for streamlining integrations across business functions like marketing.”
Waldron continues by saying, “AI models can be trained on an organization's data and processes, enabling self-configuring and self-maintaining integrations that adapt as the business evolves. By simplifying and automating integrations, GenAI will allow IT teams to manage more complex integration landscapes at lower cost. Consolidated AI integration platforms can replace fragmented point solutions, reducing licensing costs and technical debt. The key is leveraging AI to make integration efforts more composable and adaptive to change, rather than hardcoded, brittle integrations that require rebuilding for every process shift. With proper strategy, GenAI can deliver massive savings in integration costs across the enterprise.”
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Changing the Corporate Operating Model With AI
How does AI change the corporate operating model and how an integration is done?
Waldron said, “AI fortunately enables more agile and composable integration architectures that can adapt to changing data sources, processes and business strategies by allowing integration components to be swapped in and out more easily. It can augment and accelerate the integration development process itself by automating parts of the integration creation, deployment, testing and maintenance. Agents can act on integration outputs, going beyond just providing insights to executing on those insights across systems.”
In many of the companies that I have worked at, including a startup that I cofounded, fixing integrations as software changed was painful. Waldron said, “AI also necessitates better data centralization, cleansing and preparation to create 'AI-ready data' that AI models can effectively reason over. To be fair, successful AI adoption requires prototyping, cross-functional collaboration and an agile, iterative approach to infusing AI into existing processes, and their supporting integrations, rather than trying to start new AI initiatives from scratch."
What are customers finding to be the most compelling use cases for AI/generative AI?
Waldron said, “Some of the most compelling use cases involve enhancing existing processes, including quote-to-cash and lead lifecycle management.” He also sees the opportunity to infuse AI capabilities into sentiment analysis, allowing for measurable improvements to processes already in place.
Meanwhile, using AI for support use cases means the AI can reason over data to provide frontline support, such as a draft of initial responses that humans can refine. Waldron said he believes AI can help with the process of consolidating data from disparate sources into a centralized data lake or repository for AI models to generate insights and suggestions for actions.
The key benefits customers see, according to Waldron, are measurable process improvements, cost savings through consolidation and automation and the ability to harness large datasets in a way that generates actionable insights.
The Business Impact of AI-Enhanced Integration Processes
How can AI accelerate the creation and management of business integrations? What are the business impacts of moving faster?
Waldron said, “AI can augment the creation process by speeding up how integrations are built. Process mining environments can recognize trends from existing integrations and make intelligent suggestions to accelerate new integration development. AI can automate testing, deployment and scaling of integrations by analyzing real-time throughput and monitoring integration performance.
“Additionally, the business impacts of moving faster with AI-powered integrations are significant. Organizations can adapt to changes more rapidly, reducing technical debt. Faster integration enables quicker rollout of new processes, products and capabilities, accelerating digital transformation initiatives. Streamlined integrations can also reduce IT costs by consolidating tech stacks and legacy systems. Overall, AI-driven integration agility can provide a competitive advantage for businesses.”
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What are your thoughts on the claim that, for many organizations, AI plus streaming data creates the opportunity to transform the corporate value proposition?
“This line of thinking underpins some of the promise from the emergence of the IOT era,” argued Waldron, adding, “if you piece together the ease of connective technologies (i.e., working with large datasets), APIs and connected applications, there are many traditional organizations that are sitting on a treasure trove of valuable data. But that data was only useful if you employed an army of analysts to channel it.
“Where this is showing up is AI bringing the ability to gain deeper insights into customer behavior with products, that is in turn directly influencing the corporate value proposition. So not quite in the same vein as you’ve described, yet still altering the way in which companies are able to reinvent their value thanks to AI.”
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Navigating the AI Landscape: Integration and Beyond
Gartner has called 2025 the year of “Agentic AI,” something that puts AI closer to applications and makes the movement of data between apps and GenAI critical. Do you agree?
According to Waldron, he agrees with Gartner’s analysis, adding, “As AI becomes more integrated with applications, the movement of data between apps and generative AI models will be critical.
“Integration plays a key role in enabling AI to access and reason over diverse data sources. Effective integration allows organizations to centralize data from disparate systems into a unified repository that AI agents can leverage. Additionally, integration is necessary for AI agents to act based on its insights by pushing data or triggering workflows across connected applications.
“Agentic AI brings AI closer to applications, and seamless data movement facilitated by robust integration is vital for realizing its full potential.”
What recommendations do you have for CIOs early in the AI process and immature at how they integrate the business?
For CIOs early in the AI process, Waldron’s recommendations include:
- Start with existing core processes that are well-documented and integrated. Look for opportunities to enhance or accelerate these processes with AI and intelligent reasoning. This provides a solid foundation to build upon and measurable outcomes to evaluate success.
- Adopt an agile, prototyping mindset. Quickly get AI prototypes in front of cross-functional teams to iterate and gain confidence before broader deployment. This process allows for fast iteration and learning.
- Focus on reducing complexity, not adding more tools and integrations. Leverage composable AI platforms that can orchestrate AI capabilities across existing systems.
- Target processes with clear ROI potential, like invoice processing, support automation or sentiment analysis on customer interactions. Demonstrating value early builds confidence.
- Promote an AI-native culture by involving new hires and interns in AI projects from the start. This develops AI fluency across the organization.
- Form cross-functional AI teams to drive adoption with shared ownership across departments.
- Get CEO and board buy-in by running focused experiments that showcase AI's measurable impact on critical processes.
“The key,” said Waldon, “is starting small with measurable use cases, taking an agile approach, reducing complexity and developing AI fluency — not embarking on massive new initiatives from day one.”
Parting Words: Harnessing AI for Next-Level Integration
Generative AI is emerging as a game-changer in the journey toward creating agile, integrated businesses.
Still, 18 years after Jeanne Ross, Peter Weill and David Robertson introduced enterprise architecture as a foundation for operational excellence, many organizations remain stuck in silos and fragmented systems — despite companies that prioritize customer experience and industrialize their integrations seeing massive benefits. GenAI accelerates this transformation by automating integration processes, reducing technical debt and enabling adaptive, composable architectures.
Rich Waldron emphasized that AI can streamline integration development, enhance data centralization and deliver measurable improvements in processes like customer lifecycle management and support automation. CIOs starting their AI journey should focus on small, high-ROI projects, adopting agile methods and reducing complexity while building cross-functional AI fluency. With the right strategy, generative AI promises to unlock transformative potential, driving operational efficiency and innovation across the enterprise.
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