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9 Out of 10 Enterprises Experience Challenges Integrating AI into Their Tech Stack

Rich Waldron
Tray.io

The integration and maintenance of AI-enabled Software as a Service (SaaS) applications have emerged as pivotal points in enterprise AI implementation strategies, offering both significant challenges and promising benefits. Despite the enthusiasm surrounding AI's potential impact, the reality of its implementation presents hurdles. Currently, over 90% of enterprises are grappling with limitations in integrating AI into their tech stack.

We recently commissioned and released findings from The 2024 AI Implementation Strategies in the Enterprise survey that delves into insights from a diverse cohort of 1,044 US-based business professionals, including executives, team leaders, department heads, and practitioners at companies with 1,000 or more employees. This study explores the intricacies of AI integration efforts across various industries and internal departments. We found the following:

1. SaaS bloat remains a challenge — and AI is further complicating the issue

SaaS bloat persists as a significant challenge, with more than half of respondents (55%) reporting that they have more than 50 SaaS apps in their tech stack, and 37% state they have more than 100. Complicating matters further, the majority of SaaS applications now incorporate f AI functionality — 73% of respondents state that over half of their apps have AI capabilities or AI-augmented features. Moreover, 96% intend to leverage these AI features to enhance process efficiency and employee productivity, customer satisfaction and cost reduction.

2. The rapid proliferation of AI within existing SaaS apps is causing significant integration pains

Organizations face challenges such as provisioning, ongoing management, change management, developer dependency, lack of implementation frameworks, and difficulty in experimenting with and prototyping AI features. Additionally, AI tools are perceived as costly and time-intensive to integrate.

Looking ahead, as enterprises prioritize data governance and employee skill development in their AI implementation strategies, anticipated key challenges include managing data governance, compliance, security, and trust, along with addressing the lack of familiarity with AI tools and workforce skills. Employee resistance and deployment costs also loom as significant hurdles.

3. Lack of clear and aligned AI integration strategies threatens to hinder progress

The survey findings reveal a notable disconnect between executives and practitioners regarding AI implementation strategies. While almost half of enterprise executives (48%) emphasize building strong integrations between internal SaaS apps and AI, practitioners often lack clarity on AI strategy, with nearly 20% stating their organization lacks an AI strategy altogether.

4. Despite the challenges, enterprises are optimistic about the potential of AI

Enterprises remain optimistic about AI's potential benefits, including improving process efficiency, enhancing productivity, boosting customer satisfaction, reducing costs, and gaining competitive advantage. When asked, "where can your organization most benefit from the application of AI?" respondents identified IT is universally identified as the number one practice, followed by Product Development and Engineering and Customer Service and Success. Respondents envision leveraging AI to enhance internal processes, automate manual tasks, and improve decision-making. In the future, AI is expected to streamline tasks, accelerate decision-making, and provide frameworks to enhance job performance.

Streamlining AI Integration for Sustainable Growth

As enterprises embark on their AI implementation journeys, they will be challenged with managing the functionality of dozens of different AI features in a sustainable way without causing conflicts between connected apps in their tech stack. Organizations should proactively address the dual challenges of SaaS bloat and the accelerated infusion of AI functionalities by auditing their tech stack, prioritizing applications for AI integration, and developing robust implementation frameworks. Cross-functional collaboration is also crucial for aligning IT, data governance, compliance and business objectives, while a mindset of continuous improvement ensures adaptability to the evolving landscape of AI integration.

Despite current challenges, organizations should maintain optimism about AI's potential benefits, including improved efficiency, productivity, customer satisfaction, cost reduction and competitive advantage. Leveraging AI across various departments, particularly in IT, product development, engineering and customer service, can yield significant dividends.

Moving forward, by thoughtfully prioritizing the strategic integration of AI into business processes with a lens on sustainability and optimization, organizations can unlock the full potential of AI to drive innovation and success in the digital age.

Rich Waldron is CEO and Co-Founder of Tray.io

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9 Out of 10 Enterprises Experience Challenges Integrating AI into Their Tech Stack

Rich Waldron
Tray.io

The integration and maintenance of AI-enabled Software as a Service (SaaS) applications have emerged as pivotal points in enterprise AI implementation strategies, offering both significant challenges and promising benefits. Despite the enthusiasm surrounding AI's potential impact, the reality of its implementation presents hurdles. Currently, over 90% of enterprises are grappling with limitations in integrating AI into their tech stack.

We recently commissioned and released findings from The 2024 AI Implementation Strategies in the Enterprise survey that delves into insights from a diverse cohort of 1,044 US-based business professionals, including executives, team leaders, department heads, and practitioners at companies with 1,000 or more employees. This study explores the intricacies of AI integration efforts across various industries and internal departments. We found the following:

1. SaaS bloat remains a challenge — and AI is further complicating the issue

SaaS bloat persists as a significant challenge, with more than half of respondents (55%) reporting that they have more than 50 SaaS apps in their tech stack, and 37% state they have more than 100. Complicating matters further, the majority of SaaS applications now incorporate f AI functionality — 73% of respondents state that over half of their apps have AI capabilities or AI-augmented features. Moreover, 96% intend to leverage these AI features to enhance process efficiency and employee productivity, customer satisfaction and cost reduction.

2. The rapid proliferation of AI within existing SaaS apps is causing significant integration pains

Organizations face challenges such as provisioning, ongoing management, change management, developer dependency, lack of implementation frameworks, and difficulty in experimenting with and prototyping AI features. Additionally, AI tools are perceived as costly and time-intensive to integrate.

Looking ahead, as enterprises prioritize data governance and employee skill development in their AI implementation strategies, anticipated key challenges include managing data governance, compliance, security, and trust, along with addressing the lack of familiarity with AI tools and workforce skills. Employee resistance and deployment costs also loom as significant hurdles.

3. Lack of clear and aligned AI integration strategies threatens to hinder progress

The survey findings reveal a notable disconnect between executives and practitioners regarding AI implementation strategies. While almost half of enterprise executives (48%) emphasize building strong integrations between internal SaaS apps and AI, practitioners often lack clarity on AI strategy, with nearly 20% stating their organization lacks an AI strategy altogether.

4. Despite the challenges, enterprises are optimistic about the potential of AI

Enterprises remain optimistic about AI's potential benefits, including improving process efficiency, enhancing productivity, boosting customer satisfaction, reducing costs, and gaining competitive advantage. When asked, "where can your organization most benefit from the application of AI?" respondents identified IT is universally identified as the number one practice, followed by Product Development and Engineering and Customer Service and Success. Respondents envision leveraging AI to enhance internal processes, automate manual tasks, and improve decision-making. In the future, AI is expected to streamline tasks, accelerate decision-making, and provide frameworks to enhance job performance.

Streamlining AI Integration for Sustainable Growth

As enterprises embark on their AI implementation journeys, they will be challenged with managing the functionality of dozens of different AI features in a sustainable way without causing conflicts between connected apps in their tech stack. Organizations should proactively address the dual challenges of SaaS bloat and the accelerated infusion of AI functionalities by auditing their tech stack, prioritizing applications for AI integration, and developing robust implementation frameworks. Cross-functional collaboration is also crucial for aligning IT, data governance, compliance and business objectives, while a mindset of continuous improvement ensures adaptability to the evolving landscape of AI integration.

Despite current challenges, organizations should maintain optimism about AI's potential benefits, including improved efficiency, productivity, customer satisfaction, cost reduction and competitive advantage. Leveraging AI across various departments, particularly in IT, product development, engineering and customer service, can yield significant dividends.

Moving forward, by thoughtfully prioritizing the strategic integration of AI into business processes with a lens on sustainability and optimization, organizations can unlock the full potential of AI to drive innovation and success in the digital age.

Rich Waldron is CEO and Co-Founder of Tray.io

Hot Topics

The Latest

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

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Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...

2025 was the year everybody finally saw the cracks in the foundation. If you were running production workloads, you probably lived through at least one outage you could not explain to your executives without pulling up a diagram and a whiteboard ...

Data has never been more central to a greater portion of enterprise operations than it is today. From software development to marketing strategy, data has become an essential component for success. But as data use cases multiply, so too does the diversity of the data itself. This shift is pushing organizations toward increasingly complex data infrastructure ...

Enterprises are not stalling because they doubt AI, but because they cannot yet govern, validate, or safely scale autonomous systems, according to The Pulse of Agentic AI 2026, a new report from Dynatrace ...

For most of the cloud era, site reliability engineers (SREs) were measured by their ability to protect availability, maintain performance, and reduce the operational risk of change. Cost management was someone else's responsibility, typically finance, procurement, or a dedicated FinOps team. That separation of duties made sense when infrastructure was relatively static and cloud bills grew in predictable ways. But modern cloud-native systems don't behave that way ...