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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

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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