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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

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Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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