Reading Time: 15 minutes

In the race to incorporate AI into IT systems, organizations walk a precarious tightrope between speed, security, and business value. A recent survey of 600 IT professionals reveals that leadership is increasing pressure on their IT teams to incorporate generative AI immediately into the technology stack, with almost 60% reporting business stakeholders hold unreasonable expectations on the speed and agility of new technology implementation. 

“Business leaders want to move fast with AI and not get left behind by their peers,” says David Egts, Field CTO for Public Sector at Salesforce. “But IT leaders face the daunting task of turning business demands into reality while doing it safely, securely, and responsibly.” 

latest report
Learn why we are the Leaders in API management and iPaaS

With data trapped in proprietary and siloed systems and new AI models emerging every day, the pressure to quickly incorporate AI gets more critical by the day.  AI implementation is an imperative every organization must now face. As such, nine out of 10 IT professionals report that generative AI has forced them to re-evaluate their technology strategy – and at times, face leadership decisions to shift traditional IT spend toward funding AI initiatives

The balancing act: Speed, security, and business value

While AI holds immense promise for driving innovation and efficiency, its rapid implementation can introduce significant security risks. Central to mitigating these risks and concerns is integration with 95% of IT leaders citing it as an impediment to AI adoption.

This is where IT leaders are feeling the squeeze. To successfully navigate this complex landscape —  and take advantage of AI’s potential — IT teams must build an effective integration strategy to effectively implement AI into their tech stack while ensuring the integrity of their systems and data. 

Thus, organizations need to carefully balance the urgent need for AI adoption with the imperative of ensuring the integrity of their systems and data. That all depends on a comprehensive AI implementation strategy that includes an integration roadmap.

5 AI challenges for IT teams

Rapid AI adoption presents a unique set of challenges for IT teams. These obstacles, if not effectively addressed, can hinder the successful implementation of AI solutions and undermine their potential benefits. According to the survey, these are the five top AI challenges faced by IT teams.

1. Lack of AI skills in the workforce

The rapidly evolving nature of AI demands a workforce that is not only skilled but also continuously updated with the latest advancements. The scarcity of AI skills in the workforce has made it challenging for IT teams to implement and maintain AI solutions effectively. 

One significant hurdle lies in the shortage of skilled professionals who possess the expertise required for AI development and deployment. This skills gap demands a recruiting strategy targeting specialized AI talent. However, it also requires substantial investments in training and upskilling for existing IT personnel. Both options can prove time-consuming and financially taxing, potentially delaying AI initiatives and straining IT budgets. 

2. Data security concerns

As AI relies heavily on data, ensuring its security is paramount. Forty-eight percent of IT professionals worry that their organization’s security infrastructure can’t keep up with the demand for innovation. 

The fear of data breaches and cyber threats can significantly impede the willingness to adopt AI solutions. Ensuring the confidentiality, integrity, and availability of this data becomes imperative. Thus, IT teams must implement robust security measures and adhere to stringent compliance regulations to safeguard data and maintain user trust.

3. Data quality

The adage “garbage in, garbage out” is particularly true regarding AI. Poor data quality can lead to inaccurate insights and flawed decision-making. 

Addressing data quality concerns is vital for the successful deployment and utilization of AI. IT teams must establish rigorous data governance practices to ensure that data utilized for AI training and decision-making is accurate, reliable, and representative of the intended use cases.

4. AI implementation slows down other initiatives

The urgency to implement AI should not come at the cost of stagnation in other crucial projects. However, 31% of IT workers reveal that they need more time to implement and train AI models and algorithms, leading to a slowdown in overall IT initiatives. 

Furthermore, AI implementation can inadvertently divert attention and resources from other essential IT priorities. Conversely, servicing technical debt diverts attention and resources from innovation to initiatives such as AI. Well-architected integration platforms lower technical debt and security risk, freeing time and resources for innovation.

5. Increased cost of programming and tools

Nearly half of IT professionals express worry about the increased cost of programming and tools associated with AI integration. 

AI implementation involves significant costs, encompassing the technology itself and the infrastructure, training, and maintenance required to support AI systems. Organizations must conduct thorough cost-benefit analyses to assess the return on investment and ensure that the potential benefits of AI justify the associated expenses.

Mitigate AI challenges with integration

Many of the challenges IT teams face are rooted in an infrastructure not built for modern AI. Data is often trapped in proprietary and siloed systems. 

Though new AI models seemingly emerge every day, rapid implementation can lead to flawed responses that are vulnerable to hallucinations without proper grounding. Nearly half (45%) worry their organization’s data management infrastructure simply can’t keep up. 

Meanwhile, workforces are already overwhelmed by their day jobs that predate AI. Almost one-third (31%) of IT workers say they lack the time to implement and train AI models and algorithms. With the overall demand on IT teams increasing 39% from 2023, it’s no wonder IT teams are facing a balancing act when it comes to responding to the AI mandate and maintaining everything else on their plates.

But the directive remains: AI is the priority. 

To make it happen, systems must be seamlessly integrated with existing IT infrastructure and applications — but the existing infrastructure and lack of time are significant barriers. Without integration will lead to uncoordinated and ineffective AI initiatives.

To successfully navigate the challenges of AI implementation, IT leaders need to adopt a proactive and strategic approach that includes an end-to-end integration strategy. 

  • First, MuleSoft can unlock trapped data using prebuilt and custom connectors. These connectors let the data ground AI prompts with real data, which minimizes the temptation for AI to hallucinate. 
  • Second, MuleSoft helps development teams leverage the latest and greatest AI models through API-led connectivity. Leveraging APIs to connect applications from inventory data to customer data to transactional data enables IT teams to enact effective data governance and management practices to ensure that data is accurate, complete, consistent, and accessible. This is essential to further outline how data will be collected, stored, processed, and used to train and deploy AI models. 
  • Third, MuleSoft can help customers buy down technical debt on their modernization journeys through APIs and API reuse. Retiring technical debt frees time for developers to innovate and fully embrace AI.

It all starts with integration

The journey to AI implementation is undoubtedly fraught with challenges, but with a strategic approach and a commitment to addressing these issues head-on, IT teams can navigate this complex maze successfully. 

Integration is a crucial factor for the successful implementation of AI across the organization since within those systems lies the data needed for AI to build recommendations and derive insights. From operational efficiency to improved customer interactions, connected systems lead to deeper insights and better business outcomes. Absent pulling together myriad systems, AI projects are at risk of failure, leading to wasted resources and missed opportunities.

Integration is foundational to every named concern from IT leaders. According to Egts, “The best AI model in the world isn’t useful if it’s not connected to your data.” A sound integration strategy ultimately enables teams to leverage modern APIs and build automations as part of the AI roadmap.

Balancing speed, business value, and security requires a concerted effort. By mapping out the integration foundation to your AI strategy, organizations can be better prepared for their AI future. 

The AI landscape continues to evolve and the ability to overcome these challenges will not only determine the success of AI initiatives but also position businesses for sustained growth and innovation in the digital era. By mapping out the integration foundation to your AI strategy, organizations can be better prepared for their AI future. 

Learn more about the state of integration in the recent Connectivity Benchmark Report and find out how IT leaders are moving toward adopting AI technology while safeguarding their IT environments and maintaining compliance with industry standards and regulations.