Top tips for navigating these 6 AI integration challenges

With the AI era taking shape, enterprises are increasingly looking at how to best integrate artificial intelligence into their existing systems. Take Coca-Cola Europacific Partners, a global beverage company that incorporated AI-powered analytics as part of its procurement transformation journey. This effort led to more than USD 40 million in overall business benefits, including USD 5 million in annual cost and avoidance savings.¹

Embedding AI into business processes is a vital component of digital transformation initiatives. AI automates repetitive tasks, helps enhance efficiency, aids in uncovering actionable insights and leads to faster and smarter decision-making.

AI integration varies based on industry, business needs and use cases. E-commerce retailers, for instance, might employ predictive analytics for sales forecasting, recommendation engines for tailored product suggestions and virtual assistants equipped with natural language processing (NLP) to enhance the customer experience. Manufacturing companies, meanwhile, might implement AI agents for inventory management and supply chain optimization, predictive maintenance for robotic assets, and chatbots like ChatGPT for customer support.

For many organizations, however, integrating AI is far from seamless. They can run into hurdles like compatibility issues, technical problems or disruption to business operations.

Here are common challenges enterprises might encounter and tips to tackle them for a smoother AI integration process.

In a study by the IBM Institute for Business Value (IBV), 72% of CEOs say that proprietary data is key to unlocking the value of generative AI.² But a lot of enterprises deal with incomplete, outdated or siloed datasets. According to another IBV study, some of the top barriers chief data officers face when using their organization’s data to power AI include accessibility, accuracy, completeness, consistency and integrity.³

Top tips:

• Conduct audits to identify data sources across your organization and assess the current state of your data, flagging any gaps or weaknesses.²

• Invest in data cleaning, formatting and labeling.

• Establish a framework that helps ensure high-quality data, taking into account accuracy, completeness and consistency, among other dimensions.²

• Use a cloud-native data platform that promotes real-time collaboration across silos and makes data more accessible no matter where it resides.²

• Create a data governance strategy that serves as a roadmap for effective data management spanning collection, storage and usage.

The integration process requires specific knowledge and skills to build, deploy and maintain AI systems. Companies might find it difficult to form a balanced team with all the right experts—be it AI architects, data scientists or machine learning engineers.

Top tips:

• Invest in upskilling your existing workforce through AI training and development programs.

• Hire new talent with the necessary expertise if it’s within your budget.

• Consider third-party consultants or specialists offering AI integration services. These providers can bridge any short-term skill gaps while you build your long-term AI capabilities.

AI integration costs can be high, especially at the start. To keep up with current AI technologies, enterprises might need to upgrade their IT infrastructure and modernize legacy applications. Continuing costs for maintenance must also be kept in mind. All these expenses add up, eating into financial resources.

Top tips:

• Adopt a phased approach, beginning with small projects on minor functionalities. This gives you an idea of initial costs, allows you to gauge value and scalability and provides you with the opportunity to measure AI ROI.

• Opt for more cost-efficient solutions, such as cloud-based platforms, APIs or middleware as you look into modernizing legacy systems, and pretrained AI models that you can fine-tune instead of train from scratch.

AI-driven systems inadvertently learn biases that might be present in training data and exhibited in machine learning algorithms. These learned biases can trickle down to model deployment, leading to potentially harmful outcomes, such as applicant tracking platforms discriminating against gender or predictive AI tools in healthcare returning lower accuracy results for historically underserved groups.

Meanwhile, AI hallucinations happen when generative AI-powered computer vision tools or large language models produce seemingly correct yet completely fabricated or altogether inaccurate outputs. AI hallucinations are an intrinsic feature of these models’ nondeterministic nature and usually appear during complex reasoning, extended interactions or long sequences.

Integrating AI effectively requires actively managing the risks associated with AI bias and hallucinations, as both can be detrimental to a company if not addressed.

Top tips:

• Introduce practices that cultivate fairness and accuracy, such as assembling diverse AI development teams, employing representative training data sets and incorporating a human-in-the-loop approach or human oversight throughout the integration process.

• Put bias and hallucination mitigation processes in place across the AI lifecycle. This entails selecting the most appropriate learning model, defining boundaries that limit possible outcomes and outline clear probabilistic thresholds, rigorous testing, ongoing monitoring and continuous refinement.

• Develop an AI governance strategy encompassing frameworks, policies and standards that steer responsible AI development and use.

Keeping data private and secure must be a priority for enterprises. “Ungoverned AI systems are more likely to be breached and more costly when they are,” according to IBM’s 2025 Cost of a Data Breach report.⁴ While the report found only a small percentage of the researched population (13%) experienced breaches of AI models or applications, of those compromised, 97% lacked proper AI access controls.⁵ As a result, 60% of the AI-related security incidents led to compromised data, while 31% led to operational disruption.⁵

Top tips:

• Fortify identity security by adopting modern, phishing-resistant authentication methods such as passkeys and implementing robust operational controls for nonhuman identities like AI agents.⁴

• Strengthen cybersecurity fundamentals, including access control, encryption and key management.⁴

• Regularly educate your employees on emerging AI threats and best practices.

• Periodically update and test your incident response strategies to include relevant scenarios that address the unique complexities and risks introduced by AI.

Change typically brings about resistance. Such resistance from your teams can occur in the form of concerns about job security and job displacement, avoiding getting onboard with new technologies or being skeptical of adapting to modified workflows. No matter how sophisticated your AI tools are or how well they’ve been embedded into your organization, if adoption is low then all your efforts are for naught.

Top tips:

• Involve your employees from the start, factoring in their feedback throughout the AI integration process.

• Initiate a shift in mindset, framing AI as a system designed to augment and not replace human capabilities, allowing staff to focus on higher-value tasks.

• Provide targeted training, such as walking through a specific workflow with your teams so they can immediately see the tangible benefits.

• Offer ongoing support not only on how to use AI technologies but also on how to interpret the insights they generate for more informed decision-making.

• Foster a collaborative, AI-ready culture, devising strategies for working constructively alongside AI.

By being aware of these challenges and knowing how to overcome them, you’ll be on your way to streamlining the integration process and realizing AI’s full potential for your business.