AI Integration Challenges: Common Risks and How to Navigate Them

I’ve noticed two different kinds of businesses running in the AI race today.

One sort are those that are far ahead of the competition, with AI fully integrated into their day-to-day operations and ways of working.

And the second are still stuck at the starting line.

If you’re in the latter camp, you’re not alone: research shows that only 26% of organisations consider themselves ‘seasoned’ at AI adoption, and, as of 2023, just 16% of businesses are in the process of adopting AI technology.

Luckily, this is a ‘The Tortoise and the Hare’ race; slow and careful AI integration is key to crossing the finish line with improved efficiency, streamlined processes, and properly secured data. So not all hope is lost.

In this article, I’ll outline some of the key AI integration challenges I’ve witnessed businesses facing, including various AI challenges such as privacy concerns, algorithm bias, and regulatory issues. I will also discuss the importance of data privacy and security, and explain how you can overcome these hurdles.

Suggested reading: AI is becoming increasingly important in almost all business sectors,  but perhaps none more so than education. Read our free eBook, ‘AI Use Cases for Education Publishers‘, to learn more about educational AI integration.

Introduction to AI Integration

Artificial intelligence (AI) integration is the process of incorporating AI technologies into existing business processes to enhance operational efficiency, improve decision-making, and gain a competitive advantage. Successful AI integration requires careful planning, effective data management, and robust governance frameworks. AI adoption and implementation involve several key steps, including data analysis, AI model development, and deployment. However, integrating AI into operations can be challenging due to significant challenges such as poor data quality, data security concerns, and ethical considerations.

AI Adoption and Implementation

AI adoption and implementation involve several key steps, including data collection, data analysis, AI model development, and deployment. Effective data management is crucial for successful AI integration, as high-quality data is essential for training accurate AI models. AI algorithms require large amounts of data to learn and improve, making data readiness a critical factor in AI adoption. Implementing AI also requires careful consideration of ethical principles, such as transparency, accountability, and fairness. AI ethics is a critical aspect of AI development, as it ensures that AI systems are designed and deployed in a responsible and ethical manner.

Challenge 1: Maintaining data security

Data security failures are one of the most significant global risks faced by businesses, with some experts suggesting that cyber incidents are more of a threat than even climate change or a recession. Mishandling sensitive data can lead to significant legal and reputational consequences for organizations.

Small and medium-sized businesses are increasingly being targeted by cyber attackers, and certainly can’t afford to skip any steps when it comes to data security. The risks of poor AI integration could include:

• Non-compliance: This risk increases with off-the-shelf tools that have restrictive or industry-agnostic data privacy policies.
• Data leaks: If your AI tool is compromised by an attacker, a huge amount of internal and client data could be at risk.
• Data poisoning: This involves threat actors skewing the data on which an AI model is trained to decrease the quality of results and increase the likelihood of copyright breaches.
• Exposure to prompt injection attacks: Hackers can add prompts to your AI tool to extract private data or otherwise disrupt your system.

Advanced data encryption, including robust encryption methods, ensures that your data remains secure even if intercepted.

Solution

While the number of potential data security challenges that come with AI can seem daunting, they can be effectively managed through careful integration. This starts with choosing the right AI tool.

I would recommend using Microsoft Azure AI services to create a tool that is tailored to your specific compliance and security requirements. Azure OpenAI has the benefit of:

• Advanced data encryption
• Network security protocols
• Transparent security reporting
• Centralised identity and access management

As much as AI use poses certain security challenges, it can also be used to enhance security, for example through automated threat identification and deep data analysis. 

Pro tip: Read our recent article, ‘What is the Future of Cloud Security?‘, for more insights on current global security challenges.

Challenge 2: Keeping up with AI regulations

Without careful human oversight and a risk-oriented AI integration strategy, businesses may fall short of key compliance requirements. Robust regulatory frameworks are essential in managing the risks associated with the rapid advancement of AI technologies in finance. Large or medium-sized companies, which are statistically more likely to adopt AI, will need to be especially cautious to ensure they are meeting the expectations of shareholders, clients, and regulators.

Some recurring issues I’ve seen in businesses with ineffective AI governance include ethical concerns such as privacy, transparency, and accountability:

• AI bias: Subtle biases in existing data are exemplified in AI outputs.
• Copyright infringement: Whether due to data poisoning or poorly constructed prompts, copyright infringement is easy to miss without visibility over the whole AI lifecycle.
• Reduced transparency: Poor transparency over backend processes could mean AI hallucinations and inaccurate data are missed.

The fact that international and industry-specific regulations are still developing only adds to the difficulty of this challenge. Even adhering to the most recently published frameworks, such as the G7 Hiroshima Process on Generative AI (January 2023) or NIST AI Risk Management Framework (September 2023), could require re-evaluating your governance strategy in just six months.

Solution

Due to the complexity of ever-changing AI governance requirements, this challenge is best handled by a technical AI expert. Whether you have an in-house AI implementation team or choose to work with an experienced technology partner, ensure that your technical lead has a good understanding of:

• Up-to-date industry regulations
• Responsible AI principles
• Legal requirements

They should then be willing and able to work closely with your team to establish a governance system that works for you.

Challenge 3: Getting reliable results out of your AI tool 

Data is everything when it comes to implementing a useful, secure, and efficient AI tool. Bad data leads to significant revenue losses and complicates data management and decision-making. As Liza Schwarz, Senior Director of Global Product Marketing at Oracle NetSuite, summarises:

‘AI is only as good as the data you have. […] Having your data in a unified system is essential, so you do not have to gather data from all over the place and then question if your data is accurate or not.’

If a business attempts to integrate AI without a firm foundation of high-quality data or an understanding of how they feed said data into the AI (i.e. prompts), they could end up with a solution that is, at best, useless or, at worst, actively harmful to human productivity and efficiency. Continuous monitoring is essential to ensure that regulatory standards evolve alongside technological changes, maintaining both market integrity and consumer protection.

Solution

Your business must be ready and willing to change how you manage data. This might include:

• Establishing new internal IT governance processes
• Performing data cleaning to remove inaccurate or irrelevant material
• Understanding how best to prompt your AI solution to ensure accurate results 
• Building a scalable data lake architecture in which to pool high-quality data

Data cleaning also provides wider long-term benefits, improving a business’s ability to perform data analysis and identify potential security weaknesses.

At Talk Think Do, our team works alongside businesses to identify how their data management system may need to change in preparation for AI integration and supports them in making these changes. 

I had a fantastic experience recently working with Explore Learning to develop a custom assessment engine, helping to restructure their data storage to ensure streamlined future AI integration.

Challenge 4: Being limited by the functionalities of an off-the-shelf tool

I’ve talked to numerous business leaders who have acquired an off-the-shelf AI tool and now feel limited in what they can use AI for within their business. Many of these limitations stem from poorly managed AI projects that lack proper stakeholder alignment. Despite the many tools flooding the market in 2024, AI integration is rarely as simple as straightforward procurement.

A poor-fit AI tool will not only be a drain on your time and resources, but could also lead to:

• Technological overwhelm: Your team may struggle to use complex or poorly integrated AI tools, decreasing employee satisfaction and efficiency.
• Missed opportunities: AI is a powerhouse of possibilities. Using a tool that is a poor fit for your organisation might mean that you miss out on key benefits such as deep search technology, risk identification, or personalised user experiences. Thorough assessment of potential AI applications can help address these challenges effectively.
• Future limitations: If your AI tool is not compatible with certain legacy systems or other AI tools, you may be limited in terms of future AI use.

Solution

I believe that in most cases, custom AI development is the best way to create a tool that serves a business’s unique needs. While it may require more time and resources than AI tool procurement in the short term, it will deliver substantially more benefits in the long term, such as:

• Improved AI explainability
• Higher usability for non-technical team members
• Specific and relevant outcome-oriented processes
• Security that is tailored to organisational and industry-specific requirements

At Talk Think Do, we run in-depth discovery sessions with our clients to ensure the solution they choose is robust and will serve them in the long term.

Suggested reading: I discuss the various uses for custom AI software in more depth in my recent article, ‘Generative AI: Transforming Software & Product Delivery Across Businesses’. Read it now to learn more. 

AI Systems and Infrastructure

AI systems and infrastructure are critical components of AI integration, as they provide the foundation for AI model development and deployment. AI systems require significant computing power, data storage, and networking capabilities to function effectively. Advanced AI models, such as generative AI, require specialized infrastructure and expertise to deploy and manage. AI tools, such as machine learning frameworks and data analytics platforms, are also essential for building and deploying AI models. However, integrating AI into existing infrastructure can be challenging due to compatibility issues, data security concerns, and scalability limitations.

AI Governance and Management

AI governance and management are critical components of AI integration, as they ensure that AI systems are designed, developed, and deployed in a responsible and ethical manner. AI governance involves establishing clear policies, procedures, and guidelines for AI development and deployment. Effective AI governance requires collaboration between key stakeholders, including data scientists, business leaders, and non-technical team members. AI management involves ensuring that AI systems are transparent, accountable, and fair, and that they are aligned with business outcomes and objectives. Continuous learning and research are essential for staying up-to-date with the latest AI technologies and best practices, and for addressing emerging AI challenges and concerns.

AI Models and Development

AI models are a critical component of AI integration, as they enable businesses to extract valuable insights from large datasets and make informed decisions. AI model development involves several key steps, including data preparation, model training, and model deployment. Effective AI model development requires careful consideration of ethical principles, such as fairness, transparency, and accountability. AI models can be biased if they are trained on biased data, making it essential to ensure that data sources are diverse and representative. AI development lifecycle involves continuous monitoring and updating of AI models to ensure that they remain accurate and effective.

Drive efficiency with a custom AI solution

Some experts recommend that, for effective AI implementation, companies should spend approximately 20–30% of their time managing data. Additionally, leveraging AI insights is crucial for transforming data into actionable information that guides continuous organizational progress and aligns with strategic goals.

While the importance of data preparation can’t be overstated, it’s worth recognising that most small- to medium-sized businesses will not have the internal technical capacity to overhaul their data systems, integrate AI, and manage it as their requirements evolve.

Rather than choosing a poor-fit off-the-shelf tool, failing to achieve desired business outcomes often signals underlying organizational and strategic hurdles. I believe the best way to overcome these challenges is to integrate a highly customisable AI solution with the help of an expert implementation team.

Talk Think Do is a Microsoft Solutions Partner, Learnosity Partner, and certified CCS supplier. We support businesses with cloud application development, DevOps implementation, and custom generative AI integration using Microsoft Azure OpenAI services.  If you’re interested in integrating a custom AI solution today, book a free consultation to speak to a member of the team.