Most Common AI Adoption Challenges and How To Solve Them

Around 72% of businesses are adopting artificial intelligence for at least one function, which highlights that AI is no longer a promising trend but a necessity in the domain of custom software development.

However, not all AI implementation initiatives bring actual value. In fact, 74% of businesses struggle to achieve and scale value from AI adoption. Largely, this problem emerges due to common AI implementation challenges.

This article explores the most frequent obstacles that hinder AI adoption across organizations and offers tips for overcoming them.

This year’s report from the IBM Institute of Business Value, dedicated to the ingenuity of generative AI, reveals the most significant challenges in AI adoption, as identified by business executives. The image below illustrates these obstacles.

In this article, we will focus on the five most significant artificial intelligence problems and solutions to them.

Reported by 45% of respondents from IBM’s survey, it is currently the most significant obstacle to AI adoption according to business executives. AI-powered tools can at times produce unreliable outputs, contributing to a growing sense of distrust among users. Notably, skepticism toward the information generated by AI has increased among millennials, from 21% in 2023 to 30% in 2024.

Poor AI outputs and hallucinations of large language models (LLMs) can be severely damaging to the reputation of customer-facing AI solutions. When it comes to internal tools, poor AI output can be even more damaging. For example, a malfunction in an AI-powered receipt parser can generate inaccurate financial data, which may affect the company’s financial forecasting.

Solution:

The key to solving this problem is ensuring proper training of an ML model. When preparing data for training, it is crucial to structure the input effectively while leaving space for experimentation. Having a well-defined test dataset is essential to evaluate the accuracy of your models.

Another important aspect of maintaining AI data accuracy is a feedback loop. Regularly review the model’s outputs and fine-tune it based on performance insights. Different algorithms can be tested depending on the task. For instance, in an object classification task, there are multiple algorithmic implementations available. You can evaluate the accuracy of each by testing them against your validation dataset.

A model can also suffer from overfitting or underfitting. For instance, if you are classifying dogs and have hundreds of images of corgis but very few of dachshunds, the model can become biased toward the overrepresented class. To avoid such an issue, keep your training data balanced. You can also apply one large language model to evaluate another. Such meta-evaluation techniques help AI development teams automate the assessment of responses for quality assurance or fine-tuning purposes.

This problem is reported by 42% of business executives as one of the major AI adoption challenges. Many AI models are pre-trained on general-purpose datasets, but this knowledge is often not sufficient to handle industry-specific tasks.

Consider a logistics company that uses a general-purpose large language model for customer communication. In this scenario, the LLM-powered AI agent will likely provide generic responses with basic logistics information, rather than offer accurate, company-specific details about transportation options, routes, policies, or associated fees.

Overall, without accessing high-quality internal data, such as custom behavior logs, product and service documents, or operational records, companies struggle with:

• Fine-tuning models to reflect company-specific context
• Building AI systems that understand company-specific terminology
• Delivering accurate, relevant, and trustworthy responses to customers.

Solution:

To deal with such an AI implementation issue, create a curated knowledge base to power AI tools. Here it goes about small but high-quality datasets that include well-labeled, relevant, and diverse data. However, due to their limited size, these datasets may also constrain the model’s output capabilities.

Start by building a curated knowledge base and keep enhancing your dataset with the following practices:

• Generate synthetic data that supplements real-world examples. By applying artificial information that mimics real-world data, AI development teams can scale and fill the gaps in small company datasets.
• Use pre-trained models instead of training models from scratch. Pre-trained models help AI developers cover common tasks, which allows them to focus on more specific and complex workflows.
• Apply retrieval-augmented generation (RAG) instead of fine-tuning. This allows you to enhance your model’s ability to provide responses based on external knowledge without altering the entire model.

Such practices can effectively compensate for the lack of proprietary data needed to support your company-specific dataset and operations.

Even though the demand for AI development teams is booming, 42% of company executives report the lack of technical experience as one of the biggest challenges of implementing AI. Many software development specialists can integrate pre-built AI components in software solutions, but challenges often arise when it comes to model configuration or fine-tuning.

A lack of expertise among AI developers can lead to poor-quality outputs from large language models, along with other issues that may negatively impact your AI-powered products. Some examples of such issues include the failure to convert AI outputs into a human-centric format, security breaches, extensive data hallucinations, etc.

Solution:

Rely on a software development team with strong experience in AI and ML development. Ideally, the team should have a proven track record of successful projects demonstrating their ability to build AI models from scratch, configure and fine-tune them, and integrate these models seamlessly into existing software solutions. If a company applies its own AI-powered tools in its internal processes, it is a positive indicator.

This demonstrates that the team effectively uses its AI development expertise to address company-specific challenges. Implementation of internal AI tools also suggests the team’s commitment to innovation driven by data and AI capabilities.

Around 42% of business executives identify this issue as one of the most significant AI challenges in business. While many organizations acknowledge the transformative potential of artificial intelligence, they often struggle to translate that potential into specific business outcomes.

A key obstacle is the lack of a clear financial justification or a well-defined business case for AI initiatives. Decision-makers often hesitate to allocate resources because they don’t have a clear understanding of where and how AI should be applied. Another aspect of this problem is the lack of knowledge regarding the specific value AI initiatives can deliver and which metrics should be used to evaluate their success.

Solution:

To come up with a clear business justification for an AI implementation initiative, you need to establish measurable KPIs, such as increased revenue, cost savings, or efficiency gains. This can be achieved through a discovery phase.

This approach helps you:

• Ensure clear objectives and metrics for measuring project success
• Achieve a clear project vision and define its roadmap
• Align your project expectations with the vision of your AI development team
• Define project architecture and technology stack
• Understand project risks and develop risk mitigation strategies.

The use of AI models can introduce new security risks because they may expose sensitive information through training on improperly anonymized data or generating outputs that reveal confidential details. For example, McKinsey reports that phishing attacks have risen by 1.265% since the proliferation of generative AI platforms in 2022. No wonder that 40% of business executives cite concerns over data privacy and confidentiality as substantial barriers to AI adoption.

Solution:

To deal with this problem, prioritize regulatory compliance with a focus on industry-specific safety and data protection frameworks, such as GDPR, CCPA, HIPAA, and FedRAMP. Possible steps here include:

• Conducting a technical audit aimed at identifying the platform’s security vulnerabilities
• Implementing robust data governance policies that clearly define how data is collected, stored, and used
• Focusing on anonymization and encryption, aimed at preventing exposure of personally identifiable or sensitive information during model training and inference.

When using third-party AI solutions, companies can also benefit from dedicated model deployments. For example, Azure OpenAI allows for the configuration of data isolation, adding an extra layer of control and trust. This makes AI adoption more secure and compliant by design.

Another important practice for keeping AI models secure and compliant is conducting regular risk assessments. In particular, 81% of respondents from IBM’s survey claim to do this. However, it’s crucial to design AI-powered solutions with security in mind from the outset. Doing so can reduce the frequency, complexity, and resource demands of future risk assessments.

To mitigate the outcomes of the above-mentioned AI adoption obstacles, most companies apply the practices oultined in the image below.

A certain way to safeguard an AI implementation initiative is to rely on a team that knows the intricacies of ML development and has solid expertise in building tailored AI-powered solutions.

We at Leobit have strong expertise in helping companies from different industries adopt AI-powered tools. We have an extensive portfolio of successful AI projects. Our specialists configure custom AI models or efficiently integrate out-of-the-box solutions, such as ones from the portfolio of Azure AI services.

We also developed several AI employees that help our specialists automate and enhance varying workflows. For example, our sales team uses Leo, an email auto-response solution that provides fast and personalized responses to customer requests. This tool uses custom machine learning algorithms to categorize and score leads based on request type, industry, and technology match.

We also successfully use Leora, an AI sales agent that uses the company’s curated knowledge base to provide detailed and personalized responses to prospects. Leora integrates with a tool built with the Voice RAG (Retrieval-Augmented Generation) technology, which means that it is capable of handling voice interactions in real time. In addition, we have recently released Leona, an AI agent that automates project evaluation. It helps our sales team find the most suitable cooperation opportunities and automatically sends project proposals to promising clients.

Our AI development and implementation expertise is not limited to internal projects. We have also helped customers from different industries find solutions to artificial intelligence problems and build efficient AI-powered software. For instance, we developed a digital marketplace that facilitates mergers and acquisitions. This solution is enhanced with an AI-powered consultant that relies on pre-trained OpenAI LLMs to provide users with business valuation insights and transaction guidance. Leobit’s AI development specialists have also developed a minimum viable product (MVP) for a car warranty platform. This system applies AI/ML scripts for risk and insurance price calculations.

While working on these projects, our specialists encountered the most common AI adoption and AI implementation challenges, developed effective solutions, and successfully delivered products that use machine learning algorithms to meet customers’ needs.

While AI holds significant potential for business, its adoption may be a challenging task, primarily due to common AI challenges. The most significant ones include concerns about data accuracy or bias, lack of proprietary data, lack of expertise, poor business justification, and security concerns. Each challenge on this list can be mitigated with the right approach. However, the key point is finding the right team with solid experience in AI software development.

Leobit is ready to provide you with such a team. As an experienced .NET and AI development company, we are ready to help you handle all the common software development challenges and come up with AI-powered solutions that bring actual value.

Contact us to efficiently leverage artificial intelligence and the promises it holds.