What are the common challenges with implementing AI in business technology?

1 Data quality and availability

One of the most critical factors for successful AI implementation is the quality and availability of data. AI models need large amounts of data to learn from, and the data must be accurate, relevant, and consistent. However, many businesses struggle with data silos, data governance, data security, and data integration issues that limit their ability to leverage AI. To address these challenges, IT services providers need to help their clients establish a data strategy, a data architecture, and a data culture that enable them to collect, store, access, and analyze data effectively and ethically.

2 Skills gap and talent shortage

Another common challenge with implementing AI in business technology is the skills gap and talent shortage. AI requires a combination of technical, analytical, and business skills that are in high demand but short supply. According to a report by Gartner, 54% of organizations face skill limitations as a barrier to AI adoption. To overcome this challenge, IT services providers need to invest in upskilling and reskilling their own workforce, as well as offering training and education to their clients. They also need to collaborate with academic institutions, industry associations, and other stakeholders to foster a talent pipeline for AI.

3 Ethical and social implications

A third common challenge with implementing AI in business technology is the ethical and social implications. AI can have positive or negative impacts on society, depending on how it is designed, deployed, and used. For example, AI can enhance customer service, but it can also raise privacy concerns. AI can improve decision making, but it can also introduce bias and discrimination. AI can create new jobs, but it can also displace existing ones. To address these challenges, IT services providers need to adopt ethical principles and frameworks for AI, such as fairness, accountability, transparency, and human-centricity. They also need to engage with their clients, regulators, and the public to ensure that AI is aligned with the values and expectations of the stakeholders.

4 Integration and scalability

A fourth common challenge with implementing AI in business technology is the integration and scalability. AI solutions need to be integrated with existing systems, processes, and workflows, as well as with other AI solutions. This requires interoperability, compatibility, and standardization across different platforms, tools, and languages. Moreover, AI solutions need to be scalable, meaning that they can handle increasing volumes, velocities, and varieties of data, as well as changing business needs and environments. To overcome these challenges, IT services providers need to use best practices and methodologies for AI development, testing, deployment, and maintenance. They also need to leverage cloud computing, edge computing, and other technologies that enable AI scalability.

6 Change management and adoption

A sixth common challenge with implementing AI in business technology is the change management and adoption. AI solutions can cause significant changes in the way businesses operate, communicate, and collaborate. They can also affect the roles, responsibilities, and skills of the employees and managers. Therefore, IT services providers need to help their clients manage the change and adoption of AI solutions, as well as the cultural and organizational implications. They need to use strategies and tools that facilitate communication, education, participation, and feedback among the stakeholders. They also need to address the fears, resistance, and misconceptions that may arise from AI adoption.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?