The hype and high hopes regarding artificial intelligence and its use in business have been covered in the mainstream media. Many business leaders have read news about AI’s outstanding achievements for big technology companies like Facebook, Google, and Amazon in recent years. However, most business people lack detailed knowledge of the significant challenges that artificial intelligence adaption typically generates.
This creates a false understanding that every company can easily achieve the same kind of benefits. Also, there needs to be a better understanding of what artificial intelligence can and cannot do. This article offers 7 challenges with artificial intelligence in an easy-to-understand manner.
Challenges with Artificial Intelligence: Key Barriers Backed by Statistics
• Skills Shortage: 68% of IT leaders cite “insufficient skills and expertise” as a major challenge for AI rollout. [Confluent]
• Integration Complexity: 66% of IT leaders face challenges with inability to integrate data or fragmented systems. [Confluent]
• Data Quality: 62% of IT leaders struggle with poor data timeliness and quality. [Confluent]
• Ethical Concerns: In 2023, there were 25 AI-related regulations, up from just one in 2016. [World Economic Forum]
• Transparency Issues: 76% of CEOs worry about the lack of transparency in the global AI market. [PwC]
• Resource Constraints: 40% of executives believe AI is prohibitively expensive to implement. [Harvard Business Review]
• Expectation Management: 44% of organizations have experienced at least one negative consequence from using generative AI. [Statista]
AI is not complicated technology to understand. Implementing AI typically requires an organization-wide digital transformation and changing business operations to be more data-driven. Also, organizations must change their culture to be more digitally oriented. All this requires a lot of work, and as personal development expert Robin Sharma has said “Change is hard at first, messy in the middle and gorgeous at the end.”
I also address the main challenges companies face when integrating ChatGPT into their business processes, such as using it to write a business plan and optimize other critical operations, in my new ChatGPT book.
Most of the challenges involve data, such as identifying data, understanding how it can be used in decision-making processes or product development, and what it requires to transform and evolve an organization. Before analyzing how and where to implement artificial intelligence (AI) technology, it’s crucial to study how other companies have succeeded or failed with its adoption. This analysis is an essential pre-requisite for leadership looking to begin implementing AI. It is key to developing an effective data strategy or data operating model, which is the basis of successful AI adoption.
Challenges of Digital Transformation
When we talk about the challenges of AI adoption, we can start with digital transformation challenges. Most smaller companies don’t begin immediately with AI technologies. Rather, they first undergo a digital transformation by implementing digital communication tools, platforms, and systems for clients and marketing internally and externally. Digital transformation also requires companies to analyze how to make every business operation more digital or automated.
Successful AI requires leaders to give it the time, resources, and patience needed to transform a company. This attitude is vital so that the teams working on the technology feel secure and confident to do the work effectively.
Successful digital transformation requires a company to learn more about how they can leverage their data and analyze how their digital future will look.
7 Challenges with Artificial Intelligence
A recent survey explored the limitations and hurdles companies face when implementing AI. It included 1,388 responses from respondents representing 25 different industries, including software, finance and banking, consulting and professional services, healthcare, and government, among others.
This list gives a good understanding of some of the main challenges. The following is a list of some of the most common challenges companies face when implementing AI:
- 1. Lack of understanding: one limitation is that leadership often fails to realize the transformative impact AI can have on a company and the opportunities it provides. It is crucial to understand what tools and AI technologies are available to a company, as they are ever-increasing. In addition, managers should analyze when to implement different AI projects and have the critical mindset to prioritize the most critical AI projects that need to be implemented first.
- 2. Lack of proper AI strategy: A lack of AI strategy is a significant limitation to implementing the technology. Businesses must develop a strong core team and find help from external consultants to overcome integration challenges. To develop an effective AI strategy, start by identifying a single, high-impact area where AI can add value. For instance, customer service chatbots can significantly reduce response times while improving customer satisfaction.
- 4. Lack of data: Many companies suffer from a lack of data or data silos, which means it is isolated and doesn’t interact across business divisions. Data silos occur when information is stored in isolated systems that don’t communicate with one another. For example, a retail company might have customer data in one system and sales data in another, making it difficult to combine these insights to improve customer experience. This is why every company must learn more about managing its data and how to use it to generate business value. Several AI consultant companies report that 80% of the AI project is generally related to data management and preparation.
- 4. AI skills gap: Companies often hire new employees to take over AI-related tasks. However, it is much more recommendable and far less risky to reskill and upskill existing employees and give everyone good training on AI and what opportunities it can provide to companies. There are several free artificial intelligence courses, such as the one from Helsinki University.
- 5. Cost and time: The cost and time required for AI solutions might make a company reconsider using it, but patience is needed as it can result in huge gains down the line. Due to the Covid-19 crisis some companies are forced to look for a quick return on their AI projects. However, the leading value companies can is the know-how to implement AI projects in the future.
- 6. Lack of trust: Because of a lack of understanding of AI algorithms and technology and how they can be used to generate business value, leadership can lose its confidence in the importance of AI projects. Teams that work on AI projects should be provided with a proper atmosphere so that they don’t feel too pressured, which will impact the outcome of the project.
- 7. Cybersecurity and ethics: Cyber-attacks are an increasing threat, especially as more businesses move online due to COVID-19. This can hinder an organization, so it’s essential to take adequate measures against these threats. A breach of ethics can also stop the business from moving further ahead and result in scrutiny. It’s essential to follow the legislation regarding data. Also in the earlier blog post we covered the unethical use of artificial intelligence.
You can read more about the challenges of AI implementation in the work published by Deloitte and Gartner.
Understanding the challenges of AI applications is crucial for the company’s leadership, as it helps them move forward faster. It is recommendable to analyze all the seven points mentioned above to be better prepared for starting AI project. Once the company understands the various challenges surrounding digital transformation and AI adoption, it can develop a strategy that addresses each one.
In addition, as the technologies advance, it is crucial to analyze how to apply other technological opportunities such as Web3 or the use of NFTs, which are powerful when used together with artificial intelligence. These are especially important for B2C companies.
If your company is considering adopting AI, begin by evaluating your data readiness. Do you have the necessary infrastructure, or will you need to invest in building it? Start small and scale up as your team becomes more comfortable with the technology.
Do you know any other common challenges with artificial intelligence? Please share them in the comments section.
If you want to learn more about the benefits and challenges of artificial intelligence, please read my book: Artificial Intelligence: 101 Things You Must Know Today About Our Future.
