By 2030, AI is expected to contribute $15.7 trillion to the global economy. By this time, the majority of companies (70%) will have adopted at least one type of AI technology. However, with less than a decade to go before we reach these benchmarks, organizations of all sizes are facing barriers to AI adoption. The challenges of adopting AI are not confined to a specific industry or business function. These six obstacles can be found at companies big and small, new and old, tech-savvy and digitally-challenged.
1. Company Culture
The biggest, and most dangerous, barrier to AI adoption is the mindset that AI isn’t necessary or beneficial. This creates a corporate culture that doesn’t see the need for AI, perhaps because of a fear of lost jobs or lost control. Strategic AI implementation can give companies an advantage over their competitors. Without it, organizations risk getting left behind in the marketplace as competitors embrace AI-backed insights and efficiencies.
Deploying and leading AI initiatives, even in a wholly supportive culture, is a big job. Corporate culture needs to empower the individual(s) managing implementation because even in ideal AI adoption scenarios, there are challenges.
2. Data Requirements
AI learns from data. The resulting abilities of the AI engine are a direct result of the quality of the training dataset. Complete, structured, and bias-free data is a necessity. While this sounds obvious, it’s not easy. Even if your organization has access to relevant data, it may not be sufficiently organized or mistake-free. Datasets with human error and biases lead directly to AI with the same inclinations. This introduces ethical concerns as well as the types of mistakes that AI is supposed to eliminate.
AI implementation is costly, especially when building a customized solution. This includes money as well as time, talent, and tools. AI talent, including data scientists, software engineers, and developers, is in short supply. This shortage further increases the financial and time-based resources required for implementation.
4. Lack of Strategy
AI adoption is a broad term that can mean different things for different companies. A small business might use a chatbot on its website. A large manufacturer could add thousands of robotic arms to its facility. Attempting to integrate these additions without a strategy makes it difficult to maximize their impact—regardless of the scale of your implementation.
Strategic AI implementation may require additional resources but also adds value. Identifying use cases, potential process updates, beneficial competitive advantages, and opportunities to scale across business functions from the beginning removes barriers to AI adoption.
Depending on where your business operates and what products or services you provide, you may be subject to restrictive regulations on AI. Even if you don’t face any specific rules about using AI, you should be aware of the inherent characteristics of this type of algorithm, which is often a “black box” that can inherit biases from data. This can make it difficult to explain the reason behind AI’s decisions to stakeholders and build trust in the artificial intelligence.
6. Security Weaknesses
Technology inherently introduces cybersecurity risks. AI is no exception. The team tasked with implementation must consider how AI’s training data, insights, and decisions are protected to keep your company, employees, and customers safe.
Do any of these barriers to AI adoption sound familiar? The first step to seamlessly integrating AI into your business processes is identifying the challenges that are hindering the project before you start.