According to a McKinsey survey, high-performing companies attribute most of their profits to their integration and use of AI. According to the report, proactive AI adopters can see significantly higher profit margins in comparison to non-adopters. They are also positive about the future, predicting to grow and benefit even more, as AI applications mature.

In the report, McKinsey determined four business areas where AI can create value:

• Enabling companies to better project and forecast to anticipate demand, optimise R&D, and improve sourcing;
• Increasing companies’ ability to produce goods and services at lower cost and higher quality;
• Helping promote offerings at the right price, with the right message, and to the right target customers;
•  And allowing them to provide rich, personal, and convenient user experiences.

How can AI adoption help businesses?

According to Harvard Business Review, AI can help to support three primary business needs:

1.   Automating business processes such as administrative and financial activities that do need to be done manually.
2.   Generating insights with data analysis e.g. using algorithms to find patterns in vast volumes of data for further predictions.
3.   Engagement with customers and employees through chatbots, intelligent agents etc.

Challenges of AI implementation in business

1. Company culture- one of the biggest barriers to entry 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, organisations risk getting left behind in the marketplace as competitors embrace AI-backed insights and efficiencies.
2. Data requirements- as 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 necessary. 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.
3. Costs- AI implementation is costly, especially when building a customised 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 maximise 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 remove barriers to AI adoption.
5. Regulations- Depending on where your business operates and what products or services are provided, an organisation may be subject to restrictive regulations on AI. Even if it doesn’t face any specific rules about using AI, the organisation 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 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.