What is the Role of Artificial Intelligence in Business in 2024?

Artificial Intelligence (AI) is evolving faster than any other technology. 

While AI and machine learning provide immense benefits, over-relying on them can leave businesses vulnerable when systems fail. That's why it’s vital to understand its capabilities and key uses before determining the role of artificial intelligence in business and how to use it to move your business forward.

Many CEOs and leaders are often curious about the best ways to utilize AI for their organization, especially given how quickly capabilities are expanding. If your organization is also considering incorporating AI, here are five ways to use it this year and in the future.

Increased Productivity

Generative AI, like ChatGPT, boosts workers' productivity by 66 percent. This increase in productivity can improve your bottom line, enhance service quality, and contribute to a better work-life balance for your employees.  

Many big companies are already doing it:

• Amazon uses AI to predict product demand and place inventory closer to customers, which helps them offer same-day or next-day deliveries.
• Facebook has automated its content moderation process to scan and flag content that potentially violates its community standards.
• GE uses AI to detect, diagnose, and predict failures in machine components before failure. This helps prevent downtime across verticals.
• Walmart uses AI to optimize inventory management and supply chain processes.

All the examples above prove that AI can boost productivity, and this year, more businesses will follow the lead of large organizations in adopting AI. Still, implementing AI requires a significant upfront investment in technology and training. It’s not like installing off-the-shelf software across company devices –  you’ll likely have to train your models to optimize outcomes.

Job displacement is possible for roles mainly involving routine tasks. So, when introducing AI-powered automation to your processes, ensure it aligns with your broader goals and does not affect employee well-being.

To increase productivity using AI, start by identifying the areas in your operations where AI can have the most significant impact. These could be routine tasks, data analysis, or customer interactions. Investing in training your employees on AI technology and working with AI experts or consultants is essential to ensure a smooth integration, especially as AI continues to evolve.

Personalized Customer Experiences

Providing customers with a personalized experience can make them feel valued and result in stronger relationships and positive word-of-mouth. Today, customers expect businesses to deliver personalized experiences, so much so that 76 percent of consumers feel frustrated when they don’t have that experience. 

With the help of AI, you can analyze customer data to understand their preferences and behaviors. After analyzing said data, AI models use this information to provide personalized recommendations, content, and services. Remember, you must comply with data privacy regulations. 

Pro Tip: Be cautious of over-personalization, which can make customers uncomfortable if they perceive their privacy violated.

Where is the line on how to use AI for personalized customer experiences that don’t cross the line? Start by collecting and analyzing customer data. Then:

• Use AI tools based on machine learning (ML) algorithms that identify patterns and preferences to create dynamic, individualized user profiles.
• Use these profiles to provide tailored recommendations, content, and interactions, enhancing the overall customer experience. 
• Continuously refine your AI models based on customer feedback and interaction data to improve accuracy in personalization efforts.
  
  

Reduced Errors

Mistakes made during operations, data entry, or decision-making can result in higher costs, decreased revenue, and harm to the reputation of the company in question. Poor data quality caused by inaccuracies in data entry causes an average loss of $12.9 million annually for organizations.

AI can reduce these errors by automating complex tasks, analyzing large datasets, and assisting in decision-making. Machine learning algorithms can go through your company’s database and identify patterns that indicate errors so you can correct them proactively.

Training AI systems to detect errors can be challenging due to the need for accurate and unbiased data to train models. And just as all fraud detection systems require human oversight, you will need experts to review the accuracy of your AI models before you can rely on them to reduce operational errors. 

Where can you start?

Identify small areas prone to human error, from data entry to decision-making. Smaller use-case deployments allow you to see how the proposed process works in real-time before implementing it across your entire organization.

Here are some small-scale starting points:

• Implement AI-powered tools for data entry, which can automatically validate inputs against predefined rules and databases. 
• Leverage Natural Language Processing (NLP) tools to analyze your documents and identify errors, enhancing quality control, improving efficiency, and reducing risks. 
• Introduce AI to your real-time monitoring tools to enable it to identify any anomalies or deviations from standard procedures.
  
  

Faster Innovation

79 percent of companies rank innovation as one of their top three priorities. AI can accelerate the innovation process in your organization and give you an edge over your competitors. For fast-moving enterprises, intelligent AI uses can include:

Feature Ideation - Use generative AI models like DALL-E to create novel illustrations and UI mockups for potential new product features based on text descriptions. Designers then refine the most promising ideas.

Need-Gap Analysis - Apply NLP techniques to analyze customer support tickets, user interviews, reviews, etc., automatically detecting unmet needs and pain points. Product teams can brainstorm features to address these gaps.

Trend Forecasting - Train machine learning algorithms on technology and market trend data to predict rising trends like blockchain, metaverse, etc. Early research on emerging tech allows exploring product concepts ahead of the market.

Concept Testing - Use AI to rapidly create minimum viable product (MVP) prototypes and landing pages. Test these concepts via automated A/B testing powered by reinforcement learning algorithms to filter winning ideas.

Risk Analysis - Mitigate innovation risk using Monte Carlo simulation models that assess feasibility, market reception, and return on investment for thousands of generated product ideas in minutes.

Train your team to use AI tools effectively and monitor their progress to measure the impact on your capability to innovate.

Improved Data Analysis

68 percent of Chief Data Officers want to improve how they use data and analytics in their organizations. Practical data analysis is crucial for businesses to gain insights, make informed decisions, and identify market trends.

Business leaders often explore using AI for data analysis as their first use case. However, the accuracy of your analyses depends on the quality of the data you use. Luckily, there are AI-powered data analysis tools like Power BI that you can use on your own data set. 

You can also use tools like Azure Machine Learning and Amazon Sagemaker to build your own AI or machine-learning models for data analysis. In any case, ensure you have expert oversight to interpret AI-generated insights and balance machine analysis with common sense and human intelligence. 

Finding the Role of Artificial Intelligence in Business For Your Company

IDC predicts that by 2025, 35 percent of companies worldwide will use generative AI. Given their large market size, the percentage of companies using AI in North America and China will be even higher. For enterprises especially, your competitors are already exploring different use cases for AI. You simply must take advantage of this new technology.

But you can’t implement AI in your business without the proper infrastructure. AI and ML applications require scalable computing resources to handle large datasets and complex computations. With the appropriate infrastructure, these applications can operate smoothly without any performance issues. You can also iterate and deploy new models without worrying about system scalability. 

Redapt provides custom infrastructure solutions and technology consulting for organizations. We design and deploy secure and scalable infrastructure that optimizes AI application performance. We assess your current infrastructure, identify gaps, and recommend improvements or new technologies that align with your specific requirements.

If you are thinking about deploying AI to improve your processes, book a clarity call with one of our experts today.