While artificial intelligence (AI) has long been considered the future of business, it is clear the future has arrived. AI tools are now vital to the scalability and growth potential of most organizations. This strong commitment to using AI has been demonstrated in several surveys. First, Accenture reported that 84 percent of business leaders believe AI will help them gain a competitive advantage. In a PWC survey, 75 percent of responding executives indicated that AI will enable them to make better business decisions.
Organizations in various industries, including financial services, entertainment, retail, aviation, manufacturing, marketing, and more, use AI to solve transformative problems, increase profitability, and boost growth. Two of the most common benefits of AI usage are increased return on investment (ROI) and reduced errors due to AI’s ability to analyze vast amounts of data and perform repetitive tasks without making fatigue-related mistakes. With AI, more work gets done in less time and with fewer inaccuracies—an ideal recipe for businesses looking to initiate scalability.
Revolutionizing the way businesses scale
Growth, particularly rapid growth, has long been a dangerous process for businesses. It often introduces disorganization, inefficiency, and even outright chaos into an organization. Now, AI helps companies gain tighter control of their scaling efforts through increased automation, more accurate analysis, and more detailed intelligence. Businesses are using AI tools, like ChatGPT, Drift, JustCall, Second Nature, Crystal, XiQ, Common Room, Nektar, Clay, Appollo, Copy.ai, and Jasper, to positively impact everything from sales to lead management to data generation to copywriting, and more.
A specific way AI is helping businesses scale more efficiently is by reducing human error. Workload and personal issues can cause humans to become fatigued and distracted, but AI doesn’t get tired or lose focus. For example, finding one error in millions of a company’s records is tedious for humans, while AI can accomplish the task quickly and easily.
AI also helps businesses scale by improving customer engagement. AI chatbots can work 24/7 and handle all customer inquiries without getting stressed and irritable. AI is also capable of analyzing vast amounts of customer data and then using that data to automate and streamline interactions between companies and their customers. Thanks to AI, customers receive consistent communication and responsive support and are more likely to become repeat buyers. AI’s many benefits are why the IBM Global AI Adoption Index recently found that 77 percent of businesses are exploring the use of AI in their processes. Of those companies using AI, over 30 percent are reporting significant time savings.
How to track AI success
As AI use becomes more prominent, organizations want to ensure they get the most from their AI investment. Three key performance indicators (KPIs) that companies can use to judge the impact of their AI efforts are:
- AI model. By focusing on AI model quality, organizations can determine how their AI is performing regarding unknown datasets, new data training, and the mitigation of data errors. Common metrics used to assess AI model health include quality index, error rate, and performance.
- System environment. AI is only as effective as the information it is given and its operating environment. That makes environment-related components like data quality, systems quality, and collaboration efforts essential to AI success. Metrics to consider in determining the system environment include qualified data, volume of data, system performance, integration with various data sources, training data, and scalability. In addition, when an autonomous drone is used, it is critical to consider additional factors, such as the external environment (weather).
- Business value. One of the biggest keys to an organization’s AI success is to have a clear vision of the goals it wants to achieve through its use of AI. How does the organization define AI success? What value does it anticipate from using AI? For example, is the goal increased process automation, better customer experiences, increased sales, or time savings? Common metrics for assessing business value include adaptation rate, usage time, user experience/satisfaction, and failure rate.
Facing AI’s Biggest Implementation Challenge
AI implementation has a few challenges, including algorithm accuracy and bias, customer trust and acceptance, and integration difficulty. The biggest challenge for organizations introducing AI into their workflows is data security and privacy. Balancing the use of customer data for personalized services with data privacy regulations can be difficult. Regarding privacy, companies must now be aware of individual government regulations, such as the General Data Protection Regulation (GDPR) in the European Union (EU) and the California Consumer Privacy Act (CCPA) in the state of California. Tips for ensuring data security and privacy in today’s marketplace include implementing a company-wide, uniform privacy and security policy that covers all the company’s AI solutions and collection of human data. Companies also need to compose a clear, easy-to-read, and detailed disclosure statement that meets all applicable disclosure laws. Additionally, it is essential to execute state-of-the-art security measures to protect sensitive data. This includes biometrics, social security numbers, date of birth, behavioral data, and images. Ideally, companies should aim to have three to five layers of background checks for data access and an extra layer of security that restricts data accessibility geographically.
Data transparency is also critical. Organizations must know who is accessing data and be able to revoke access immediately. They need to be in complete legal compliance regarding the collection and selling of data with restrictions on buying and selling data from third parties. Finally, it’s vital for organizations to take steps to ensure that pictures and data from minors are not subject to an AI solution without a parent’s consent. By ensuring data security and privacy, organizations can fully utilize AI to sustain existing growth, solve transformative problems, and initiate scalability.
Accelerate growth and innovation with AI
AI is transforming life. Robots deliver food, autonomous vacuums clean the floor, chatbots address customer service requests, and Siri and Alexa instantly report the weather or play a favorite song. In business, companies use natural language processing (NLP) and natural language understanding (NLU) chatbots, interactive agents, voice and mobile assistants, content creation wizards, convolutional neural networks (CNNs) image detection tools, and email marketing automation to smooth operations and boost efficiency. AI-powered solutions are game-changers for leaders seeking to grow and scale their businesses.
By addressing privacy and security immediately, companies can more safely utilize AI to respond quickly to market changes, produce innovative, efficient solutions, and gain an advantage. It’s imperative that companies do not let their fear of AI challenges prevent them from embracing it, as this could cause them to quickly fall behind the competition. As American businessman, Mark Cuban recently said, “Artificial intelligence, deep learning, machine learning—whatever you’re doing if you don’t understand it—learn it. Because otherwise, you’re going to be a dinosaur within three years.”
About the Author
Vivek Singh is the executive vice president of business and delivery at PALNAR, where his role encompasses leading the innovation team, driving business scalability, and converting ideation into actionable project plans. This involves overseeing customer solutions, tech team building, development, automated testing, overall delivery, and the launch of final service products. His efforts include vetting and customizing AI and data analytics research products for in-house use, later extending these innovative solutions to customers. For more information, contact viai2003@proton.me.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.
