generative AI, GenAI

Generative artificial intelligence (AI) has made a huge splash in today’s digital market within the last year, with global organizations implementing new technologies into their operational workflows and business models. Through the rapid adoption of automated technologies, roughly half of today’s work activities could be automated between 2030 and 2060. If appropriately implemented to achieve viable business use cases, generative AI has the potential to unlock new business models, transform industries, reshape jobs and boost economic productivity.

Creating Trust and Transparency With Generative AI

A recent McKinsey Digital study states that generative AI and other technologies can potentially automate work activities that account for 60 to 70 percent of employees’ time today. This should come as no surprise, with the market scope for generative AI rapidly increasing to transform how businesses contribute to the current economic flow. Yet, generative AI is worthless if the data and assistance it provides to organizations worldwide is inaccurate, inconsistent or untrustworthy. A business cannot grow if it sits on a weakened foundation. A balance is required between traditional maintenance practices and implemented AI programs to ensure productivity and achieve business goals.

For organizations to build on useful customer or product data while protecting sensitive information, generative AI models need to systematically secure or delete vulnerabilities that lead to potential data leaks. Model governance solves the issue by controlling access, implementing policies and overseeing generative AI systems. This allows these systems to run smoothly and efficiently so that data remains privatized, accurate and trustworthy. Governance provides the checks and balances necessary for generative AI to grow a business from the inside out.

Building a Generative AI Model for Specific Business Needs

As AI technologies constantly advance, so do their capabilities within the business marketplace. While primary use cases may remain similar for most organizations, it’s important to establish a generative AI model that works for a business and its specific goals and needs. This means that it is imperative for developers to address the significant areas in marketing, distribution and consumption. McKinsey Digital predicts that generative AI’s impact on productivity potentially add trillions of dollars in value to the global economy.

Some of these targeted use cases to consider when creating a customized generative AI model include:

  • Customer care. Companies are looking to bring more automation, better customer experience and 360-degree feedback that will help improve productivity, revenue growth and better market insight. According to a 2022 BCG survey, 95 percent of global customer service leaders expect customers to be served by an AI bot at some point in their customer service interactions within the next three years.
  • Supply chain management. Generative AI models help “optimize supply chain processes, forecast demand and inventory levels, and predict potential disruptions.” Implementing an efficiently governed AI model trained on crucial data points for a business allows it to identify consumer patterns and trends that lead to an optimized supply chain and stronger decision-making.
  • Sales and marketing. Generative AI plays a major role in understanding customer behavior, buying patterns, and user personas to adjust the inventory and optimize marketing strategy based on customer demand. By providing an AI model with sales calls, reports, and all relevant data, it uses these insights to inform an organization’s strategy, interactions and creative sales content.
  • Information technology (IT). Generative AI tools are implemented to produce automated code generation, documentation and conversation. Companies can also rely on generative AI insights for quick user interface (UI), application development and synthetic data creation. This saves business users and professionals valuable time to focus their efforts elsewhere.
  • Human resources (HR). AI models assist with onboarding, verifying documents, conducting interviews, training, generating automated recruitment responses and drafting interview questions. It increases personal productivity by automating internal review processes and feedback while streamlining repetitive activities. In a recent Littler study, nearly 70 percent of organizations that claimed to be deploying AI and data analytics reported using AI and analytics tools in the recruiting and hiring process.

Overcoming Generative AI Challenges to Reap the Benefits

Generative AI value, capabilities and ethics are important when considering the return on investment on IT AI spending. While the market size for generative AI is approximately $44.9 billion worldwide, Statista predicts it will reach roughly $207 billion by 2030. Generative AI shows no signs of slowing in today’s financial and economic landscape.

While generative AI indeed serves multiple purposes for the global marketplace, it is essential that enterprises avoid an overreliance on these evolving technologies. Traditional IT maintenance and enforced governance standards are necessary for eliminating and overcoming the multiple challenges that come with AI models to reach those core benefits that attract organizations. Certain AI flaws require a human touch. Primary challenges include:

  • Quality control. To ensure that generated AI content aligns with the values and needs of a business, there must be human editing and reviewing to confirm the foundational data is accurate, unbiased, and trustworthy.
  • Ethical concerns. While AI can quickly produce helpful content, it can also create harmful or misleading content. The ethics behind content creation are only enforced and maintained by the business.
  • Regulatory compliance. Creating necessary regulations for data and content usage demands complete compliance. Generative AI requires consistent monitoring by users to ensure they are upheld.

The relationship between business professionals and an integrative AI model is often a two-way street. The benefits can outweigh the mistakes and uncertainties that come with human error. Some of the benefits include:

  • Content generation. Generative AI reduces the time and effort required to produce high-quality text, images and even code.
  • Cost savings. Automation and efficiency can significantly reduce costs for several industries, especially those reliant on costly manual labor.
  • AI assists in the overall ideation process, creating new ideas that facilitate innovation. This pairs greatly with traditional brainstorming.

By fusing employees’ efforts with implemented AI modeling, businesses can create a perfect balance that patches the shortcomings on either side to further optimize growth, innovation and productivity.

Harvesting a Brighter Business Future

As generative AI technology evolves, it has the potential to grow and empower businesses like never before. Developers and organizations are discovering new capabilities to meet unique needs, carefully finding generative AI’s permanent role within the marketplace, and pairing it with traditional practices to ensure efficiency and transparency between business owners and their consumers. By acknowledging generative AI’s strengths and limitations, businesses can better understand how to leverage new technologies for growth and innovation. Business professionals around the globe have a responsibility to educate themselves on the full spectrum of what integrated generative AI models have to offer. Progress and innovation come from collaboration and experimentation.