How AI Will Impact Business Decisions At Various Levels, Explain Researchers

AI Will Impact Business Decisions At Various Levels

ChatGPT's explosive global popularity has given us AI's first true inflection point in public adoption

While AI is not a new technology – companies have been investing heavily in predictive and interpretive AI for years – the announcement of the GPT-3.5 series from OpenAI in late 2022 captured the world's attention and triggered a surge of investment in generative AI. As a result, IDC expects worldwide spending on AI solutions will grow to more than $500 billion in 2027. In turn, most organizations will experience a notable shift in the weight of technology investments toward AI implementation and adoption of AI-enhanced products/services, claims International Data Corporation (IDC).

"ChatGPT's explosive global popularity has given us AI's first true inflection point in public adoption," says Ritu Jyoti, group vice president, Worldwide Artificial Intelligence and Automation Market Research and Advisory Services at IDC. "As AI and automation investments grow, focus on outcomes, governance, and risk management is paramount."

IDC's FutureScape 2024 research focuses on the external drivers that will alter the global business ecosystem over the next 12 to 24 months and the issues technology and IT teams will face as they define, build, and govern the technologies required to thrive in a digital-first world.

A closer look at IDC's top ten predictions for artificial intelligence, GenAI, and automation reveals the following:

Tempering GenAI's Risks: Accelerated efficiency and catastrophic risk are the two sides to the shiny new GenAI coin. To reduce the risks, cloud and software platform providers will bundle GenAI safety and governance packages with their primary services to add value and differentiate their offerings.

Diverse Regulatory Requirements: Efforts to regulate the deployment and development of AI systems will vary across regions and countries. These diverse regulatory requirements are likely to result in organizations taking more phased approaches to AI rollouts, which will also increase time to value.

Conversation as the Standard UI: Conversation is already emerging as the standard user interface (UI) for both enterprise and consumer applications and solutions. These conversational AI interfaces will significantly affect customer engagement, sales, marketing, and even the IT help desk.

The Focus Shifts to Outcomes: As their understanding of automation matures, project sponsors have shifted from a technology focus to an outcomes mindset where they require tangible proof of value delivered for their investments measured by KPIs aligned with business and financial outcomes.

GenAI-based Tools Automate Software Quality: Because of the value GenAI brings to automated testing, IDC expects it to quickly change the landscape of software testing, with vendors becoming capable of producing a significant percentage of tests to decrease manual efforts and improve test coverage leading to better code quality.

GenAI Transforms Application Modernization IT Services: Increased utilization of AI in application modernization IT services can streamline efficiency, enhance services delivery speed, and bolster IT services margins.

Bringing AI to Knowledge Discovery: The latest advances in generative AI have prompted a surge of demand for capabilities such as natural language question answering and conversational search to support self-service knowledge discovery.

Monetizing GenAI: While technology is a source of advantage, it is the business model that will help businesses monetize generative AI and drive lasting competitive advantage. By 2024, 33% of G2000 companies will exploit innovative business models to double their monetization potential of GenAI.

AGI on the Horizon: Multiple groups are working toward Artificial General Intelligence (AGI) and companies will be experimenting with AGI systems by 2028. As it progresses, AGI will be transformative, impacting everything from the labor market to how we understand concepts like intelligence and creativity.

Chip Priorities Change: Until AI workloads that require the offload of tasks from server processors to accelerators standardize on algorithms and software stacks tuned to server processors, purchase of accelerators (GPU, FPGA, and AI ASIC and ASSP) will eat into purchase of server processors (CPUs).