2024 AI Business Predictions

Technological advances, surging investments and the competition for talent are all working toward one thing: In 2024, artificial intelligence (AI) will start to fundamentally change how business gets done. It will impact how companies grow revenue, conduct everyday operations, engage customers and employees, build new business models, and more. 

Seventy-three percent of US companies have already adopted AI in at least some areas of their business, according to our 2023 Emerging Technology Survey — and generative AI (GenAI) is leading the way. One year after ChatGPT hit the market, more than half of the companies we surveyed (54%) have implemented GenAI in some areas of their business.

GenAI has made AI remarkably accessible and scalable. A single GenAI model can, with a little customization, work in many business functions and across lines of business. Whether you’re a CEO or a software developer, a tax leader or a product designer, by the end of 2024, AI will enable you to do your job in new, more powerful ways.

We’ve been making AI predictions for seven years now. Based on this experience and our longstanding leadership in AI, we feel confident in making six new predictions for 2024. Some are already becoming full-fledged trends. Others are in the offing. All can lead to concrete actions that can create business value for many years to come.

PwC 2024 AI Business Predictions

The right AI choices will provide companies a significant edge

In 2024, many companies will find attractive ROI from GenAI, but only a few will succeed in achieving transformative value from it. GenAI may appear easy to use, and many cloud service providers are already embedding GenAI capabilities in their offerings. But realizing GenAI’s full potential requires more than just letting employees use new capabilities in enterprise applications, however powerful they may be. It requires taking advantage of GenAI’s capacity to be customized to your specific needs and its remarkable scalability — while also paying close attention to its potential risks.

One key is to avoid the use-case trap. If you use GenAI only in isolated instances, you’ll get only limited value. Instead, prioritize “patterns” that can scale. For example, GenAI’s capacity to draw insights from unstructured data (such as text) can help nearly every knowledge worker grow capacity and make better decisions. 

It’s important to provide workers with incentives to not just use the new technology but to use it to reimagine their jobs. Tech advances mean they can reinvent their work by finding ways to deploy and customize GenAI to automate some tasks and augment the rest. As the technology keeps improving and costs keep dropping, leadership will have some reimagining to do. Once GenAI cuts the cost of digital labor, will you be able to pivot to new operations and business models?

What to do next

  • Think and go big. To realize transformative value from AI, license a private version of one of the many publically available models that cloud service providers offer. You can then deploy an AI factory to customize it and scale it to meet your unique needs. You may also need to reimagine how your business will run when GenAI has made knowledge workers 30% to 40% more productive.
  • Put people first. The biggest barrier to transformative value may be in getting your highly experienced people to engage with GenAI to reimagine the way they work. Provide incentives for innovation. When people redefine their roles with AI, reward them with new and greater opportunities.
  • Set priorities — methodically. AI can do so much, it can be hard to know where to focus. To set priorities, consider a methodology that analyzes the value of a process, its scalability, the hours currently spent on it and the nature of the data available to support it.

GenAI will redefine the work of leaders as much as employees

No one yet knows the long-term impact of AI on overall employment, and 2024 will still be too soon for definitive answers. But AI will start to change how almost everyone — especially those at the highest levels — does their jobs. Whether in the C-suite or on the shop floor, people who know how to use AI will outcompete those who don’t. There’s long been talk about the need for AI skills in the workforce, and it’s true: Employees need skills, guardrails and incentives to use AI responsibly.

But managers face even bigger challenges. Besides learning how to use AI responsibly, middle managers will need skills to oversee and assess teams in which AI agents do much of the work. Functional leads will have to understand how AI can not just augment processes but replace them. The C-suite will have to take the lead on AI-native operations and business models. Few leaders today have both organizational and AI knowledge — and closing this gap will be critical.

What to do next

  • Be human-led and tech-powered. As you work to close skills and vision gaps in leadership teams and your broader workforce, keep your eyes on the prize — to make people more valuable. Deploy AI so that it will grow your workforce’s capacity for high-value work and complex, data-driven decisions.
  • Unleash your talent. With the right incentives, skills and guidelines, any knowledge worker in your organization could use GenAI to automate or augment their work. They could identify new ways of doing things with GenAI to drive speed, scale and lower cost.
  • Lean on AI natives. A growing number of people in the workforce — including many college grads and entry-level workers — are already accustomed to using GenAI for daily tasks. Have a plan to amplify these AI natives’ skills and mindsets rather than burying them under old-fashioned processes.

The moment of truth for trust in AI is coming

In 2024, AI will be an essential part of how your people interact with data, stakeholders and each other. Trust in AI will be critical — and that means more than just compliant, secure systems. It means deploying the right solutions for the right situation with the right data, policies and oversight to achieve relevant, reliable results. That requires responsible AI, an enterprise-wide approach and set of practices. Responsible AI can help everyone who develops and uses AI do so with an eye toward building trust.

This will be the moment of truth for responsible AI for two reasons. As GenAI takes on more work — writing financial reports, automating parts of software development, analyzing proprietary data for go-to-market strategies and so on — mistakes could have wide-reaching impacts, including stalling transformation initiatives. We also expect potential AI risks to attract public attention. Policymakers are already taking action, and we may see a GenAI-related crime, such as a political deepfake, hit the headlines. Many GenAI vendors now offer to indemnify customers for potential copyright infringements. That reduces one risk — but trust in the outcomes of your AI systems are still your responsibility.

What to do next

  • Don’t repeat old mistakes. Too many early digital initiatives began without embedding trust into their foundation. It was then necessary to try to close gaps later and reverse engineer platforms and products. A better way is to embed trust from Day One, starting with strategy and design. AI initiatives will advance faster and be more cost effective if you embrace responsible AI from the get-go.
  • Don’t start from scratch. AI’s ability to augment or automate even high-value tasks and decisions requires action to manage new risks, but that doesn’t require reinventing the wheel. Build on established governance, cybersecurity, privacy and compliance programs as you establish responsible AI in your organization.
  • Bring in the big guns. Since AI will likely permeate the organization, all of the C-suite should be involved in its responsible use. Make sure that every senior leader knows their role (and their function’s responsibilities) in helping AI systems earn trust.

GenAI will be the ‘missing link’ for data

GenAI can help you turn more data into more value more quickly — giving many data initiatives an attractive cost/benefit ratio that they may have lacked before. It can scan, read, summarize, translate, analyze and troubleshoot even highly unstructured data that’s trapped in presentations, strategy papers, customer logs and the countless other documents that define your organization. GenAI can, in other words, answer one of the greatest challenges for many companies: processing and creating intelligence around large sets of complex, unstructured data.

Even so, GenAI can’t do it all. It still requires you to digitize data, move it to the cloud, enable GenAI to access it, assure reliability and compliance, and manage risks. Executives increasingly understand just how important this data modernization work is. In our survey, 44% of business leaders said that their companies are planning to implement data modernization efforts in 2024 to take better advantage of GenAI.

What to do next

  • Make cloud your ally. GenAI (or any AI) can do more with data if it’s in the cloud. But for cloud to empower GenAI, your data will need authoritative sources, clarity on who has rights to use it, “pipelines” to continuously update and distribute it, and effective governance, cybersecurity, compliance and privacy practices.
  • Don’t drown in it. You don’t want to “GenAI-ify” what is today an all-too-common problem: too much irrelevant data. Instead, assess the value of the data you have or could acquire. You may also wish to eliminate unnecessary data that could contribute to compliance or security headaches.
  • Cultivate data stewards. To help GenAI turn data into value, teach data owners to evolve their roles from data managers to data stewards. With data a valuable product, its stewards should constantly consider its quality and its usefulness to others in your organization. The more that data is used, the greater its potential to create value.

GenAI will transform transformation

GenAI is about to make transformation more urgent in more places — and more achievable. Its ability to make sense of unstructured data, when combined with cloud, can accelerate nearly any data-related transformation initiative. It can also take transformation where it has not gone before and help you leapfrog several stages.

GenAI can often handle complex tasks and processes that were previously out of reach in finance, tax, legal, IT, compliance and other departments. It can, for example, help you more efficiently meet new Pillar II tax reporting requirements. More generally, soon you may no longer need to upgrade common enterprise applications. Instead, you could move them to the cloud, where the applications themselves and customized GenAI modules will continually evolve to meet your changing needs.

What to do next

  • Make everyone a transformation lead. Wherever knowledge work is being done, GenAI can transform it. Every knowledge worker in your company should be considering how GenAI could transform their roles — and get started on it.
  • Outsource and offshore less. As part of GenAI-led functional transformation, consider bringing key business processes back in house. Customized GenAI workflows can cost-effectively tackle many frequently outsourced and offshored tasks in tax, finance, software development, HR and elsewhere.
  • Cover all the bases. To increase AI investments, consider not just the tech, the costs and the outcomes but also factors such as sustainability, industry-specific regulations and your competition. You may need new ways to measure value, as AI augments even complex, high-value knowledge work and decisions.

GenAI will give rise to new classes of products and services

How businesses develop new offerings and revenue streams is changing dramatically. Building new processes, developing new products and services, and creating new environments for customer engagement — all of these are becoming “no code” activities thanks to GenAI. Your domain specialists and creatives will be able to work directly with AI and data (which will be presented in helpful, easy-to-understand ways), provided you have robust governance and oversight. 

We’re already seeing cloud-based enterprise applications incorporate more GenAI capabilities, but this is just the start. Soon, enterprise applications will have GenAI not as an add-on but as the core. These AI-based applications will be faster, more agile and more customizable than anything that has come before. We’ll also see products and services that result from GenAI’s convergence with other technologies, including machine learning. Extended reality devices, IoT networks, machine learning processes and others will soon be reliant on GenAI.

What to do next

  • Don’t adapt, replace. To realize AI’s potential to create new products and services, don’t just integrate it into existing workflows and technology tools. Create new ones that can turn ideas into concrete outcomes, quickly and cost-effectively.
  • Upgrade your tech foundations. To use AI everywhere, tech architecture and enterprise data models will have to change. New hardware and software will increasingly have AI at its core, potentially multiplying the value of these efforts.
  • Keep watch. As AI becomes an integral part of more and more of your daily operations, other technology applications, and new products and services, oversight and governance will be more important than ever.

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