The second of our 2024 AI Predictions Mini-Series expects enterprises to commit to significant investment in generative AI in 2024 and speculates on the dangers of ignoring the opportunity of AI at the board level.
What we’re expecting in 2024:
– A significant shift in corporate spending habits will occur, with at least 10% of budgets being earmarked for AI initiatives.
– CEOs and business leaders who choose not to invest in AI projects may risk the dangers of AI anyway, lose the talent war, and miss the opportunity for revenue growth and cost-savings.
– This shift reflects the growing recognition of AI as a transformative force across industries.
AI Spending to Grow Substantially
ChatGPT is now a year old, and its launch and the progress of its private and open-source generative AI competitors cannot be ignored by enterprises and industries.
Despite the concerns surrounding generative AI’s accuracy, data protection, security, and ethical concerns, companies are adopting generative AI at pace to leverage the benefits.
A McKinsey study estimates generative AI’s benefit to productivity could add between $2.6 billion and $4.4 billion in value to the global economy annually. The report also estimates AI could enable labor productivity growth of between 0.1% and 0.6% every year up to 2040.
McKinsey further says that generative AI can be deployed immediately for high impact on business problems, particularly for companies in sales and marketing and customer operations. There is a general feeling that generative AI’s capabilities are good enough to put them to work despite inaccuracies and other weaknesses.
In its Technology, Media, and Telecom Predictions 2024, Deloitte declares that “generative AI gains boardroom momentum” and says that generative AI will move from a “concept buzzing in enterprise circles to a reality reshaping industry.”
This report further predicts that generative AI will become integral to “nearly all” enterprise software offerings in 2024. Enterprise software companies are expected to experience a revenue uplift of $10 billion by the end of 2024 as a result. These software companies are figuring out their pricing, but through them, enterprises in every industry will grow their generative AI deployment.
Gillian Crossan, Global Technology Sector Leader, Deloitte & Touche LLP, says:
“In 2024, the technology sector is set to cross a new threshold with generative AI taking center stage. Once considered a concept, gen AI is now pivoting from the periphery to becoming a bedrock of enterprise innovation.”
Deloitte adds that enterprises are increasingly opting to train generative AI models on their proprietary data to maximize productivity and cost-efficiency but also to mitigate the risks associated with public data sets. It believes that the share of enterprise AI spending on generative AI may grow by 30% in 2024.
Forecasts are certainly mixed for AI spending in 2024. Buy Shares expects global AI spending to surge by 120% and hit $110 billion by 2024. IDC data points to life sciences and retail leading AI spending, with 67% of organizations adopting AI at scale and a further 33% launching AI pilots.
Gartner feels that generative AI impacts will be felt more in 2024 but that overall, worldwide IT spending will grow by 8% in 2024. John-David Lovelock, VP Analyst at Gartner, says:
“Organizations are continuing to invest in AI and automation to increase operational efficiency and bridge IT talent gaps. The hype around GenAI is supporting this trend, as CIOs recognize that today’s AI projects will be instrumental in developing an AI strategy and story before GenAI becomes part of their IT budgets starting in 2024.”
Morgan Stanley predicts that AI will represent 11% of healthcare spending budgets in 2024, up substantially from an allocation of 5.5% in 2022 and fuelled by the arrival of ChatGPT. The bank notes that 94% of healthcare companies already employ some form of AI or ML.
Spiceworks and Aberdeen Strategy & Research surveyed 883 IT professionals. They discovered that 66% of companies are planning to increase their IT budgets in 2024, with 57% of businesses planning to adopt AI within the next two years.
Two Consequences of Ignoring Generative AI
EY, in a report on a CEO survey from July, declared that “no corporate leader can ignore AI in 2023” and that four out of five CEOs have integrated AI, already seeking the promise of a generative AI strategy to accelerate transformation and provide strategic advantage. McKinsey discovered that a third of employees say their companies already use generative AI.
From the Deloitte report, Crossan says that regulatory developments will serve as either obstacles or catalysts for change:
“It’s imperative for industry leaders to navigate these complexities, aligning regulatory compliance with creativity, to fully unlock the transformative power of gen AI.”
There could be two key dangers of not investing in generative AI in 2024:
1. Non-adopters are outpaced by peers and lose valuable talent
We’ve all heard the promise of generative AI to provide a competitive and strategic advantage and deliver cost savings across business operations. If leaders adopt a wait-and-see approach, they may miss the opportunity curve and lose out to industry peers.
There’s a secondary aspect here: AI automates monotonous, repetitive tasks for employees, leaving them more time to focus on problem-solving and creativity.
An estimated 77% of employers struggled to fill roles in 2023. The talent shortage persists, and there’s a likelihood the best employees will gravitate toward roles where they can work side-by-side with AI taking up less attractive daily tasks.
2. AI-skeptics may miss the opportunity to mitigate risks early with safer solutions
Generative AI is certainly not without risk. These risks include hallucinations and incorrect output, privacy and data protection concerns, bias, and ethical concerns, to name a few.
Even if, at the board level, a decision is made not to invest in AI, there’s a chance that employees will still use readily available generative AI tools to help them in their work, potentially incorporating some of these risks into their outputs. The case of the Samsung employees who leaked confidential data to ChatGPT illustrates how this danger can play out. Writing for LegalDrive, Jim Tyson says most workers use AI, usually without company safeguards.
Companies that seek out generative AI solutions that meet productivity aspirations but specifically mitigate AI’s risks to their business, whether off-the-shelf or proprietary, can take an opportunity to regulate the use of AI. Deploying corporate-chosen systems with the proper guidance and training can engage, motivate, and inspire employees and allow firms to leverage the benefits of generative AI.
Frequently Asked Questions
How should enterprises choose which AI projects get budget first in 2024?
Budget first the AI project that can launch in 60 to 90 days, uses proprietary data, and has a named owner, baseline metric, and payback target. When funds are tight, back departmental pilots before enterprise rollouts unless enterprise ROI is already proven.
McKinsey has singled out customer operations and sales and marketing as near-term generative AI value pools. In practice, that means funding agent-assist, proposal drafting, or internal knowledge search before open-ended innovation labs. Use a simple budget screen: speed to production, proprietary data readiness, adoption plan, compliance risk, and measurable savings inside the current budget cycle. For customer-facing AI, a packaged deployment such as CustomGPT.ai, Microsoft Copilot, or Glean often deserves budget before a custom build unless the custom path has a stronger business case. BernCo reported 4.81x ROI from its AI rollout, which is the bar finance teams should seek before scaling.
Why are companies increasing AI budgets even when leaders worry about accuracy and risk?
Companies are increasing AI budgets because leaders now treat generative AI as a measured business investment, not a wait-until-perfect technology. Deloitte’s 2024 State of Generative AI in the Enterprise finds that many organizations are moving it from experimentation into board-level planning despite ongoing accuracy, security, privacy, and governance concerns.
Many teams are not simply spending more overall. They are reallocating budget from broad experimentation toward enterprise rollouts, departmental pilots, and governed chatbot deployments that can show near-term value. Buyers usually approve spend when three things are clear: the use case is narrow and business-specific, governance controls are defined, and the expected productivity or revenue gain is large enough to justify the risk. That is why companies keep funding tools such as ChatGPT Enterprise, Microsoft Copilot, or CustomGPT.ai. At MIT, a published deployment reports support in 90+ languages with zero hallucinations, showing why tightly scoped, well-governed AI projects often win budget approval first.
What is the real cost of delaying enterprise AI investment by a year?
Delaying enterprise AI investment by a year usually costs companies in three ways: missed productivity gains, slower revenue lift, and higher catch-up costs. It also widens the talent and execution gap as competitors train teams and redesign workflows first.
McKinsey’s 2024 global survey found 65% of organizations already use generative AI regularly, up from about one-third in 2023, so waiting now means entering after peers have already learned where AI pays off. In plain terms, if 1,000 employees could save one hour a week at a $50 loaded hourly cost, a one-year delay leaves about $2.6 million in unrealized productivity alone. VdW Bayern reported a 50 to 60% task reduction in some workflows. For budget-constrained teams comparing Microsoft Copilot, Google Gemini, or CustomGPT.ai, the risk is not just spending later, but spending more later because a skipped pilot today can turn into a larger, more expensive rollout next year.
Why is AI budget allocation a board-level issue rather than only an IT decision?
AI budget allocation is a board issue because it sets revenue priorities, cost-saving targets, compliance exposure, and acceptable risk thresholds across the business; IT should execute within those guardrails. It is not only a tool-selection decision.
For many companies, the board must decide whether AI budget goes to enterprise-wide rollout, departmental pilots, custom builds, or chatbot deployments, because those choices determine scale, payback period, and governance. This matters even more when budgets are capped or phased: leaders must choose a low-risk pilot with faster proof or a broader rollout with higher upside and heavier controls. That governance standard is reinforced by the NIST AI Risk Management Framework and ISO/IEC 42001, which treat AI oversight as an organizational responsibility, not just an IT purchase. In practice, boards may compare spend across Microsoft Copilot, OpenAI, or CustomGPT.ai. BernCo reported 4.81x ROI, which is why boards focus on portfolio return, not just implementation.
Do non-tech organizations need a meaningful AI budget in 2024, or is this mainly a software industry trend?
No. Most non-tech organizations do not need a massive standalone AI budget in 2024, but they should reserve funds for a pilot, vendor add-ons, integration work, governance, and staff training.
Deloitte’s 2024 outlook says generative AI will be embedded in most enterprise software, so hospitals, manufacturers, retailers, and professional-services firms should expect AI costs to show up in current vendors, not just in separate software projects. In practice, many buyers face hard spending ceilings and real uncertainty about whether an enterprise-wide rollout, a departmental pilot, a custom build, or a chatbot deployment will justify the cost. A simple rule is to fund one departmental use case first, measure time saved or deflection, then expand only if results hold. For example, VdW Bayern reported a 50 to 60 percent task reduction on selected workflows. Also watch for add-on pricing from Microsoft Copilot or Salesforce Einstein, which can turn small pilots into recurring license costs.
How should leaders measure AI ROI before they approve a larger budget?
Before approving more AI budget, compare ROI across enterprise rollouts, departmental pilots, custom builds, and chatbot deployments. Scale only when the option being funded pays back its added implementation and monthly cost within a defined period, often 6 to 12 months, in the exact function it will support.
Budget allocation uncertainty is often the real blocker, so use go or no-go criteria: labor hours reduced, service efficiency improved, or sales-support output increased enough to beat the next-best option on cost per ticket, case, or qualified lead. McKinsey estimates generative AI could add $2.6 trillion to $4.4 trillion annually in value, but leaders should fund expansion only after a pilot proves measurable gains such as faster customer-response handling. BernCo reports 4.81x ROI, which is stronger evidence than adoption alone. If Microsoft Copilot, ChatGPT Enterprise, or CustomGPT.ai cannot clear those thresholds, do not expand budget yet.