Moody’s warns slow AI adoption could erode margins and market share

The report outlines two scenarios: conservative and optimistic, and warns that firms slow to adopt AI could face structural margin erosion, market-share loss, and higher capital costs.

In conversation with Charleyne Biondi, AVP analyst at Moody’s Ratings, highlighted that AI adoption is increasingly a credit-relevant factor, not just a technological race. The credit impact will differ by sector and by the pace of integration: firms that adopt early may see structural efficiency gains, while slower adopters may face opportunity costs in the form of weaker competitiveness, particularly in sectors where new AI-native entrants may arise quickly.

The magnitude of these effects, however, largely depends on whether AI capabilities progress along a conservative or accelerated path. 

Conservative vs. optimistic scenarios

Moody’s developed two scenarios to model AI’s impact across sectors:

• Conservative: A slow rising tide of AI integration leads to better efficiency and margins without materially altering competitive hierarchies.
• Optimistic: Rapid AI advances create fast-moving credit effects with a far greater risk of competitive displacement for firms slow to adapt.

Biondi explained that the “main difference lies in AI capabilities, how quickly models and agents can scale to handle complex tasks. In the conservative scenario, capability gains plateau earlier, limiting productivity to incremental efficiency. In the optimistic scenario, capabilities compound faster, enabling broader workflow substitution and new revenue models.”

Sectoral winners and losers

Certain sectors are poised to capture disproportionate gains from AI deployment. According to Moody’s, high-gain sectors include finance, healthcare, insurance, and logistics, because they stand to benefit most due to their reliance on data and standardised workflows. Sectors that have a limited upside are utilities, oil & gas, pharmaceuticals, and heavy manufacturing because they face structural barriers to fast AI-driven transformation.

The report outlines that “sectors with high reliance on human labour and standardised workflows such as insurance or logistics will be able to cut costs by automating repetitive tasks with AI. Data-driven sectors, including finance, healthcare and automotives, will gain from enhanced analytics, predictive modeling, and decision support.”

In finance, for example, AI-powered fraud detection systems have already reduced false positives by over 50%, lowering compliance costs and loss ratios. However, not all industries are equally positioned. “These industries depend on long-lived physical assets, strict safety rules, and regulated business models, which slow down change,” Biondi said of utilities and heavy manufacturing. “AI will help in areas like planning, equipment maintenance, and supply-chain efficiency, but it won’t transform core production quickly.”

Disruption risk vs. upside potential

Moody’s defines disruption risk as the probability and severity of revenue displacement, margin compression, and stranded IT or capex as AI reshapes industry economics.

Biondi detailed: “In our Heatmap, the ‘lightning’ sign indicating disruption in certain sectors means that at least 10% of issuers in a given sector will face significant pressure from AI. Importantly, disruption is not uniform: in some industries, a few large incumbents, often controlling most of the market share, stand to gain from AI, while smaller competitors lose ground.” This dynamic means that even in sectors facing intense disruption, the aggregate credit effect may be positive if dominant players capture the upside.

Regional disparities in AI gains

Moody’s also highlights regional disparities that will shape the global distribution of AI benefits. Differences in innovation ecosystems, energy costs, regulation, and access to computing power will produce divergent credit outcomes.

“The most important regional difference is the strength of the local tech ecosystem, jurisdictions that master major parts of the AI value chain, from chips and compute to models and data, are best placed to capture long-term gains. The US is strongest, combining compute, model leadership, capital markets, and scale. The EU and UK have strong incumbents but face higher costs and tighter budgets. China has size and policy support but is constrained by limited access to advanced chips. The Gulf states are investing heavily and scaling quickly, yet remain reliant on foreign providers—leaving sovereignty risks.”

Boards and investors: Underestimating the real risk

Despite growing awareness, Moody’s warns that many boards and investors still underestimate the financial implications of slow AI adoption. “Awareness is still uneven. Many focus on compliance or reputational risks when it comes to AI adoption, but often underestimate the opportunity cost of slow adoption.”

Another overlooked risk is the growing transfer of value to AI infrastructure providers. “If agentic AI evolves to automate entire workflows, this trend will intensify, concentrating bargaining power and credit exposure in the hands of a few infrastructure players, while reducing the share of value retained by issuers and companies implementing these systems.”

Moody’s message is clear: AI adoption has become a material factor in long-term competitiveness and credit profiles. Firms that fail to act decisively risk falling behind in a landscape where value is increasingly captured by those who build, own, and scale AI infrastructure. In the race to 2030, the margin for delay is narrowing. The question is no longer if AI will reshape industry economics, but how fast, and who will lead.