Forrester Research forecasts AI-driven finance shift in 2027, with 50% surge in AI banking M&A

AI-driven finance to reshape banking by 2027

Forrester Research’s report, Predictions 2027: Banking and Investing, forecasts a fundamental shift from digital banking to AI-driven finance within the next three years. The report, based on data from 915 respondents across the U.S., U.K., and Canada, expects AI agents and operating models to redefine how financial firms acquire customers and deliver advisory services.

The transition, however, will not be smooth. The report highlights that only 5% of banks will succeed in their AI transformation programs, even though one-third have announced such initiatives. Many banks are expected to rebrand existing AI projects as "AI-native" without making substantive changes to their operational infrastructure.

Legacy systems and M&A activity shape AI adoption

A key obstacle to scaling AI in banking is the retention of legacy processes and operating models. According to Forrester, these outdated systems will prevent most banks from fully deploying AI at scale. David Kennedy, CFO of Dell Technologies, emphasized this challenge in a discussion with SiliconANGLE, stating that banks are racing to determine whether AI will merely enhance their existing operations or fundamentally transform them.

To bridge the gap, the report predicts a 50% increase in M&A activity focused on AI banking technology in 2027. Banks will seek to acquire proprietary data assets, governance capabilities, and AI-native orchestration tools. This trend reflects the need to navigate hybrid computing environments, which combine on-premises and cloud-based infrastructure.

AI assistants and proprietary data as differentiators

Forrester forecasts that 40% of banks will launch AI assistants in 2027. However, the report cautions that these tools will only deliver meaningful value if backed by proprietary data, decisioning frameworks, and specialist expertise. Without these, AI assistants risk becoming generic solutions that fail to differentiate banks in a competitive market.

Suresh Andani, Corporate Vice President of Compute and Enterprise AI at AMD, and John Roese, Global Chief Technology Officer at Dell, have previously highlighted the importance of scalable infrastructure for AI adoption. Their earlier comments align with Forrester’s emphasis on governance and hybrid environments as critical enablers for AI-driven finance.

Enterprise AI infrastructure as an enabler

This report follows earlier collaborations between Dell and AMD to develop rack-scale AI infrastructure for enterprises. Their joint efforts aim to simplify server consolidation, modular deployment, and performance optimization for AI training, fine-tuning, and inference. Such infrastructure could address the hybrid computing needs identified in Forrester’s predictions.

Separately, AMD and Cloudera have emphasized the importance of "right-sizing" AI infrastructure to align technical performance with business value. Their work underscores the challenges banks may face in scaling AI without overhauling legacy systems or investing in compatible hardware and software solutions.

What this means

Lazyfounder analysis — our interpretation, not reported fact.

For founders and operators in fintech, banking, and enterprise AI, Forrester’s predictions signal both opportunity and urgency. The projected 50% increase in AI banking M&A suggests a consolidating market where proprietary data, governance, and AI-native tools will command premium valuations. Startups offering solutions in these areas—particularly those that bridge legacy systems and hybrid environments—may find tactical acquisition or partnership opportunities.

The warning that only 5% of banks will succeed in AI transformation is equally significant. It suggests that most institutions will struggle to move beyond incremental improvements, creating a gap between leaders and laggards. For operators, this underscores the importance of testing whether their AI initiatives are truly transformative or merely rebranded enhancements. Meanwhile, founders building infrastructure for AI-driven finance—whether in compute, data governance, or orchestration—should focus on scalability and integration with legacy systems to remain relevant in a market where hybrid environments are becoming the norm.

Key takeaways

• Banks and financial services firms must decide whether AI will enhance existing operations or transform them entirely—rebranding alone won’t suffice.
• Proprietary data, governance, and AI-native orchestration will be critical for banks to scale AI effectively.
• The projected 50% rise in AI banking technology M&A signals opportunities for startups in AI infrastructure, data governance, and hybrid computing solutions.
• Failure to modernize legacy processes could limit AI deployment, creating a divide between leaders and laggards in the sector.

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