This concise research note produced independently and without vendors’ influence, spans over eight pages. Offering brief insights into digital transformation trends, it details how they might impact enterprise software ecosystems. It talks about both macro factors such as economic growth, inflationary conditions, and geopolitical tensions. Then, It provides insights into micro factors such as mergers and acquisitions — or factors such as product and corporate strategy of different enterprise software vendors.

To create this report, we actively monitor enterprise software companies impacting digital transformation trends through a variety of sources. These sources include surveys, interviews, and panel discussions. They also include proprietary databases, customer inquiries, industry reports, vendor briefings, and user forums. This guide is specifically designed for digital transformation executives building a case for transformation in their organizations — or enterprise software product executives planning their R&D spend. This guide will also be helpful for digital transformation practitioners when planning their careers.

Questions this report will answer:

What structural advantages do AI-enabled business models create that traditional models cannot easily replicate?

Which incumbent capabilities are most vulnerable to disruption from AI-driven speed, cost, and quality gains?

How should organizations prepare for declining organic search traffic as AI displaces traditional discovery channels?

What does it mean for digital channels to shift from engagement layers to fulfillment layers?

How will geopatriation influence vendor selection, architecture decisions, and long-term technology risk?

Are regional technology providers positioned to gain durable market share from global incumbents?

How do AI-native enterprise systems fundamentally differ from legacy ERP architectures?

What legacy constraints become irrelevant in AI-native platforms, and which persist?

How can agentic layers deliver continuous improvement without large-scale workforce reskilling?

Where does the agentic layer meaningfully substitute for a full system replacement—and where does it not?

How should leaders balance short-term labor displacement risks against long-term productivity gains from AI?

Will workforce disruption constrain digital transformation budgets in the near term?

How much AI investment are legacy vendors realistically willing to make in aging platforms?

What signals indicate that AI-driven M&A is reshaping enterprise software categories rather than merely adding features?

How can organizations avoid value erosion from premature or poorly scoped AI initiatives?

What governance mechanisms are needed to manage AI-driven, consumption-based pricing volatility?

How will AI-augmented implementation tools compress upgrade and adoption timelines?

When does embedding business logic in a data fabric outperform replacing transactional systems?