Against the backdrop of global artificial intelligence (AI) spending that is projected to reach US$644 billion in 2025 — which would be a 76.4% increase from 2024 according to Gartner — it’s clear that organizations view AI as existentially necessary. But what specifically are enterprises thinking about when they invest in AI?
We decided to partner with Forrester to find out. We conducted a double-blind study of 400 senior decision-makers responsible for AI and enterprise cloud strategy. Respondents included directors, vice presidents, and C-level executives from the technology, financial services, media, retail, manufacturing, and healthcare industries.
The goal of this research was to uncover how enterprises are approaching AI workflows and AI tools in the near and long terms, particularly for customer-facing applications for which expectations are the highest. The State Of Enterprise AI: Gaining Experience And Managing Risks (a Forrester OSNAP report) revealed that AI is now a strategic imperative across industries and geographies.
To understand why these results matter, let’s unpack what’s driving them, why adoption looks the way it does, and what organizations and development teams can do next to stay ahead.
AI is defining the competition
Forrester’s data shows that 76% of enterprises are applying AI solutions and apps to improve customer experience (CX), 71% to strengthen retention, and 76% to enhance operational efficiency. These are the core drivers of revenue growth and competitive advantage.
Organizations that successfully deploy AI are better positioned to:
- Differentiate on customer experience
- Capture and protect market share
- Accelerate innovation cycles
- Unlock operational leverage
- Future-proof against disruption
Differentiate on customer experience
Personalized services or recommendations, faster service with automation, and intelligent service resolution directly improve CX, which Forrester identifies as the number one driver of AI investment. In crowded markets, the quality of customer interactions is the deciding factor in loyalty and retention.
Capture and protect market share
By embedding generative AI, and other AI capabilities into the customer journey, companies create stickier experiences that are harder for competitors to replicate. This not only increases net retention but also raises switching costs for customers.
Accelerate innovation cycles
Firms using AI can bring new products and services to market faster, especially when incorporating advanced capabilities like procedural content creation, visual search, and predictive analytics. In industries where speed to market correlates with share gains, this is a decisive edge.
Unlock operational leverage
AI-powered efficiency, whether in automating processes or augmenting staff skills, translates to lower costs, faster scaling, and the ability to reallocate efforts toward higher value work.
Future-proof against disruption
As Forrester notes, enterprises are measuring AI success not only by efficiency but also by its impact on long-term revenue and customer lifetime value. Organizations that hesitate risk being outpaced by competitors who integrate AI as a foundational capability.
From a developer’s perspective, this shift means AI is now embedded in the critical path of business outcomes. Code that powers AI directly influences metrics like customer lifetime value and net retention. That makes reliability, scalability, and ethics in AI design more important than ever.
Proven use cases and the next wave of innovation
Forrester highlights that the most widely adopted AI use cases are those that directly impact customer touchpoints: personalization (53%), automating customer service resolution (53%), and answering customer questions (52%). These use cases dominate because they deliver measurable, near-term results.
A recommendation engine that boosts conversions or a chatbot that reduces churn is far easier for leadership to justify than abstract AI initiatives with unclear return on investment (ROI). For businesses and their development teams, this emphasis translates into ongoing demand for systems that can scale, process real-time data, and minimize latency in customer interactions.
Technology choices will define winners and losers in the AI race
Businesses are getting on board to ride this powerful AI wave without wiping out. There are many stories of AI pitfalls and failures, so it takes a thoughtful and deliberate approach to be one of the success stories.
Often, choosing to start with established use cases for quick wins, like an AI chatbot, helps to build confidence in their AI strategy and build AI fluency.
In turn, this confidence opens the door to more complex or niche AI implementations that are more tailored to their business. These will tend to vary by industry. For example:
Retail and travel focus on personalization and visual search to enhance customer experience.
Financial services places higher value on automation and fraud detection.
Healthcare and life sciences adopt AI more cautiously, balancing innovation with compliance and risk.
Beyond these established use cases, experimentation is on the rise. Nearly half of organizations (46%) are piloting procedural content creation, 40% are testing visual search, and 37% are exploring facial recognition.
These experiments matter because today’s “side projects” can become tomorrow’s competitive baseline. Think about how mobile check-in in hospitality shifted from novelty to necessity. Businesses that encourage development teams to pilot emerging technologies are more likely to be prepared for the explosive rise in AI-powered tools and solutions.
Risk, security, and reputation: The hidden costs of AI
The study also highlights some sobering realities. A total of 63% of respondents cite security concerns as a top barrier, 55% worry about compliance, and nearly half (45%) fear reputational harm if AI fails to meet expectations.
From a technical lens, these numbers underscore the need for governance frameworks to:
- Secure data pipelines that prevent leakage
- Model monitoring to detect drift or bias
- Test protocols that mirror production conditions
But mitigating these risks goes beyond technical safeguards.
Businesses can benefit from thinking holistically about risk management. Security requires not only hardened infrastructure but also clear data handling policies and regular audits to catch vulnerabilities before attackers do. Compliance demands collaboration between development teams, legal teams, and regulatory experts to ensure that models meet evolving industry and regional standards. And reputational risk calls for transparency and accountability.
Companies that can explain how their AI systems make decisions and demonstrate fairness, are far less likely to face backlash when something goes wrong. In other words, enterprises are not simply writing models; they are deploying business-critical systems that reflect on the organization’s integrity.
Mitigating risk means combining engineering discipline with organizational accountability, so that the pursuit of innovation does not come at the expense of security, compliance, or trust.
Overcoming the integration hurdle
Enterprises are hedging their bets across multiple technology approaches. Forrester reports high adoption of cloud native technologies (81%), open source AI (72%), and managed AI services (77%). Yet 55% of organizations still identify technology and platform gaps as their top challenge.
This duality reflects a reality that developers know well: The tools exist, but integration is hard. Proprietary APIs, fragmented ecosystems, and uneven compliance requirements all slow progress. Organizations that build flexible, hybrid architectures that use open source innovation, managed services for scale, and edge deployments for low-latency experiences overcome this hurdle more often.
Companies that are aiming to deploy AI globally in the near term require more than cloud infrastructure: they need partners who can operate across regulatory zones, support local data residency, and deliver consistent performance.
Specialty AI providers rank highest in Forrester’s data as preferred partners. This aligns with what I see in practice: Organizations want vendors that combine global reach with deep technical expertise.
Five steps enterprises can take today
The Forrester study makes one thing clear: AI is here to stay, and success depends on how organizations navigate both opportunity and risk. Based on the findings, here are five practical steps you can take today:
- Anchor AI in customer value. Every implementation should tie directly to measurable improvements in CX, retention, or revenue.
- Balance speed with governance. Move fast, but build compliance, testing, and monitoring into the development cycle from the start.
- Invest in flexible architecture. Avoid lock-in by combining open source, cloud native, and managed AI services strategically.
- Choose partners wisely. Look for vendors who offer global scale, local compliance expertise, and proven reliability.
Treat experimentation as R&D. Don’t silo your pilots. Integrate learnings into long-term AI roadmaps.
Defining the next era of AI
The Forrester study confirms what many of us have already observed: AI has become central to enterprise strategy. But the numbers also highlight the tension between promise and risk.
Developers, architects, and business leaders alike should recognize that building AI today means shaping not just applications, but the customer experiences and brand reputations of tomorrow. Companies that anchor AI to customer value, adopt flexible technology strategies, and partner for scale will not only keep pace in the AI era, they will also help define it.
Learn more
If you’re interested in reading more about the state of Enterprise AI, read the full report.
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