Why AI Investment Still Isn’t Translating to Revenue
AI investment across the tech industry is accelerating, but revenue performance is not keeping pace.
Salesforce’s 2025 Trends in Technology Report highlights the disconnect: 93% of tech companies are increasing AI investment, yet 55% still expect to miss their revenue targets this year. From what we see at Artisan Studios, this gap is not about AI capability; it’s about readiness.
Too many organizations approach AI as a technology rollout instead of a business transformation. Without the right foundations, even well funded AI initiatives struggle to create measurable impact.
The Real Problem Is Readiness
Several data points in the Salesforce report point to the same underlying issue:
- Only 45% of organizations have a clearly defined AI strategy.
- Just 14% have fully integrated their data.
- 46% of teams say they do not have the data they need to do their jobs.
AI does not operate in isolation, it depends on strategy, data, and operating models working together. When those pieces are misaligned, AI simply automates existing inefficiencies instead of fixing them.
This is why many teams feel like they are doing AI without seeing meaningful outcomes.
Data Integration Is the Starting Line
AI success is tightly coupled to data quality and accessibility. If customer, product, and operational data live in disconnected systems, AI agents lack the context needed to make good decisions.
Organizations that see value from AI do not treat data integration as a technical cleanup project, they treat it as a strategic priority. They establish ownership, align on definitions, and invest in the unglamorous work of integration before scaling automation.
Without this foundation, AI outputs may look impressive, but they will not be reliable, trusted, or actionable.
A Costly Misstep Building What Does Not Differentiate
Another pattern we see is organizations overbuilding AI in areas that do not create competitive advantage.
Salesforce reports that 72% of tech companies have built some or all of their go to market agentic AI internally. That often means months spent developing sales, marketing, or support automation, despite the availability of proven off the shelf solutions.
That tradeoff is rarely worth it.
Every engineering cycle spent recreating commodity functionality is time not spent improving the core product. High performing organizations make a clear distinction: they build where AI differentiates their offering and buy where AI is table stakes.
This clarity preserves focus and accelerates time to value.
Adoption Is a Change Problem
Even with the right strategy and data, AI adoption does not happen automatically.
AI changes how decisions are made and how work gets done. Teams need clarity on how their roles evolve, how much to trust AI outputs, and when human judgment still applies.
Organizations that succeed invest in enablement and change management as part of the rollout. They create feedback loops, encourage experimentation, and are transparent about limitations.
When this step is skipped, AI tools get deployed but quietly ignored.
When AI Misses the Mark, Customers Feel It
Customer expectations continue to rise. Salesforce notes that 80% of customers expect more than they used to, and 75% expect a more personal experience. Yet many companies report flat or declining retention.
This is the hidden risk of poorly executed AI: when automation is layered onto broken workflows or incomplete data, customers experience inconsistency instead of personalization. That gap between expectation and experience erodes trust and ultimately revenue.
Three Questions That Clarify the Path Forward
Before scaling AI, tech leaders should be able to answer three questions clearly.
- Do we have integrated, trusted data that supports intelligent automation
- Are we building AI where it differentiates or where it is simply expected
- Is the organization prepared for how AI changes the way work happens
When the answer to any of these is not yet, the priority should not be more AI. It should be readiness.
What This Means for AI Strategy
The companies that win with AI will not be the ones that deploy the most tools or agents. They will be the ones that sequence their efforts correctly. Strategy first. Foundations next. Technology lasts.
At Artisan Studios, we help organizations take that approach by aligning AI investment with business outcomes and preparing teams to actually use what gets built.
AI is a powerful accelerator, but acceleration without alignment does not lead to growth. It leads to drift.
