Editorial
Artificial Intelligence is no longer a futuristic concept; it is a transformative force reshaping many industries, business models and even everyday life. From automating routine business tasks and personal productivity to enabling complex business automation, AI is driving innovation at an unprecedented pace.
However, understanding the AI opportunity requires a deep dive into its facets. This series aims to help readers become more AI savvy by providing perspectives from analysts and thought leaders, as well as insight into the technological fundamentals that underpin successful implementations.
This series will provide a comprehensive exploration of the evolving AI landscape. In the coming weeks, we will examine the key market segments, analyze how AI is disrupting industries and discuss the insights of leading analysts and thinkers. We will also break down the critical technologies and enablers that make AI solutions possible while addressing the challenges the field faces, such as security, privacy and hallucinations.
The Roadmap for This Series
- AI Market Definition: The Analyst Perspective — Understanding how analysts and market authorities define and segment the AI market, distinguishing between traditional AI, machine learning, generative AI and agentic AI.
- The AI Opportunity — Exploring how AI is set to change markets, disrupt businesses and create new opportunities based on current analyst perspectives.
- Thought Leaders’ Takes on AI — Sharing insights from industry authors and thought leaders, drawing from years of discussions and interviews.
- Tech Fundamentals of AI — Reviewing the key technologies that power AI, including how they interact and evolve over time.
- AI’s Key Enablers — Examining essential enablers such as data technology, cloud computing, large language models (LLMs), vector databases, workflows, transformers and deep learning network models.
- Addressing AI’s Challenges — Delving into pressing concerns, including hallucinations, security, privacy and other potential areas of concern.
My vantage point as an analyst allows me to observe AI’s rapid evolution and its transformative impact across industries. AI sits at the heart of today’s digital transformation, and its influence will only continue to grow. This series offers a well-rounded perspective on the AI market, combining analyst viewpoints, thought leadership insights and deep dives into the technology that makes AI possible. Whether you are a business leader, a technology enthusiast or a chief data officer, this series will provide critical insights that will help you navigate and leverage the AI revolution. The journey ahead promises to be as disruptive as it is exciting; let us explore it together.
AI Market Definition: The Analyst Perspective
Although AI is rapidly ascending the business agenda, most organizations are still at the preliminary stages of adoption.
Only 11% of organizations currently consider AI a cornerstone of their business
Recent research reveals that only 11% of organizations currently consider AI a cornerstone of their business, actively shaping and driving strategy. Another 24% see it playing a key supporting role in broader strategic initiatives. Despite this modest level of maturity, 82% (the majority) acknowledge the importance of AI to their future. Investment in AI is driven by a desire to tackle persistent business inefficiencies, anticipate future industry disruption, and maintain competitive parity.
The strategic outcomes organizations hope to achieve through AI investments are telling. The top goals, in order, include:
- Enable data-driven decision-making
- Enhance customer experience
- Boost operational efficiency
- Accelerate revenue growth
- Gain competitive market advantage
- Expand into new markets
However, there is a significant gap between aspiration and execution — just 8% of organizations report that AI is deeply embedded in their core business processes today. This reflects that many organizations continue to experiment and pilot AI against meaningful business use cases. But it also indicates many additional opportunities exist for organizations to build toward more scalable, integrated implementations of AI that can deliver quantifiable business value.
Related Article: Generative AI Is Your Co-Pilot — Are You Ready to Take Off?
Funding the Future: AI Moves From Hype to High Priority
Organizations are starting to put money behind their AI ambitions. One in five has allocated or reallocated a substantial portion of their tech budgets to generative AI, while 17% are doing the same for agentic AI.
These investments signal more than just interest — they reflect a growing belief in AI’s strategic value. Notably, early adopters are not confined to IT; enthusiasm is spreading across the C-suite, pointing to a broader shift in executive mindsets from AI for experimentation’s sake to tangible implementations that can power enterprise transformation.
Emerging AI Market Segments: From Traditional to Generative and Agentic Intelligence
As the AI market rapidly evolves, analysts and market authorities are beginning to segment it into three distinct categories:
- Traditional AI and machine learning
- Generative AI (GenAI)
- Agentic AI
Traditional AI has long been rooted in structured data, relying on cloud platforms, databases, big data architecture and statistical modeling. These systems are optimized for predictive analytics and pattern recognition within well-defined data environments. In contrast, generative AI represents a significant departure — built on large language models (LLMs) that leverage deep learning techniques, particularly generative pretrained transformers (GPTs). These models utilize high-dimensional mathematical processing, vector databases and unstructured data to generate content, extract meaning and enable new forms of problem-solving.
Although production deployments remain limited, adoption trends reflect growing interest in these newer AI segments. Research shows that 17% of organizations are actively adopting GenAI and another 29% are enthusiastic about its potential. Yet only 15% have moved GenAI into production.
Agentic AI, the most nascent of the three segments, integrates structured and unstructured data with workflow logic to create autonomous agents capable of executing complex tasks. Despite the promise of agentic AI, adoption is still emerging: 9% of organizations are actively exploring it, 24% are excited by the possibilities and just 6% report production use. Ease of implementation within existing apps should accelerate its use during the next year. Popular LLM platforms such as GPT, Gemini, LLaMA, Claude and Deepseek are powering many of these initiatives, pointing to a diverse but still maturing ecosystem.
“Higher adoption levels for GenAI and traditional AI models are no surprise. Agentic AI relies on these capabilities, and its effectiveness requires a high degree of maturity in GenAI and traditional AI. Otherwise, agentic AI becomes nothing more than automation that can get you to a bad business result even faster than you could before”, said Brian Lett, research director for Dresner Advisory Services.
Who’s Using AI Now: The Core Roles Driving Adoption
When it comes to the hands-on use of AI, data science, machine learning and business intelligence (BI) experts lead the pack — 68% of organizations identify them as frequent or constant users. Close behind are statisticians and data scientists (65%), business analysts (59%) and, to a lesser extent, citizen data scientists (34%). These roles are all increasingly vital as organizations look to turn raw data into actionable insights.
AI engagement varies by sector. Respondents from consumer services, technology and business services report the highest overall usage. Healthcare, while more selective, places the highest value on BI experts and data scientists, giving them top importance ratings. In contrast, government and education lag, showing the least interest in all user roles, highlighting a potential gap in talent deployment and AI maturity across sectors.
Top AI Use Cases
Eighty-two percent consider having defined use cases to be important. The most cited use cases, in order, include:
- Operational efficiency
- Customer segmentation
- Customer lifetime value
- Price optimization
- Quality assurance
- Predictive maintenance
- Fraud detection
- Product propensity
Finance respondents are interested in demand forecasting, report above-average interest in customer segmentation, but less often regard all other use cases.
Data Literacy: The Foundation for Effective AI Adoption
Data literacy is emerging as a key differentiator in successful AI, data science and machine learning adoption. Organizations with elevated levels of data literacy are significantly more likely to be using these technologies in production — 37% compared to just 27% of those with moderate literacy and only 19% of those with low or extremely low literacy. This correlation underscores the importance of equipping teams with the skills to understand, interpret and act on data effectively.
Industries and roles with a long-standing focus on data management — such as technology, financial services and specialized analytics functions — tend to report the highest levels of data literacy. These organizations are better positioned to evaluate the value of AI initiatives and also more capable of implementing them in ways that drive tangible business outcomes. As AI becomes more embedded in strategic operations, building data literacy across the workforce is no longer optional, it is a critical enabler of success.
As the numbers show, data-literacy levels complement and enable all successful data and analytics-related implementations — AI as well as BI — and yet often get overlooked when planning AI investments.
"Data literacy is not a light switch you can turn on. Its development takes time, commitment of resources and cultural reinforcement."
"Data literacy is not a light switch you can turn on," said Lett. "Its development takes time, commitment of resources and cultural reinforcement. You want people using AI outputs to have the requisite data-literacy levels to question when things don’t look right before allowing those outputs to become inputs into business processes or systems. Otherwise, AI functions as an unchecked black box, with high potential for creating business risk, with few in the organization having the ability to spot such risk in time. There’s not a C-level executive who would sign up for that scenario in their organization, yet less than half of organizations have formal programs in place to continuously develop data-literacy levels among employees.”
Related Article: Data Engineering Is Key to Scaling AI — Here’s What the Latest Research Says
Webinar
Oct
16
Agentic AI Playbook: Real-World Customer Service Use Cases You Can Deploy Now
Boost self-service by 30% and slash call volume by 63% with agentic AI.
Register
Webinar
Oct
22
Beyond Storage: Smarter Content, Bigger Impact with DAM + AI
Discover how the DAM + AI duo makes content smarter, stronger and more accessible.
Register
Webinar
Nov
6
Fix the Content Bottleneck: Build a Better WebOps Strategy
Content stalled? Dev overloaded? You’re not the only one. Learn how streamlined WebOps bridges the publishing gap.
Register
Webinar
On demand
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Watch Now
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
The Ambition-Implementation Gap
The AI revolution is clearly underway, yet most organizations are still in the preliminary stages of their transformation. While interest and strategic intent are high, there remains a significant gap between ambition and real-world implementation. This series aims to explore both the potential and the complexity of embedding AI into modern business operations.
In this opening piece, we examined how the market is being segmented, where investments are flowing, who is using AI and why data literacy is essential. What is clear is that success with AI takes more than enthusiasm: It requires focus, foundational skills and a clear commitment to scale. As the series continues, we will provide the insights and guidance needed to close the gap between vision and execution.
This series also will provide a roadmap for navigating the evolving AI landscape with confidence and insight. By spotlighting analyst perspectives, technology fundamentals, use case trends and the voices of industry thought leaders, readers will gain a comprehensive view of AI’s current state and future trajectory. Whether your organization is just beginning to explore AI or looking to scale its impact, this journey is designed to inform, inspire and empower. The AI era is here — those who understand it best will shape what comes next.
Ready to learn more? You can find Part 2 of the AI Revolution series right here.
Learn how you can join our contributor community.
