Summary
- AI is shifting from experimentation to enterprise strategy, driven by business outcomes like revenue generation, efficiency, and competitive advantage.
- Success depends on starting slowly and thoughtfully, focusing on small pilots, carefully selecting use cases, and ensuring strong data governance.
- The next shift will be learning how to operationalize agentic AI to enhance business capabilities.
A collaborative conversation on the state of AI
Over the past year, business interest in AI has increased, with nearly two-thirds of organizations actively experimenting.i Growing curiosity and storied successes have solidified AI’s role as a major focus for leaders across industries.
GDT recently hosted a panel of esteemed industry colleagues to discuss the current state of AI and how our industry-leading solutions work together to enable AI across the stack for our joint customers.
Key takeaways from our discussion covered a range of timely topics, including:
- Top business drivers and use cases for AI
- Challenges when implementing AI solutions
- Sustainability and environmental impacts of AI
- Keys to successful AI implementations
- Predictions for AI in the near future
Here are some of the key takeaways from our conversation to help equip your organization as you plan the next steps in your AI journey:
1. AI is a must-have.
The bottom line is that the business landscape as it is now has made AI a must-have. To remain competitive, today’s business leaders don’t really have a choice when it comes to accelerating AI, but they do have a choice in what partners can help them do most effectively.
As cooperative players in the AI ecosystem, our panel speakers all underscored this common theme: Today’s businesses must accelerate AI use case development and validation and putting AI to work in the highest-value scenarios to start growing their competitive advantage.
2. AI investment must be based on business outcomes.
The major drive behind AI investment right now is the need to stay competitive. Decision-makers clearly understand that AI adoption is a must if they don’t want their organization left behind.
Along with that baseline understanding is a shift from thinking in terms of consumer use cases, like the personal use of ChatGPT as an assistant, to thinking in terms of enterprise use cases in unique industries such as higher education, healthcare, financial services, and more.
Business outcomes are the most important consideration in making decisions on AI investments. What today’s AI decision-makers need to realize is that AI built into the enterprise environment has the potential to accelerate revenue generation like never before. This is where the concept of an AI factory comes into play: No longer are organizations simply investing in AI assistants for individual team members; instead, savvy IT leaders are investing in creating AI factories within their organizations, facilitating a technology ecosystem that allows the organization to produce multiple custom-built AI solutions that enable their different business units to innovate and grow.
The AI factory concept captures what may be the most fundamental shift in how businesses view AI: no longer a costly investment in search of a return, but a direct driver of revenue.
3. AI success starts with asking the right questions.
When you clearly understand the problem you’re trying to solve with AI, the investment almost always proves worthwhile. That’s why, at GDT, every client engagement starts with a discovery phase. Driving value with AI requires understanding your top business priorities: Do you want to save money, make more money, reduce risk, etc.? Starting with the “why” and exploring use cases from there is the most helpful way to move forward and drive tangible, measurable value.
Our conversation highlighted some common, high-value use cases that are already driving positive impacts for AI users across a variety of sectors:
- Financial services: Enhancing personalization and customer engagement
- Healthcare: Integrating records and imaging to enhance patient care
- Manufacturing: Using computer vision to identify defects
- Product development: Leveraging proprietary data to produce new designs at scale
- Public sector: Fast-tracking research and innovation
4. Data complexity is one of the most common reasons for failure.
As technology leaders, we’ve all seen examples of “failed” AI projects. Some are a result of trying to copy someone else’s AI success without understanding why it worked. Others are a result of going too big, too fast.
One of the most important things to understand about success with AI is just how often data complexity stands in the way. When data is not properly prepared or managed, your AI project doesn’t have the foundation it needs for scalable success.
Common data concerns include:
- Data silos that duplicate or cause confusion with data
- Lack of understanding about what the data represents
- Poor and inconsistent data quality
A successful AI model relies on data standards and data hygiene processes and requires human validation along the way to ensure data accuracy.
As with data accuracy, responsible AI use requires safeguards to ensure safe and ethical use. Organizations need proactive plans to address data security, access controls, model hallucinations, and ethical use. A strong governance plan will provide employees with clear guidelines, usage policies, and controlled environments to ultimately protect the data, employees, and the organization.
5. Managing sustainability will be crucial for the next phase of AI growth.
Large-scale GPU workloads used to power AI solutions can put significant pressure on infrastructure, as data centers are becoming a major consumer of electricity to operate and cool servers. We discussed efforts taking place to reduce data center energy consumption, such as:
- Optimizing GPU utilization for individual AI workloads
- Replacing air cooling with liquid cooling for large data centers
- Moving data to a hybrid infrastructure that combines on-premises and cloud resources
As innovation in this space continues, customers must consider energy efficiency and environmental impact when developing AI strategies. Environmental impact will remain a focus for years to come as the volume of AI programs grows.
Considerations for AI success
A successful AI project takes a strategic approach that relies on clarity and discipline over speed.
The most successful AI organizations follow these simple practices:
- Begin with small POCs or pilot projects.
- Think big and develop a long-term plan.
- Develop use cases prescriptively, ensuring each addresses a problem.
Factors that may hinder success include:
- Trying to “do everything with AI”
- Skipping over a discovery phase
- Underestimating the importance of the data
What’s on the horizon?
Experts agree that the next big trend for AI is likely the adoption of AI systems that act, decide, and assist, such as agentic AI.
As excitement grows around the potential for agentic AI at enterprise scale, early adopters and the major AI leaders are laying the groundwork to help decision-makers across industries better understand how to use agentic tools alongside employees to support decision-making and drive measurable outcomes. It’s expected that advanced agentic systems are two years away from mainstream adoption.
A defining moment for AI leadership
AI is becoming a core business capability that drives innovation at scale. The emergence of agentic AI and AI factories marks a shift from isolated experimentation to more integrated, enterprise-wide systems. Organizations that lead in this next phase will treat AI as a strategic priority, align initiatives to business outcomes, and build the data and infrastructure required to scale effectively.
This is where the right partner makes the difference. GDT helps organizations cut through AI hype to identify practical use cases, assess readiness, and prioritize initiatives that align with real business objectives and infrastructure requirements. Through a structured, business-first approach, we help reduce risk and accelerate the capture of measurable value, ensuring AI strategies are actionable, scalable, and built to deliver outcomes.
If you are considering an AI initiative, contact GDT to request an AI strategy workshop tailored to your needs, infrastructure, and desired outcomes. Our team can support your AI program with design, deployment, operations, and lifecycle AI infrastructure for reliable performance at scale.
