You’ve probably seen the headline: 95% of AI projects fail. It’s a statistic that stops leaders in their tracks—but it doesn’t have to define your story. The truth is, AI success isn’t about luck; it’s about having the right foundation in place. From strategy and governance to data readiness and change management, the difference between failure and measurable ROI lies in how you approach your initiatives.

At TSIA, we see how technology companies like yours are leveraging AI—whether that’s predicting resource needs, analyzing customer adoption, or integrating generative AI into support and sales. In this blog, you’ll discover best practices to increase your odds of success, and real-world examples from TSIA members who are already proving AI’s value.

Key Takeaways

• AI success starts with the basics. Clear strategy, executive alignment, and a dedicated budget create a foundation for initiatives that scale.
• Data and people matter as much as technology. Clean, integrated data and a skilled workforce ensure your AI projects deliver reliable and sustainable outcomes.
• Practical use cases show what’s possible. TSIA members are using AI to forecast resources, drive adoption, streamline support, enhance education, and accelerate sales.

Best Practices To Ensure Your AI Initiative Succeeds

The reality is, success with AI doesn’t happen by accident—it requires a clear strategy, the right resources, and a culture that embraces change. Here are the best practices that can help you build AI initiatives that deliver measurable results.

1. Start With a Clear Strategy

Your AI program requires more than just a few pilot projects—it needs executive backing and a clear roadmap. When your C-suite is directly involved, you secure the resources, visibility, and organizational alignment that AI projects need to thrive. Creating a dedicated AI budget also ensures your most important initiatives don’t get lost among competing priorities.

2. Invest in People and Skills

AI isn’t just about technology—it’s about people. You’ll need to:

• Upskill your workforce with training on data literacy, AI tools, and change management.
• Bring in specialists, such as data scientists and machine-learning engineers, who can design, implement, and scale AI initiatives.

Blending internal growth with external expertise gives your team the depth to handle both short-term execution and long-term strategy.

3. Get Your Data in Order

AI is only as strong as the data behind it. If your data is siloed, messy, or incomplete, your AI outcomes will suffer. A centralized data strategy—complete with strong governance and integration across systems—ensures your AI models have reliable, accessible, and high-quality data to work with.

4. Start Small, Then Scale

One of the primary reasons AI projects fail is attempting to accomplish too much too quickly. Instead:

• Identify high-impact use cases that align with your strategic goals.
• Run pilot programs to test your approach, learn from the results, and refine it before implementing enterprise-wide rollouts.

This phased approach minimizes risk and helps you prove value quickly.

5. Prioritize Change Management

AI can raise concerns across your workforce, and addressing them head-on is essential. Communicate openly about how AI supports—not replaces—your employees, and involve teams early in the process. This helps build trust, reduce resistance, and create a culture where AI is seen as a partner in success.

6. Measure and Improve Continuously

Don’t just launch AI and walk away. Define clear success metrics from the start and track ROI regularly. Utilize feedback loops and performance reviews to refine your strategy, enhance implementations, and stay current with evolving AI best practices.

Related: AI Isn’t Just a Feature, It’s a Go-to-Market Strategy

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Real-World AI Use Cases From TSIA Members

Observing how other companies implement AI can spark ideas for your own initiatives. TSIA members are already deploying AI across professional services, customer success, support, education, and sales—and the results demonstrate its versatility and impact.

Predictive AI for Resource Forecasting

Siemens Digital utilizes predictive AI to forecast resource needs, enabling it to allocate teams more effectively and enhance service delivery. With AI-enhanced forecasting, you can plan with greater accuracy and ensure customer projects stay on track.

Predictive Analytics for Customer Adoption

By applying predictive analytics, Informatica can analyze customer adoption patterns and proactively engage with accounts at risk. This approach enables customer success teams to step in early, build stronger relationships, and drive higher retention.

Related: Informatica’s Digital First Customer Experience

Generative AI in Support Services

BMC Software has integrated generative AI into its IT Operations Management suite to streamline processes and enhance efficiency. For managed services teams, generative AI means faster resolutions, better scalability, and more time to focus on higher-value activities.

Intelligent Content Creation

OpenText leverages AI to create and curate educational content, accelerating development and enhancing learning resources. If you deliver education services, AI can reduce the manual effort involved in content creation while ensuring your training stays fresh and relevant.

Related: How OpenText is Leveraging AI for Content Development in Education Services

AI-Powered Sales Enablement

PROS has introduced AI-enabled chatbots and generative AI into its sales process, offering real-time insights and support. With AI embedded in tools like CPQ and collaboration platforms, sales teams can respond faster, customize offers, and close deals more effectively.

These examples show how companies like yours are applying AI in practical, measurable ways. Whether it’s improving forecasting, strengthening customer success, or boosting sales, AI is proving its value across the business.

Building AI Initiatives That Actually Deliver Results

AI may have a reputation for high failure rates, but that doesn’t have to be your experience. With the right strategy, clean data, skilled people, and a phased approach, you can move from experimentation to measurable business impact. As TSIA member examples demonstrate, AI is already driving value across professional services, customer success, support, education, and sales.

The takeaway is clear: success with AI isn’t about avoiding risks altogether; it’s about making smarter, better-informed decisions at every step.

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FAQs

What are the most common reasons AI projects fail?

AI projects often fail because organizations underestimate the preparation required. Lack of executive alignment, unclear goals, poor data quality, and inadequate change management are among the most common pitfalls. Without a solid foundation, even the most advanced AI technology can’t deliver value.

How can smaller organizations approach AI if resources are limited?

You don’t need a massive budget to get started with AI. Focus on one or two high-impact use cases that align with your business priorities. By starting small—such as automating repetitive tasks or improving forecasting—you can build confidence, demonstrate ROI, and make a stronger case for additional investment.

What role does TSIA Intelligence play in supporting AI initiatives?

TSIA Intelligence is an AI tool built exclusively for technology and services companies. It draws on over 20 years of proprietary TSIA data to provide you with insights, benchmarks, and answers to the top questions leaders like you are asking. Whether you’re evaluating use cases or looking for best practices, TSIA Intelligence helps you make informed, data-driven decisions and avoid the trial-and-error approach that leads many AI projects to fail.