The Challenges of AI Deployment in Enterprise Applications
Many companies forget that AI-powered enterprise applications are, at their core, business apps. While AI is a powerful tool, businesses often rush to deploy various applications without achieving substantial business value. The process of implementing AI should be similar to any other business application deployment, focusing on hitting the target of increased business value.
The Urgency and Reality of AI Adoption
Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. This rapid growth indicates the urgency businesses face in adopting AI technology. At a recent Insight Amplify technical conference, several tech executives shared their experiences deploying AI applications. They agreed that most successful implementations have been internal-facing, serving as a proving ground to master the technology before moving to customer-facing applications.
Common Steps in Successful AI App Deployment
The executives followed similar steps in their AI deployment processes:
- Identify the business problem in partnership between the business line and IT team.
- Build a minimum viable product and deploy it to test functionality and practicality.
- Evaluate the return on investment to ensure financial viability.
- Expand the application with necessary cost and security controls.
These steps are the same as those for traditional app development. However, the addition of AI often causes businesses to overlook fundamental principles.
Three Critical Missteps in AI Deployment
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Developing AI Apps Internally Without Proper Expertise Companies should focus on their core business rather than trying to develop AI applications themselves if they’re not in the software development business. It’s often better to rely on partners with specialized AI knowledge.
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Improperly Preparing Data Poor data quality is a significant issue in many organizations. Data is often siloed, inadequately secured, and poorly organized, which can lead to problems when deploying AI applications. Proper data organization and normalization are crucial.
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Locking Yourself into Specific Solutions or Vendors The rapidly evolving nature of AI technology means businesses must stay flexible. Locking into specific on-premises solutions, cloud providers, or vendors can be risky. Companies should stick to their strengths, follow proven development.
Moving Forward with AI
To successfully deploy AI, businesses should avoid overthinking and stick to what’s familiar and proven. Working with trusted partners who have expertise in AI can help companies navigate the evolving landscape and achieve their goals.
Conclusion
Deploying AI in enterprise applications requires a thoughtful and informed approach. By understanding the common missteps and following proven development processes, businesses can unlock the full potential of AI technology.
