How to Overcome Challenges in Implementing AI Solutions in Organizations.

As the world moves toward digital transformation, many organizations struggle to leverage their data assets and implement Artificial Intelligence (AI) solutions. The promise of AI to transform how businesses operate is clear, but on the path to implementation, obstacles often arise that hinder progress. To overcome these challenges, it's essential to adopt a strategic and practical approach.

1. Focus on the Organization’s Strengths

One of the most valuable lessons Unisys learned while developing its own AI solutions was the importance of focusing on the organization’s strengths. Many companies fall into the trap of trying to solve all problems at once, which can dilute efforts and delay results.

Instead, focus on clearly defined business problems that align with your specific industry strengths. For example, a company with strong expertise in data analysis could begin its AI journey by developing predictive models to improve operational efficiency. This approach not only speeds up development but also allows the company to leverage its internal knowledge to create value from the start.

2. Leverage Existing Data Resources

Another critical factor for successful AI initiatives is utilizing existing data resources and systems of record. Many organizations already possess vast datasets but often fail to effectively integrate them into their AI strategies. To maximize these opportunities, businesses should identify relevant data that can serve as the foundation for AI models.

These data resources may come from various sources, such as customer relationship management (CRM) systems, ERP platforms, or even daily operational data. The key is to use these assets as central enablers, allowing AI to provide more accurate insights and more effective solutions.

3. Address Challenges in a Targeted Way

Rather than trying to solve all challenges at once, a more effective approach is to segment the problems and address them strategically. AI solutions should be implemented with a clear, specific focus. For example, a hospital might concentrate its AI initiatives on improving patient care through predictive diagnostics, while a manufacturing company could use AI to predict machinery failures and optimize maintenance.

By tackling segmented business problems, organizations can achieve quick, tangible results, which in turn helps build stakeholder confidence in the potential of AI.

4. Evolve Based on Learnings

As AI initiatives progress, it's essential for organizations to be willing to learn from each stage of the process and adjust their strategy accordingly. AI is a rapidly evolving technology, and what works today may not be the ideal solution tomorrow. Therefore, maintaining flexibility and being open to revisiting previous decisions will allow companies to adapt quickly to change.

5. Collaboration Between IT and Business Teams

Finally, a successful AI implementation requires close collaboration between IT teams and business departments. The IT team provides the necessary infrastructure and technical expertise, while the business teams define the priorities and challenges that AI needs to solve. This synergy ensures that the solutions are not only technically sound but also aligned with the organization’s strategic goals.

Conclusion

Implementing AI can be challenging, but by focusing on the organization’s strengths, leveraging existing data resources, and addressing challenges in a targeted way, companies can accelerate their AI projects and achieve more effective results. A focus on clearly defined business problems, combined with collaboration between IT and business teams, ensures that the solutions developed deliver real, measurable value.

As companies continue to navigate the dynamic landscape of digital transformation, these lessons learned can serve as a valuable guide for making the most of AI’s potential.