From roadblocks to acceleration: An expertise-first approach to AI
July 11, 2024 / Chris Arrasmith
Short on time? Read the key takeaways:
- As organizations grapple with effectively leveraging their data assets and implementing AI solutions, they often encounter roadblocks that hinder progress. Consider tips Unisys has learned from developing an AI solution.
- Focusing on your strengths can accelerate the development of AI solutions tailored to your specific industry.
- Instead of tackling broad challenges, concentrate on clearly defined business problems that align with your strengths.
- Utilize your organization's existing data resources and systems of record as key enablers for successful AI initiatives.
The promise of data and AI is tantalizing — optimized operations, enhanced decision-making and breakthrough innovations. Yet, turning this promise into reality for many companies across industries remains elusive.
As organizations work to leverage their data assets and implement AI solutions, roadblocks can emerge that hinder progress. At Unisys, we navigated our own AI journey by taking a focused, expertise-driven approach to our Unisys Logistics Optimization™ solution. Our experience has created a blueprint your company can apply to overcome roadblocks and realize AI's immense potential.
Here are the key lessons we learned that you can use to propel your AI initiatives:
#1 - Identify your core strengths and expertise
True innovation stems from leveraging your organization's unique core strengths and deep domain expertise. Take stock of what products, services or areas of expertise set you apart from competitors.
For example, Unisys has decades of experience managing air cargo logistics. This experience has created a wealth of knowledge for helping airlines manage their cargo operations. By focusing on this specific domain, we were able to nail down a problem statement and then accelerate the development of Unisys Logistics Optimization, an AI-assisted capacity and routing solution that can drive real efficiency gains for our clients.
#2 - Pinpoint a specific business problem
It's tempting to boil the ocean when kickstarting data and AI initiatives. However, the most transformative solutions concentrate on solving clearly defined business problems aligned with your core strengths. Map out the operational pain points and inefficiencies you understand within your domain of expertise and then prioritize those opportunities.
Maximizing capacity utilization and optimizing route planning represented huge opportunities for the air cargo industry. As we developed our technology, these became central to the problem statement.
#3 - Map to multi-dimensional value drivers
Effective AI projects create value across multiple dimensions. Identify value drivers for your target use case to ensure your AI experiment drives business growth. Don't just concentrate on reducing costs — map all the potential upsides like increasing speed, improving customer experience, supporting sustainability and driving revenue growth. For example, we identified increased revenue, operational cost savings, fuel efficiency and improved customer experience as key value drivers.
#4 - Leverage existing data assets
Don't underestimate your existing data resources and systems of record when innovating. These can be key enablers for any successful AI initiative. Audit what proprietary, well-structured data assets you already have that competitors lack. This accelerates model development and provides a comprehensive, high-quality training environment.
Accelerate your journey toward innovation
Successfully implementing AI requires a tailored, strategic approach that addresses each organization's specific challenges and goals. By starting with a well-defined problem and leveraging your existing sources, companies can overcome common hurdles and realize the full potential of AI.
At Unisys, our experience developing U-Lo has shown us firsthand the impact these strategies can have. While the journey may look different for each organization, the core principles of a successful AI implementation remain constant. By applying these lessons to your own AI initiatives, you can reveal new efficiencies, insights and opportunities to delight your customers and stay ahead in an increasingly competitive landscape.
For a closer look at how organizations are applying generative AI and the challenges they face, the recent report from Harvard Business Review Analytic Services, sponsored by Unisys, is a great resource. The report, titled "Operationalizing Generative AI for Better Business Outcomes," explores the state of gen AI adoption across 500 leading organizations and provides valuable insights into realizing real ROI from gen AI investments.
To learn more about how AI can help maximize air cargo profitability, visit us online or contact us today.