When to Use Standard AI Tools and when to use Research-Based Development for Maximum Impact : Leveraging the Best of Both Worlds
Introduction
In today’s fast-paced business environment, decision makers are increasingly realizing the transformative potential of AI and data science. However, they often face the dilemma of choosing between using standard AI tools or opting for research-based development. In this article, we will shed light on the importance of having a partner that excels in both areasāstandard AI tools and research-based development. By providing decision makers with relatable examples, we aim to demonstrate the significance of striking the right balance and making informed choices to drive maximum impact from AI initiatives.
Standard AI Tools : Efficiency and Rapid Deployment
Standard AI tools, such as widely used libraries and frameworks, offer decision makers a quick and efficient way to address common business challenges. These tools come with pre-built models and algorithms that have been tested and fine-tuned by the community. Decision makers can leverage these tools to solve tasks such as customer segmentation, demand forecasting, and sentiment analysis. Standard AI tools deliver measurable results within shorter timeframes, enabling businesses to swiftly extract value from their data and make data-driven decisions.
Example :
Imagine a retail executive who wants to optimize inventory management. By employing standard AI tools, they can utilize demand forecasting algorithms to analyze historical sales data, identify patterns, and predict future demand with accuracy. This allows them to optimize stock levels, minimize waste, and maximize revenue.
Research-Based AI Development
Tailored Solutions for Unique Challenges. While standard AI tools are valuable for common use cases, companies often encounter unique challenges that require customized solutions, or even new model development. Research-based development steps in to address these specific needs. Large corporations often have the resources to invest heavily in AI research and development (R&D). Medium-sized companies can also capitalize on the benefits of research-based AI developments by partnering with a company that excels in research-based AI and data science. Having such a capability, helps businesses go beyond off-the-shelf AI models to address specific business needs.Ā Research-based AI developments often push the boundaries of what standard AI models can achieve, by leveraging the latest breakthroughs in machine learning, natural language processing, computer vision, and other AI subfields. Adopting these cutting-edge capabilities, companies of all sizes can gain a competitive advantage and stay ahead of the curve in their respective industries.
Examples :
A media streaming platform used research-based IA developments to enhance its recommendation system. They leveraged advanced machine learning algorithms, to create personalized content recommendations for each user. As a result, they increased user engagement and longer viewing sessions, leading to higher customer retention rates and improved revenue for the company.
A healthcare organization used research-basedĀ AI developments to improve patient care through personalized treatment plans. They used advanced machine learning models that analyze patient records, genetic data, and medical research. As a result, they could perform more accurate diagnoses and highly personalized treatment recommendations. They ultimately improved time-to-recovery and patient experience, and reduce handling costs.
A medium-sized e-commerce company leveraged research-based IA developments to enhance its customer experience. By utilizing advanced natural language processing models, they developed a chatbot capable of understanding customer queries, and responding to them, based on the company’s knowledge base, with remarkable accuracy. The quality of responses of their chatbot helped them to significantly reduce response times, increase customer satisfaction, and ultimately drove higher conversion rates.
The Power of Dual AI Expertise, augmented through Applied Mathematics
In the dynamic landscape of artificial intelligence (AI), companies that strive for a competitive edge must embrace a multilayer approach to deliver their AI roadmap, that activates the power of standard AI models, research-based AI development, and expertise in applied mathematics to unlock new opportunities, build strategic advantages, and generate a sustainable growth of their enterprise value.
The transformative potential of this triad with laying the foundation through the use of standard AI models, such as deep learning and machine learning algorithms which serve as the foundation for many AI applications. They provide powerful tools for tasks like image recognition, natural language processing, and predictive analytics. Companies that employ these models gain access to off-the-shelf solutions, enabling them to quickly implement initial use cases and address common challenges, of model performance, and confidence level, while already delivering positive ROIs. The models serve as a starting point to put the foundation of a company’s AI/prediction capability. To achieve a competitive advantage, companies must go beyond the basics.
Research-based AI development is the second step. It involves leveraging cutting-edge advancements and pushing the boundaries of what AI can achieve. By investing in AI R&D, companies can develop custom solutions that align precisely with their specific needs and ecosystem. This approach allows for tailored AI systems, automation tools, and innovations, driving increased efficiency, better outcomes, and in most cases, starting to build competitive and strategic advantages. Research-based AI development helps companies to stay ahead of the curve, as they can adopt the latest breakthroughs in machine learning, computer vision, and more.
Augmenting the company’s AI capability through Applied Mathematics is the bridge to success. It acts as a vital bridge between standard AI models and research-based AI development. Companies harness mathematical principles and algorithms to optimize and enhance AI solutions. Expertise in applied mathematics helps uncover underlying patterns, improve algorithmic efficiency, and enhance the interpretability of AI systems. It enables companies to develop robust models that provide accurate predictions, reliable insights, and better decision-making capabilities.
Examples :
A financial institution combined standard AI models with research-based AI development and applied mathematics to detect fraudulent transactions. They adapted, then trained their AI model to identify patterns of suspicious behavior. Advanced mathematical methods were applied to optimize the model’s performance and interpret the decision-making process. This integrated approach significantly improved fraud detection accuracy, and reduced losses, while safeguarding the institution’s reputation.
A retail company integrated standard AI models, research-based AI development, and applied mathematics to optimize its supply chain operations. Machine learning algorithms were employed to analyze historical data and forecast demand patterns accurately. Applied mathematics techniques helped fine-tune the model, considering variables such as seasonality, market trends, and pricing dynamics. This holistic approach enabled the company to optimize its inventory management, reduce stockouts, and improve overall supply chain efficiency.
Conclusion
The convergence of AI and Applied Mathematics empowers companies to unleash the transformative power of AI to sustainably grow and stay ahead of the competition, through building strategic and competitive advantages, becoming data-driven decision-makers, reducing prediction costs, uncovering hidden opportunities, and fostering innovation. Growing the capability combination or partnering with experts, is crucial for companies to successfully execute their strategy.
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