In the ever-evolving landscape of artificial intelligence (AI), two distinct paradigms stand out: symbolic AI and generative AI. These approaches offer unique capabilities and find their own niches in the realm of business applications. As AI permeates every sector, from finance to healthcare, understanding its diverse methodologies becomes essential for businesses seeking to thrive in the digital age.
The Impact of AI in Automation
AI’s impact on automation extends across various sectors, including financial services, healthcare, insurance, automotive, retail, transportation, and logistics. According to PwC, AI is projected to boost local economies’ GDP by around 26% by 2030. As businesses embrace automation to streamline operations and enhance efficiency, the role of AI becomes increasingly pivotal.
Generative AI: Unleashing Creativity
Generative AI represents the cutting edge of AI innovation, harnessing the power of machine learning to create content ranging from textual narratives to visual artworks.Generative AI mimics human “System 1” thinking, which psychologists describe as automatically occurring operations of thought that are done innately and quickly, with little or no effort. Generative AI operates automatically and rapidly, albeit with potential for errors and biases. This approach thrives in scenarios where creativity, rapid adaptability, and innovation are valued. From virtual design tools in retail to predictive modeling in healthcare, generative AI finds applications across diverse domains.
Symbolic AI: Logic in Action
Symbolic AI, reminiscent of traditional AI methodologies, operates on the manipulation of symbols and rules to execute tasks. This approach mirrors human “System 2” thinking, which is characterized by deliberate, logical reasoning. Symbolic AI excels in environments where transparency, structured decision-making, and compliance are paramount. Industries such as healthcare, finance, and regulatory compliance heavily rely on symbolic AI for expert systems, medical diagnosis, and natural language processing tasks.
Applications Across Industries
The applications of AI span various industries, each benefiting from the unique capabilities of symbolic and generative AI. From automating customer service and simplifying detail-intensive tasks to enhancing product development and improving health and safety measures, AI permeates every sector. For instance, financial institutions leverage symbolic AI for coding assistance, while retailers employ generative AI for virtual design tools. Logistics companies rely on AI robotics for warehouse management, while construction firms deploy AI-based safety tools to enhance worker well-being.
Generative AI applications
- Sampling creative products
Example: Wayfair’s Decorify tool lets shoppers virtually redesign their homes and test furniture designs, enhancing engagement and driving sales.
- Developing products
Example: Beiersdorf utilizes AI predictive modeling in skincare research to create innovative products tailored to consumer needs, driving product enhancement and innovation.
- Automating customer service
Example: Klarna implemented a customer-service AI assistant on its app, helping users manage payment schedules and address queries, boosting operational efficiency.
- Simplifying detail-heavy tasks
Example: Citi introduced GitHub Copilot, a generative-AI coding assistant, speeding up code development for insurance and investment products.
- Targeting problems and solutions
Example: John Deere employs AI in its See & Spray product to reduce herbicide use and increase crop yields, addressing agricultural challenges effectively.
Symbolic AI applications
- Managing warehouses
Example: DHL collaborated with Robust.AI to develop autonomous robots capable of managing supply-chain tasks, optimizing operations and reducing labor costs.
- Securing networks
Example: BT developed Eagle-i, a cybersecurity platform powered by AI, detecting and mitigating cyberattacks in real time, safeguarding networks and preserving brand reputation.
- Monitoring perpetual systems
Example: E.ON utilizes AI-enabled drones to monitor electricity infrastructure, conducting consistent oversight and identifying weaknesses, enhancing service reliability.
- Improving health and safety
Example: Suffolk Construction partnered with NewMetrix to develop an AI-based safety tool, minimizing risks, enhancing worker safety, and fostering a culture of well-being.
- Complying with regulations
Example: Tetra Pak utilizes AI to analyze product-related data and ensure regulatory compliance, enhancing product quality and consumer trust.
In the journey towards digital transformation, businesses must navigate the spectrum of AI, leveraging both symbolic and generative approaches to address diverse challenges and opportunities. By understanding the nuances of these AI methodologies and identifying suitable use cases, companies can unlock new levels of efficiency, innovation, and competitiveness. As AI continues to revolutionize industries and redefine business processes, embracing the spectrum of AI is imperative for success in this new age of automation.