Tapping into the AI revolution can be a complicated, confusing and costly prospect for companies—particularly small businesses and startups. Leaders in search of a quick solution who don’t have big budgets or in-house tech teams may believe they’re locked out of the many benefits of artificial intelligence.
Enter foundation models. Pretrained on vast datasets, these AI tools not only offer access to a broad range of general knowledge, but can also be fine-tuned for specific needs. Foundation models’ flexibility can enable a business to improve and streamline processes across its teams, all without the budget hit and effort required to develop an AI initiative from scratch. Below, members of Forbes Technology Council detail how foundation models can help businesses leverage AI better and faster for a real competitive edge.
1. Lowering The Costs Of AI Implementation
Foundation models can accelerate AI adoption for small businesses by providing adaptable frameworks that reduce the time and cost of implementation. This enables them to quickly integrate capabilities like AI voice agents, customer insights and automation, leveling the playing field with larger companies. – Lyle Pratt, Vida
2. Facilitating Faster Prototyping And Experimentation
Foundation models facilitate rapid prototyping and experimentation in AI initiatives. Companies can test ideas and iterate solutions swiftly without building models from scratch. This agility is crucial in the tech industry, where speed to market can be a significant competitive advantage. By accelerating the innovation process, businesses can respond promptly to market demands and emerging trends. – Abhishek Shivanna, Nubank
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3. Removing The Need For Extensive In-House Expertise
Foundation models like GPT-4, DALL-E and LaMDA can enable businesses of all sizes to adopt AI capabilities without needing extensive in-house expertise. Recently, I came across a small e-commerce startup that used a foundation model to create a personalized shopping assistant, analyzing customer behavior to suggest products. This shifts the focus from developing basic models to innovating with AI. – Santhosh Vijayabaskar
4. Being Easily Adaptable For Specific Tasks
Foundation models can accelerate AI adoption by providing pretrained, adaptable models that businesses can quickly fine-tune for specific tasks, such as customer service or predictive analytics. This reduces the need for extensive data and development time, making AI more accessible and cost-effective. The flexibility and scalability of foundation models enable businesses to innovate faster. – Jonathan Gillham, Originality.ai
5. Democratizing AI Development
Foundation models will democratize AI development, allowing businesses to quickly customize powerful models for specific tasks without extensive AI expertise or data. This will accelerate AI adoption across industries, enabling rapid innovation in areas like customer service, product development and process optimization, ultimately leveling the playing field for businesses of all sizes. – Victor Rijo, Phrasly AI
6. Deploying AI Capabilities With Minimal Data
Foundation models can significantly accelerate a business’ AI applications by streamlining the development of domain-specific solutions. A notable advantage is their ability to perform few-shot or zero-shot learning, allowing companies to deploy AI capabilities with minimal data. Enhanced human-AI collaboration can be fostered, improving decision-making processes through better context understanding. – V S Adithya Dwadasi, AHEAD
7. Simplifying Access To Knowledge As A Service
Foundation models offer an efficient way to access knowledge as a service by automating tasks that require general information that’s available online. For processes that don’t rely on business-specific knowledge but instead align with widely available data, foundation models can streamline and automate knowledge gathering. – Gonçalo Ribeiro, YData
8. Reducing The Need For Task-Specific Training
Foundation models are revolutionizing AI adoption by reducing the need for task-specific training, allowing for rapid adaptation to tasks like natural language processing, decision making and predictive analytics. Their ability to generalize across various domains shortens development cycles, giving businesses access to advanced AI without the need for extensive resources or expertise. – Dr. Pramod Kumar, QuantLase Laboratory
9. Enabling Rapid AI Deployment Across Functions
Foundation models are the ultimate business AI accelerator, democratizing advanced capabilities. They’re transforming operations overnight: Imagine a customer-service AI tool that’s capable of handling 80% of inquiries or marketing teams generating personalized campaigns at scale. By enabling rapid deployment across functions without specialized tech, these models slash costs, boost productivity and unlock innovation. – Prem Naraindas, Katonic AI
10. Providing Detailed Image And Video Analysis
Foundation models have changed image and video analysis. They can detect and localize objects, classify content, detect anomalies, and even generate or edit images. This is all down to their huge training datasets, which allow them to learn complex patterns and make accurate predictions. They are versatile and scalable, so they can be used in many applications, from self-driving cars to medical imaging. – Sarath Babu Yalavarthi, AT&T
11. Building Shared, Common Knowledge
In my experience, foundation models foster “adaptive intelligence” by building shared, common knowledge across departments. This not only breaks down silos, but also enhances collaboration, helping teams align better with business goals. By integrating diverse functions, foundation models accelerate processes, drive faster decision making and improve efficiency, which results in a more agile organization. – Suri Nuthalapati, Cloudera
12. Serving As A Universal Interface
Foundation models could accelerate AI adoption by serving as a universal interface for disparate data sources. Instead of building multiple custom models, businesses can fine-tune one foundation model to integrate and analyze diverse datasets—ranging from text to images to sensor data—seamlessly. This dramatically reduces the complexity of data management and allows for faster AI-driven insights. – Andrii Kovalchuk, WeSoftYou Inc.
13. Detecting Trends And Cycles In Data Streams
Large language models are highly skilled at recognizing patterns in language, but this capability extends to time series data as well. They can detect underlying trends and cycles in data streams. The major advantage LLMs offer is their ability to incorporate all available context related to time series. This allows them to introduce reasonable biases into the model guided by their broader embedded world knowledge. – Igor Kiselev, Accenture
14. Personalizing UX At Scale
Foundation models offer businesses the potential to personalize user experiences at scale. By fine-tuning these models to specific industries or user segments, companies can create AI applications that provide tailored recommendations, enhance customer support or improve accessibility. This level of customization was previously challenging and time-consuming but is now more achievable. – Thomas Griffin, OptinMonster
15. Enhancing Defect Detection And Process Optimization
In terms of product quality control, foundation models could enhance defect detection and process optimization. By analyzing vast amounts of sensor data and production metrics, these models could identify subtle patterns that lead to quality issues. This would allow businesses to implement predictive maintenance, reduce waste and consistently deliver higher-quality products to market. – Marc Fischer, Dogtown Media LLC
