It seems like everyone in the business community is talking about “AI” or “artificial intelligence” lately. It’s not all hype: by 2025, analysts expect the global AI market to soar to a whopping $190.61 billion!
As leaders and managers, AI demands our attention. The first place that many of us start is with ChatGPT, which is a large language model that can follow instructions and respond in a way that sounds human—often with impressive results.
But here’s the thing, ChatGPT and models like it can only take your business so far. They require a lot of hand-holding to understand the nuance of your business, from customers, operations, the markets you operate in, to internal processes. In other words, they’re like a jack of all trades—but master of none.
This is where fine-tuned AI models come in.
Let's break it down in the simplest way: imagine you have a talented employee (let's call him "AI Andy"). Now, AI Andy is incredibly smart. But when he first joins your team, his skills are quite general. He's pretty good at many things but doesn't necessarily know the ins and outs of your specific business.
That's where fine-tuning comes in. Think of it as giving AI Andy a crash course in the unique operation of your business. It’s like turning AI Andy from being just smart to being super-smart and specialized just for you.
This transition, this "fine-tuning", is how AI Andy becomes a rockstar for your specific business needs. And in the next few sections, we’re diving deep into 5 reasons why you'd want such a specialist on your team.
According to a report from Accenture, AI technologies could increase labor productivity by up to 40% by 2035. Now, that's a game-changer. And for those wondering about the real-world impact, let's dive into some tangible examples.
JP Morgan's "COIN" AI system is a fantastic illustration. Traditionally, their legal teams would spend a staggering 360,000 hours annually reviewing documents. COIN, once fine-tuned for the task, now handles the same volume in mere seconds. Imagine reallocating those human hours to tasks that truly need a human touch. That’s efficiency personified.
Another example to consider is the medical field. Zebra Medical Vision, an AI health startup, has developed AI models that can read and diagnose medical images. These fine-tuned models can identify diseases like breast cancer and cardiovascular issues in a fraction of the time it takes human radiologists, with a whopping 90% accuracy.
Consider e-commerce apps like Shopify that leverage AI apps to help businesses answer customer queries and offer 24/7 assistance. This doesn’t just lead to happy customers but also allows their human support teams to focus on more complex, personalized customer interactions.
Let’s also look at Unilever, a global consumer goods company. They've integrated AI into their recruitment process, screening entry-level employees. This has saved 100,000 hours of interviewing time and around $1m in recruitment costs every year. The HR team now dedicates more time to strategic planning and workforce development.
These stories and numbers don’t lie. A well-implemented, fine-tuned AI model isn't a futuristic concept. It's here, and it's revolutionizing the way businesses, big and small, operate. By taking care of repetitive tasks quickly and efficiently, AI ensures that your team can focus on what truly matters: innovation, relationship-building, and growth.
There’s a universally accepted truth in the business world: it's not just about the money you make, but also about the money you save. With technological advancements, AI emerges as a beacon of cost efficiency. Let’s explore how fine-tuning AI models brings substantial savings to businesses.
Let's start with a scenario: imagine a business process that requires ten employees working full-time. With a fine-tuned AI model, the same workload might be managed with just two employees overseeing the AI. The savings here are twofold: not only in terms of salaries but also the ancillary costs related to employees, such as training, benefits, and office space.
And we’re already seeing this happen in the business world. According to Micro Sourcing, the adoption of AI in recruitment has resulted in a cost reduction of 75% per resume screen, a 35% reduction in employee turnover, and a 4% increase in revenue per employee.
Another example is from the banking sector. Bank of America introduced Erica, an AI-driven virtual assistant, that handles a myriad of customer queries, from balance inquiries to transaction histories. By doing so, the bank not only improved its customer service response time but also significantly cut down on the labor costs associated with call centers.
Human error is inevitable — we all make mistakes. But in the business sphere, these mistakes can be expensive. A survey by Gartner highlighted that every year businesses lose $12.9 million due to poor data quality. Enter fine-tuned AI. With their meticulous processing capabilities, AI models significantly trim the error rates.
The best example here is the healthcare industry. The Johns Hopkins School of Medicine reported that medical errors cause 250,000 deaths in the US per year. AI systems, when fine-tuned, have been instrumental in reducing these errors, leading to improved patient care and substantial savings on potential legal claims and treatment costs.
A Harvard study shows that companies that used artificial intelligence in their sales and marketing saw an increase in lead generation by more than 50%. Why? Because a model aligned with your business specifics understands your audience, market dynamics, and industry trends at a granular level.
For instance, consider the music streaming titan, Spotify. While there are generic algorithms to recommend songs, Spotify went a step further. They fine-tuned their AI models to understand individual listening habits, moods, and even the times users were most active. As a result, they could curate "Discover Weekly" playlists that felt intensely personal to each user. This tailored approach contributed significantly to their user retention rates.
In today’s digital age, the power dynamic has shifted firmly into the hands of customers. Their expectations are skyrocketing, and businesses must rise to the challenge. According to PwC, 80% of American consumers say that speed, convenience, knowledgeable help, and friendly service are the most crucial elements of a good customer experience. Clearly, the stakes are high, but how can fine-tuned AI models elevate the customer journey? Here are three ways:
Everyone appreciates the personal touch. According to Epsilon research, 80% of consumers are more likely to make a purchase when brands offer personalized experiences.
A generic AI might understand what a customer wants, but a fine-tuned AI understands your industry and target audience way better.
Take the case of Amazon. It offers a personalized experience through its robust AI recommendation system. By analyzing user browsing and purchase history, as well as similar user behaviors, Amazon's AI models suggest products that align with individual preferences. This approach enhances user engagement and increases the likelihood of purchases. Additionally, features like 'recommended for you', 'products you might like', or 'customers also bought' further contribute to a personalized shopping journey, creating a tailored experience for each user.
One step beyond personalization is anticipation. A fine-tuned AI model can forecast customer needs, sometimes even before they're aware of them. For instance, American Express uses its AI-driven systems to analyze transactions and alert users about potential fraudulent activity, sometimes catching these irregularities before customers notice them.
Fine-tuned AI chatbots have revolutionized customer service. According to Invest, 67% of global consumers interacted with chatbots for customer support in the past year. These aren't the generic "How can I help you?" chatbots — these are sophisticated, fine-tuned models that understand context and sentiment, and can even predict user queries.
H&M, the retail giant, offers a stellar example. Their chatbot not only handles customer inquiries but also offers fashion advice based on current trends and user preferences, enhancing the shopping experience and boosting sales.
Growth is a universal goal for any ambitious business. But as many entrepreneurs can attest, scaling operations isn't just about expanding; it's about expanding efficiently and sustainably. But how does a fine-tuned AI model fit into this picture?
Well, as demand spikes, so does the pressure on operations. Often, businesses struggle to maintain quality amidst increased volume. With fine-tuned AI models, routine processes are automated, ensuring consistency regardless of scale.
An illustrative example here is Dropbox. With millions of files uploaded daily, their AI system automatically categorizes and organizes data, ensuring that even as the user base grows, file retrievals remain swift and accurate.
The really good news is that you don’t have to be an AI engineer from MIT to fine-tune AI models. At Entry Point, we break down the process into three simple steps:
Identify the task you want your model to perform. This can include tagging, data extraction, classification, summarization, translation, and even conversational chatbots.
Import your examples. This is the data you’ll train your AI model on. You can upload a . CSV file to speed up the process.
Run fine-tunes across different base models. Plus, you can improve your model by having Entry Point automatically generate more training data for you.
And that’s it! You now have a fine-tuned AI model trained on your company’s data. There won’t be a more efficient AI model for your business out there! But don’t take our word — try out Entry Point today to experience the power of a fine-tuned AI model.