The AI race is on, with ChatGPT unofficially leading the pack. Since then, companies, regardless of size, are embarking on their respective AI implementation plans. ChatGPT, combining deep learning and natural language processing capability to produce generative AI, is a game changer in many business use cases.
As the key person leading transformational change in your company, it makes sense for you to seek or develop AI solutions for business. Up to 37% of companies in the US have somehow adopted generative AI. Yet, generative AI is still an evolving field, with multiple hurdles that data scientists, machine learning teams, and developers must grapple with.
As the technical lead and co-founder of Uptech, I’ll share our team’s experience in implementing artificial intelligence in various apps. Last year, we built several apps incorporating powerful Generative AI capabilities, including Dyvo.ai, Angler,ai, Hamlet, and an AI-powered virtual assistant for Plai.
What the numbers say about Generative AI in business
ChatGPT made history for being the only app to garner one million users in less than a week. And there’s more to its meteoric rise than mere buzz. Its ability to write creative stories and demonstrate programming codes impresses both business users and consumers alike.
Shortlist surveyed 500 employers, and 33% agreed that ChatGPT would boost productivity by up to 74%. On that note, the tech industry might witness the most considerable changes, with 43% of millennials worrying about losing their jobs to AI.
Job loss worries aside; companies continue to adopt ChatGPT and other generative AI solutions at a rapid pace. According to ResumeBuilder, 49% of companies have used ChatGPT by Feb 2023, with 30% to follow suit shortly. More importantly, there are significant financial implications in adopting ChatGPT, with 25% of surveyed companies reportedly saving more than $75,000.
With ChatGPT proving to be a practical game-changer in business, companies showed interest in expanding their investments in Generative AI. According to a PitchBook report, venture capitalists injected $4.5 billion worth of investments into generative AI deals in 2022. The figure far dwarfs the $408 million invested in 2018. Likewise, Goldman Sachs is optimistic about the economic implications of generative AI, forecasting a global GDP growth of $7 trillion.
How Generative AI benefits businesses
Businesses face cost pressure, productivity concerns, and an influx of data in today’s digital environment. Generative AI solutions offer timely benefits to help companies to navigate challenges in competitive markets. I share several examples below.
Increase process efficiency
Generative AI uses deep learning models capable of processing large numbers of information in real time. More importantly, they can be trained with business-specific datasets that allow them to replace humans in specific tasks. With a smarter virtual assistant that works tirelessly, businesses can refocus their workforce on tasks that require creative human input. For example, marketers can use AI tools to outline SEO strategies instead of creating them from scratch.
Automate business processes
Many companies still rely on a manual workforce to coordinate business operations. This affects their agility and ability to respond promptly to changing market dynamics. Already, AI companies have repurposed generative AI models into automation-capable solutions, such as InstructGPT, to perform follow-ups of the initial prompt. For example, you can ask the AI software to ‘build a real estate business’, and it will respond with a strategy, design a website or write scripts for phone calls to prospective clients.
Improve decision making
Businesses sit on top of voluminous data, which might prove helpful in supporting decision-making if they are correctly harnessed. Conventional business intelligence software cannot ingest and analyze textual data in ways that deep learning models can. For example, you can use generative AI to detect specific patterns from sales data and predict probable outcomes to support your next move.
Create personalized experience
Generative AI allows businesses to shift away from the one-size-fits-all customer funnel into one that caters to individual preferences. This way, customers enjoy a highly-personalized experience that encourages them to stay loyal to the brand and increase spending. For example, Amazon uses Generative AI to suggest relevant products based on the customer's past transactions, browsing behaviors, and current activities.
Top concerns about using Generative AI
Unsurprisingly, businesses will welcome the powerful capabilities that generative AI offer. Yet, you should be pragmatic when integrating generative AI capabilities into your business. There are still ethical and practical concerns about the technology that remains unsolved.
- Pre-trained generative AI models like GPT often needs to be further fine-tuned before you can use them for specific business use cases. For example, ChatGPT has undergone semi-supervised training involving human feedback before it becomes a chatbot that understands human instruction.
- Even with adequate training, the model might make mistakes or produce biased responses. For example, an AI-powered loan application system might be biased and disqualify specific applications because it was not trained with enough data.
- Not all business professionals welcome Generative AI with open hands. They are concerned that AI will take over their jobs, which they have every right to be. A recent BBC report indicated that AI might replace 300 million jobs globally. That said, AI implementations will also create new job opportunities requiring new skills, such as prompt engineering.
Practical Generative AI Applications in Business
So, what can you do with generative AI technologies? How does it affect your business operations? Let’s explore the ways generative AI is transforming different job roles.
Content creation involves writing marketing pitches, blog articles, and other textual copies, which might take hours. With generative AI, marketers can draft the initial copy in seconds and make further edits. For example, by specifying simple instructions, you can use tools like Jasper.AI to create copies based on the AIDA framework.
Generative AI is also disrupting the graphic design space. It powers tools that help graphic designers brainstorm fresh ideas and deliver publish-ready photos. AI models like MidJourney allow anyone to create professional renderings, while our own AI solution, Dyvo enables marketers to generate studio-quality product photos for ecommerce stores.
Generative AI has yet to impact video creation in the way it did in image or text creation. There is still a need for substantial editing before the generated videos are fit for production. Works are, however, in the pipeline to elevate the roles of Generative AI in this space. For example, Runway has revealed a powerful AI system capable of generating new realistic videos from text, images, and existing videos. Meanwhile, Synthesia.io, allows users to turn text into a video narrated by human-like AI avatars.
Translating text or speech from one language to another requires a profound understanding of both languages' cultures, nuances, and contexts. Generative AI models can assist human translators by simultaneously translating the original text into multiple languages. Given that large language model like GPT are primarily designed for natural language processing tasks, such tools will likely provide near-fluent translations.
Having a chatbot capable of gathering new ideas from existing data is helpful for many businesses. And Generative AI makes this possible. For example, Plai is a digital solution that helps HR managers review employee performance. We added a new AI feature that allows managers to create employee development plans with feedback from the generative model, taking into account the results of the review. Or managers can create and refine their goals (OKRs) with AI, and suggest if your department goals are not aligned with the company strategy.
Whether in logistics, sales, or inventory management, employees will benefit from the automation capabilities that generative AI brings. For example, production managers can use AI software to optimize resource allocation, scheduling, and inventory movement to improve manufacturing efficiency.
Generative AI powered by language models is inherently expert coders. Models such as Amazon CodeWhisperer or Github Copilot demonstrate a strong understanding of programming languages, structures, and usages. Therefore, they will assist software development teams in writing, building, and testing codes. Some researches indicated that developers using these models complete programming tasks 50% faster.
Customer support teams are tasked to provide prompt resolutions, and they’ll benefit from generative AI-powered agents. When trained with specific products or services, the generative AI model can interact with customers like human personnel do. So, you can use these chatbots to filter and respond to common queries and escalate complex ones to your support team.
How to implement Generative AI into your business
Generative AI implementation will benefit your business, but where do you start? Here’s a step-by-step guy to introduce AI capabilities in your company.
1. Assess your requirements
Business benefits from AI in different ways. As such, you must identify areas where generative AI proves most impactful. For example, if your business is heavily marketing-oriented, using AI to write ad copies will reduce your team’s workload. Also, it’s essential to consider the cost of training and maintaining AI systems. Generative AI models must be trained with large numbers of data, and the cost may be prohibitive for some companies.
2. Choose the right generative AI model
The term generative AI broadly covers a system designed with one or several deep learning models, including a transformer, generative adversarial network (GAN), and variational autoencoder. Each model has its respective strengths, disadvantages, and use cases. For example, GANs are helpful in generating new images, but transformers allow you to build ChatGPT-like applications.
3. Prepare training data
Deep learning models for generative AI implementations require immense data to train with. Besides quantity, training data quality is also crucial. Therefore, you must gather raw data from diverse sources. Then, you’ll need to clean, label, and review the annotated data to ensure they are fit for training the model.
4. Train and fine-tune the model
Next, you train the model with the prepared datasets. The model would learn in self-supervised mode and adjust its hyperparameters accordingly. The process goes on iteratively until the results converge at the optimal point. It’s important to realize that training a deep learning model is a compute-intense process. You will require powerful computers equipped with GPU or TPU to train the models. That's why it's preferable to avoid training new model from scratch, if you can fine-tune existing one.
Pro tip: Remember that mistakes could happen in training, or you might need a different training algorithm to produce a better-fitting result. This will incur additional costs and time as you retrain the model. Also, there are times when you’ll need to fine-tune the model to align its performance to your business goals further.
5. Develop the generative AI solution
Once trained or fine-tuned, your next step is integrating the generative AI model into your business workflow. This involves developing a new application or adding new AI features to an existing one. When doing so, consider the computing infrastructure required to deploy and scale the AI workload. For example, you’ll need a cloud-based service to run the AI models and secure data pipelines to safeguard information privacy.
6. Monitor and refine the model
Generative AI models might suffer from performance issues when deployed. For example, your AI solution fails to respond accurately to new data despite showing ideal results during training. Or it may demonstrate bias or hallucination. Such issues call for further fine-tuning to adjust its weights and biases.
Challenges of implementation of generative AI
Even with your best efforts, using AI in business is fraught with challenges, particularly generative AI. These are your most significant hurdles.
- Training data quality directly influences the model’s output. If you provide questionable datasets, the model’s performance will be impacted.
- Some companies underestimate the resources to train a foundational model. OpenAI spent around $4.6 million to train the GPT model, which it later fine-tuned to produce ChatGPT. Instead of training a model from scratch, it’s more cost-effective to fine-tune pre-trained ones for your business needs.
- As powerful as they are, Generative AI could be better. You will still find irregularities, such as bias and hallucinations, on models you’ve trained extensively.
- Not all business processes will notice significant impacts from AI. So, integrate AI into the workflow that provides the most significant operational and financial returns.
- Building generative AI solutions involve storing, moving, and processing large volumes of data. To prevent data risks, use encryption and other data protection measures.
How Uptech can help to implement generative AI
At Uptech, we don’t only blog about generative AI but have built such solutions for our clients. Our team taps into years of experience in app development and knowledge of emerging AI technologies to provide generative AI solutions for various business applications.
- We’ve built Dyvo.ai, an AI-powered app allowing users to create unique avatars from selfies. In this project, we trained and fine-tuned the AI model on the cloud to produce unique photos. From here, we expanded Dyvo.ai for business use cases by enabling the app to generate mesmerizing brand-aligned product photos with AI.
- Our team augmented Plai, a digital HR solution, with generative AI. This allows managers to summarize performance reviews and provide feedback to employees automatically.
- Meet Hamlet, an AI-powered text summarizer built with the tech-davenci-003 model. It allows users to create text summaries from PDF files.
- And there’s Angler.ai, which lets marketers align their campaigns to relevant target audience and social network platforms with AI algorithms.
We took a user-centric approach in every app we built to ensure product-market fit. While our team strives to bring state-of-the-art technologies to businesses, we’re equally concerned about solving real-world problems. As such, we test your idea by preparing a PoC within 3 months, before designing an AI solution that your customers find useful.
Listen to our client’s experience with our AI development team.
"Uptech is a great partner for software and web development projects. I was impressed with the talent level for each of the roles, including design, front-end, back-end," Indy Sheorey, Founder & CEO, Angler AI.
Generative AI will transform the business landscape in ways never seen before. It offers improved efficiency, automation, and personalization when appropriately integrated. On paper, incorporating AI capabilities is a systematic process of training deep learning models. Yet, several challenges remain unresolved, which underscores the value of engaging field-proven AI development providers.
Talk to our team now to start implementing generative AI in your company.