You already know that artificial intelligence is a game-changer. You also know that you need to implement it strategically to capture its full potential. Let’s talk about that.
What is Generative AI, Really?
In November 2022, ChatGPT changed the world. Developed and maintained by OpenAI, the chatbot introduced consumers to what was, at the time, still a relatively novel concept: AI content generation. In just two months, the software attracted over 100 million users.
A little over two years later, and generative AI has permeated just about every industry.
How the technology works is deceptively simple. It starts with a specialized AI model trained on billions, sometimes even trillions of datapoints. Over time, the model starts to recognize semantic patterns in its training data, which it can then use to generate content.
Unsurprisingly Large Language Models (LLMs) like ChatGPT are the most widely-known of these, generating text based on user queries. Along that same vein, there are also GenAI models for image, video, audio, and code.
Outside of producing content, GenAI can also be leveraged for predictive analytics, data processing, and automation.
Business AI vs. Consumer AI
In 2023, Samsung experienced multiple data leaks, including internal meeting notes, design documentation, and source code — all because it allowed its workers to use ChatGPT. The semiconductor giant overlooked the fact that, like most consumer GenAI tools, OpenAI’s software trains itself on user inputs. Later that same year, Samsung and several other companies restricted the use of generative AI by employees.
The issue wasn’t with GenAI technology itself.
It was the use of consumer software in a business context. Most free AI tools aren’t built for security or compliance, instead being designed first and foremost for ease of access and ease of use. Uploading anything sensitive or proprietary to these platforms is asking for trouble.
A GenAI tool designed for business use, on the other hand, generally:
- Regard all data as sensitive, and allow a business to opt-out of its prompts being used for model training
- Support security measures such as role-based access control and multifactor authentication
- Accommodate regional and industry regulations around data security
- Are built with a security-by-design mindset
Security aside, consumer GenAI tools typically adopt a one-size-fits-all approach. They can’t be tailored to a specific application or use case, nor are they designed to integrate with enterprise systems and software. They tend to struggle with industry-specific terminology, and lack contextual awareness into your business’s operations.
In other words, while well-suited for general queries and non-sensitive work, consumer GenAI falls woefully short for anything more specialized or complex.
The Capabilities (And Limitations) Of Artificial Intelligence
As with any new technology, there’s currently a bit of a disconnect between what generative AI can do and what some people claim it can do. While it’s inarguably one of the most disruptive technologies ever developed, it isn’t a magic bullet.
It’s crucial that you understand both where the technology excels and where it falls short.
What GenAI Can Do
We’ll start with the most obvious: Customer-facing professions such as sales, marketing, and support. These fields saw the most immediate gains from GenAI, and continue to benefit from the technology in a multitude of ways:
- Sophisticated LLM-based support agents
- Personalized marketing messages
- Contextual guidance for sales teams
- Advanced consumer behavior and sentiment analysis
These use cases speak to one of the technology’s greatest strengths: Its capacity to ingest and respond to natural language. While there’s some nuance and finesse to prompts, GenAI doesn’t tend to have a steep learning curve. Consequently, this means that once you’ve selected a tool, it can usually be deployed with minimal training.
Accessibility aside, the other major draw of GenAI is its capacity to parse and recognize patterns in data that would be nearly impossible to sift through manually. GenAI software is incredibly well-suited for any analytics-focused use case, able to guide strategic decision-making through deep insights and recommendations that might otherwise be overlooked.
Cybersecurity represents another area where GenAI excels. AI-driven security tools can monitor an organization’s entire digital estate, dynamically identifying and remediating threats with minimal human intervention. This allows security teams to spend less time sifting through alerts and more time focusing on significant vulnerabilities and threats.
Other areas where GenAI’s data processing capabilities shine include:
- Diagnostics in fields such as radiology and neurology
- Projections and predictions in finance
- Quality control in manufacturing facilities
Last but not least, there’s workforce enablement. For starters, there are a great many repetitive tasks that can be automated through GenAI, including data entry, inventory management, and reporting. Beyond that, GenAI can be an invaluable tool for brainstorming, assisting employees with both research and ideation.
To give a more concrete example, let’s look at a few ways someone on your business’s marketing team might integrate AI into their workflows:
- AI-powered search platforms such as perplexity for competitive intelligence and market research
- Brainstorming and ideation tools such as ideamap
- Transcription software such as rev to capture key insights from meetings with customers
- AI assistants such as claude to fact check product messaging against documentation
- Customer experience and product analytics platforms like Amplitude
- Intelligent automation through Microsoft Copilot and similar solutions
What GenAI Can’t Do
While Generative AI has incredibly transformative potential, it cannot and should not replace human beings in the workplace. Even the most sophisticated GenAI platforms don’t actually create original content. They generate novel material based on patterns from their datasets.
While unmatched in its capacity for processing data, recognizing patterns, and automating manual work, GenAI cannot create or innovate — but that’s because it wasn’t necessarily designed to. The technology also struggles with:
- Understanding sarcasm, metaphorical language, and cultural context
- Grasping moral and ethical frameworks
- Making decisions with incomplete information
- Grasping implicit meaning
The reality is that artificial intelligence is at its best when paired with human intelligence, and it always will be. Empowered by AI tools, human employees can work smarter, faster, and more effectively.
Should You Train Your Own AI Model?
We’ll wrap things up with a deceptively simple question. Namely, should you license a model or build your own? Believe it or not, that isn’t an either/or question.
You’ve actually got a few different options:
- Fully pre-trained models
- Pre-trained models with the option for fine-tuning
- Low-rank adaptation (LoRA), which adds trainable parameters to an existing model
- Building a model entirely from scratch
In most cases, you’ll probably want to go with the first option, and ease into fine-tuning or LoRA. That said, if you aren’t sure which camp you fall into, there are a few things you can consider.
Intended Use Case
If you need something to assist with market research or ideation, there’s not much point in expending the time and resources to build something from scratch. An off-the-shelf option will work just as well.
On the other hand, if you’re deploying GenAI to assist in something like scientific research or product defect detection, you may want something with a bit more customization.
Requirements
Outside of what you need your model to do, you’ll also need to think about performance, cost, and compliance. For example, an off-the-shelf model might not be sufficient if your business:
- Operates in a highly-regulated industry
- Requires an extremely high level of speed, accuracy, and reliability
- Has unique integration or implementation requirements that aren’t met by existing vendors
Resources and Expertise
Although custom AI models are more accessible than they’ve ever been, training is still a time-consuming and costly undertaking. It requires considerable knowledge and expertise. You’ll also need a massive, high-quality dataset.
Remember also that GenAI is also extremely resource-intensive. If you plan to operate your model internally, make sure you have the infrastructure to support it.
Going Beyond the Basics
Generative AI is arguably one of the most compelling technological advancements the world’s ever seen. But accessing its full potential requires you to do more than download a few free tools. You need to think carefully about where and how you want to apply it, and keep in mind what the technology can and cannot do.
More importantly, you need to take the necessary steps to ensure your business is ready to actually leverage the technology before you commit to a deployment. Stay tuned for our next blog, where we’ll walk you through preparing your business for AI adoption.
Are you starting to explore new ways your business can use Generative AI? Feel free to reach out to see if your business is AI-ready.
