May 23, 2023 | 4 min read

Like most of the world, we’ve been engrossed in the conversation around generative AI since the release of OpenAI’s ChatGPT 4 – an artificial intelligence (AI) chatbot that uses natural language processing to generate responses, compile information and provide answers to human queries. While this technology has been around for many years, ChatGPT’s public release has sparked fascination and fear in almost equal measure.

While news sites, talk shows and podcasts all report on these new all-in-one solutions and transformational use cases, there are plenty of skeptics – not to mention those who predict AI will eventually take their jobs. Not a day goes by without something new on the topic. So we wanted to shed some light on this technology, bring some balance to the conversation and help you understand the impact Generative AI can have on your business.

What is Generative AI?

Generative AI is simply an AI model designed to create a high-value output or asset. By training a machine learning model to understand patterns and structures, it uses that knowledge to create new, original content. A good example other than ChatGPT are Midjourney and DALL-E 2, which each produce AI-generated images.

For years, we’ve been utilizing machine learning (ML) because of its ability to support decision making and Conversational AI to make information instantly accessible, improve customer journeys and strengthen the customer experience.

“Most AI systems today are classifiers, meaning they can be trained to distinguish between images of dogs and cats. Generative AI systems can be trained to generate an image of a dog or a cat that doesn’t exist in the real world. The ability for technology to be creative is a game changer.”


But since generative AI has exploded onto the scene, the conversation has shifted. We see companies exploring the possibilities and searching for use cases to prove the argument for and against. This makes it even more important to understand the differences between these AI models.

Traditional ML is mainly used for decision making, while generative AI in this context is used to create new content. This is a useful distinction when exploring the variety of AI tools and applications available and starting to define value-adding use cases.

Some business use cases for ChatGPT

ChatGPT’s language model makes it especially useful when it comes to understanding common queries and generating text-based responses, fast. The most scalable use cases are those that help users by automating a quick answer to common pain points. These range from surfacing answers from FAQ pages and scheduling meetings as a customer-facing chatbot to providing admin support by summarizing action points from a call transcript or compiling a list of survey questions. But it isn’t without its faults, so the right approach is key.

What is the goal of AI and how should we approach it?

No matter which model you use or which use cases you’re considering, it’s important to define the role of technology and your approach to AI as an organization.

The objective of Artificial Intelligence (AI) is not and never should be to replace humans. Instead, AI should support your people for a more efficient, accurate and high-quality output. It therefore requires commitment, financial resources, and the involvement of experts, along with careful governance and change management.

  1. AI is not a quick fix
    Many companies see AI as an opportunity to reduce costs, focusing on the technology or algorithm’s ability to save on human resources. A collaboration model between humans and technology is often overlooked but in practice, it’s essential to ensure successful implementation. We’re passionate about the fact technology alone cannot achieve the desired impact and this makes AI deployment an ongoing process to be managed and monitored as part of a wider digital transformation strategy.
  2. Much of the effort will stay with people
    Without human expertise, technology and algorithms can only function to a limited extent. For example, a company using AI to project next year’s sales would need to draw upon human experience. In one such case, the algorithm and technology alone were not sufficient to provide actionable results. Only by bringing in human resources and integrating their talent for data analysis were they able to save hundreds of millions of dollars.
  3. Understand it’s limitations
    While technology like ChatGPT proves popular, it’s not 100% reliable as a source of information for businesses and is prone to errors. If humans are not sufficiently involved, the algorithm is used incorrectly, or the training data is incomplete, AI’s use (or misuse) can pose its own risks. For example, important data may be rejected because it does not fit the algorithm or, more critically, the risk of using incomplete data may be misjudged, leading to poorly informed decision-making.

Read more about creating a harmonious relationship between people and technology in our Human+ whitepaper >>>

Looking beyond hype, hope and fear

With the increasing hype surrounding OpenAI, there are inevitably different arguments for and against the democratized use of artificial intelligence. From fake news and misinformation to bias and political influence, AI has the power to be disruptive both socially and politically. But when it comes to technology, the possibilities are exciting and there are innumerable opportunities yet to be explored – many of which will prove useful to businesses.

Within each area of influence, there are both advantages to be gained alongside the need for careful monitoring of access and outcomes. Even Jürgen Schmidhuber, dubbed the ‘father of AI’ warns of the risk of this technology getting into the wrong hands. But like any advancement, he understands that it can’t be taken back and believes that “the same tools that are now being used to improve lives can be used by bad actors, but they can also be used against the bad actors.”

So, along with governance and regulation, it’s clear AI needs to be combined with robust cybersecurity measures along with more research to understand how AI can be misused so such risks can be mitigated.

Leveraging Cloud-based AI infrastructure

With more and more companies investing heavily in AI-related initiatives focused on specialized development of comprehensive automation platforms, cloud-based machine learning solutions, and tailored IT services, many cloud providers, including Microsoft Azure, Amazon Web Services, and Google Cloud Platform stepped up to offer the necessary computing power and storage capacity. This allowed for a virtually limitless utilization of AI models and facilitated scalability in terms of storage and processing capabilities – something that businesses should take into account when investing in AI.

Revolutionizing business strategy

Automation and digitization, including the integration of AI, have become a top priority for C-level executives as they reshape how businesses create value, engage with customers and partners, and navigate markets. It’s no longer a ‘nice to have’, it’s now a business imperative needed to enhance quality, expand product/service offerings, and gain that all important competitive edge.

Go All-In with confidence

It’s clear that Generative AI technology such as ChatGPT will have a huge impact on businesses in the future.

The key is in the approach. The requirements and outcomes of an AI deployment are often misunderstood, and resources often not allocated properly. The focus is usually too much on the technology itself. So education, training, and advice should be sought by any business wanting to take advantage of digital transformation.

When we define use cases for AI and automation technologies, we take a holistic approach that harnesses the power of people, processes and technology, as well as our compliance and cybersecurity expertise. We advocate for ongoing training and responsible governance and ensure your business goals are at the heart of every transformation strategy.

If you’d like to learn more, book a meeting with one of our Automation Engineers.

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