The Challenges of Implementing Generative AI at Work


September 5, 2023

The Challenges of Implementing Generative AI at Work

After the launch of some hugely popular generative AI tools late in 2022, the interest in AI has reached a new high. Talks of how to utilise AI has been a hot topic for years, but it seems that this year it has exploded; with research activity around – generative AI – increasing by 247% from Q1 – 23 vs. Q2 – 23.

Many teams are looking for ways to harness the power of AI in all areas, but crucially in order to increase customer engagement, improved decision-making, reduce demand, increase efficiency and optimise content and products.

However, making the most of AI isn’t always simple, and projects can turn into an uphill struggle. This can be caused by issues with back-end support to enable AI to do its job, getting internal buy-in and a number of other reasons.

In this blog post, we want to provide you with details on why some AI projects are failing and how you can get buy-in and ensure a successful AI implementation.

What is Generative AI?

Generative AI is a popular form of artificial intelligence technology. It can be used to craft diverse forms of content, encompassing text, imagery, audio, and synthetic data. What has recently spurred considerable attention toward generative AI is the advent of user interfaces characterised by their user-friendliness, enabling the rapid creation of high-caliber text, graphics, and videos within a matter of seconds.

Although this technology isn’t brand new, the inception of generative AI traces back to the 1960s, when basic forms were embedded within chatbots. However, the real watershed moment transpired in 2014. This juncture saw the introduction of generative adversarial networks (GANs), a subtype of machine learning algorithm that instigated a monumental shift. GANs empowered generative AI to produce remarkably authentic images, videos, and audio recordings of actual individuals, ushering in a new era of capabilities.

And more recently, the launch of free-to-use tools such as ChatGPT, Google Bard and Midjourney have excelled its popularity in 2022-23 as mentioned earlier.

It is also interesting to note that although you may not realise it, many of us have been using AI tools for longer than you’d realise; with popular apps like Google Maps and Grammarly having AI integrated within them.

How Can You Use AI at Work?

Whether you were an early adopter or not, there are still a number of ways that you can implement generative AI to help you do your job better. And this isn’t just using ChatGPT to write your emails.

Here is a list of the most popular ways AI is being used to transform organisations from TechTarget:

Growth stat: TechTarget Activity Data, Worldwide, Q1 2023 - Q2 2023

Top markets researching: TechTarget Audience Data, North America, Last 90 Dats from July 2023

What are the Benefits of Using Generative AI?

Business Intelligence & Analytics

Generative AI enriches business intelligence & analytics by generating insightful data patterns, facilitating advanced predictive analysis, and aiding decision-making processes.

Customer Experience

Generative AI simplifies the process of personalising interactions, tailors recommendations, and creates immersive content, enhancing engagement and loyalty.

AI & Data Science

Generative AI automates data augmentation, accelerates model training, and aids in complex data synthesis, bolstering research and innovation. Simplifying the process of gathering data and getting it into a usable and understandable format.

AI & Machine Learning

Generative AI advances AI & machine learning by generating diverse datasets, aiding model creation, and enabling creative exploration of algorithms and architectures.

AI-enabled Infrastructure

Using AI within infrastructure allows you to optimise resource allocation, automate system tuning, and improve efficiency, transforming how infrastructures are designed and managed.

Software Development

You are now able to automate code generation, suggest design elements, and speed up development cycles.

AI-enabled Security

Security is being transformed by AI, as it allows you to identify vulnerabilities, simulate attacks, and plan robust defense strategies, fortifying digital landscapes.

Content Management and Services

AI is giving teams the ability to further tailor content, automate curation, and refine content discovery, speeding up the content creating process.

Natural Language Processing (NLP)

AI can generate coherent text from live speech, aiding language understanding, and enabling more nuanced human-AI communication.

Customer Service and Support

Automates customer responses, provides real-time assistance, and enhances self-service options with AI, elevating customer satisfaction and operational efficiency.

Challenges Implementing AI

So with all these clear benefits of using AI, why are many projects failing to get off the ground?

In this section, we list the most common reasons that are stopping organisations from using AI:

Data Quality

AI is hugely reliant on the data that it has available to it. If the quality of the data is low, then the output that it creates will also be low. So before you implement AI, you need to ensure that your data is relevant and readable.

To ensure that your data is in a usable state, we recommend that you complete a full data audit to ensure that data is accessible, complete and appropriately formatted.

One of the challenges around this is that many organisations are finding that big datasets do not work as well with AI as smaller datasets. Smaller datasets provide more context for analytics and make their AI solutions less data demanding.

Learn more about preparing your data here.

Integrating into Legacy Systems

When implementing a new AI tool, many IT leaders need to decide whether to integrate them into or replace older systems. As incorporating generative AI into legacy systems and environments can lead to challenges in the future.

For example, an ambulance service implementing AI-powered resource allocation may clash with established dispatch protocols. Harmonising these divergent approaches requires meticulous planning, potentially involving the creation of intermediary solutions to enable seamless communication.

These challenges can arise as most legacy systems have a very specific way of doing things and are programmed to perform a single task. Contrary to that, AI leverages different types of thinking, which can clash with legacy systems and processes which may raise new challenges.

To tackle this Pablo Alejo, from West Monroe says “Organisations have to find new ways to either create integrations or adopt new capabilities, with new technologies, that enable them to reach the same outputs, or outcomes, faster and more effectively.”

Employee & Stakeholder Buy-in

AI has always been a divisive topic, with some people loving it and others hating it. This debate is excelled when it comes to using AI in the workplace as some people may have worries about losing responsibilities. A lack of understanding of the technology can also lead to resistance.

And without buy-in from the people who will be using the tools, you are unlikely to reach the potential returns.

That’s why educating your team on what AI can do to make their jobs easier and make it clear that the tools are there to improve the quality of their work and not to replace them.

Internal Skill Shortages

Since the recent rise of access to AI tools, there have been brand new job titles popping up, such as AI Prompt Generator, whose sole job is the use AI tools to their maximum potential.

As all AI tools are programmed in unique ways and follow differing rules of analysis, how you prompt them will give various outputs. There are a bunch of great resources out there to help you get the most from these tools using refined prompts (like this), but many organisations are looking for specialists that will research how each tool is developed to ensure that you are using it to its full effect.

However, as this is a brand new position in many organisations, there aren’t ma internal resources available and experts in this area aren’t yet widely available.

Building the Business Case

Implementing the right generative AI tool and maximising the benefits that you get from it through data and processes can create huge cash and resource savings. However, many tools come at a significant cost when you factor in the price of the technology and the resources that are needed to successfully implement it.

That’s what you need to create a bullet-proof business case to present to key decision-makers that cover all bases.

How FourNet Can Help

FourNet has a proven track record of successfully implementing new AI solutions and working with organisations to ensure that they are getting the most from their tools. We are experts in assessing and analysisng data to see where pain points arise and providing tailored recommendations for what technology is right for you.

Helping you to build a comprehensive business case to take to your team that will show actionable steps, key challenges and opportunities and a clear return on investment.

Learn more about how we can help you use: