AI shows no signs of losing momentum, with every type of business considering the use cases it offers. This is creating incredible advancements, boosts in productivity, and business growth.
Are you yet to embrace the benefits of AI in your enterprise? Now is the time, and one of the the most effective forms of AI to implement into your business is Generative AI. Generative AI is responsible for some of 2024’s most well-known AI agents. ChatGPT and Gemini, to name just two, which have certainly taken the world by storm.
These large language learning models specialise in understanding and generating natural language, making them ideal for powering chatbots in business settings. However a chatbot is far from the only way to use this brilliant technology, though. In this guide, we’ll explore all the clever ways you can make Generative AI your business’s number one secret weapon.
What is Generative AI (and why all the hype?)
In simple terms, Generative AI is a form of artificial intelligence capable of generating text, images, videos, or audio. The form of Gen AI that has grabbed the most headlines is the text-generating large language model (LLM). Examples of LLMs you may have heard of include Chat GPT (Open AI), Gemini, LLaMA, and Mistral.
Large Language Models are hugely powerful deep-learning models. Their vast training sets give LLMs natural language processing abilities that exceed previous AI technologies.
What is NLP?
Natural Language Processing (NLP) is an AI technique that focuses on enabling computers to understand, interpret, and generate human language. NLP combines linguistics, computer science, and machine learning so it can process and analyse large amounts of natural language data.
Because they understand natural language so well, LLMs grasp and act on free text instructions, you can chat with them and direct them like a human. This slashes development times and avoids the extensive programming for previous AI technologies. It’s this leap in productivity and ease of execution that’s so exciting for enterprises.
In short, combining AI and natural language processing makes artificial intelligence accessible to non-technical users and much cheaper to deploy.
What Can Businesses Use Gen AI for?
At Oakland, we are a data consultancy with a wealth of experience in everything data; from strategy and governance to data engineering, analytics and artificial intelligence. One thing we feel incredibly passionate about is simplifying data strategy by taking an easy, seamless approach that everyone in your organisation can understand.
What Not to Do
Imagine the huge amount of insight trapped within your business stored in free-form texts, videos and images. Now you can draw insights from these ‘hidden’ datasets. This vast data landscape has long been a double-edged sword for businesses—rich in information but daunting in scale and cost to analyse. Critical insights or risks are missed because they are buried in the data. AI represents a revolutionary shift in data handling because it can read, summarise and generate content at a scale unimaginable by human workforce.
Businesses can now harness the advantages of Generative Artificial Intelligence to build their own solutions and applications. The AI services offered by Open AI, Azure Cognitive Services, AWS, and others allow enterprise users to call on large language models via API.
The natural language processing power of LLMs makes them well suited to a range of tasks, including:
- Interrogating data or public information sources to extract insights and summaries
- Summarising or extracting meaning from large volumes of unstructured information, such as free text
- Writing or checking code (developer assistance)
- Recognising patterns or objects in texts and images
- Executing rule based or repetitive tasks
- Validating documentation against standards
- Chatting in structured contexts (e.g. Q&A, user or customer support)
How Could We Implement Generative AI At Our Enterprise?
Enterprises looking to adopt Generative AI already face a range of technology options. But if you strip it back, there are three basic choices:
- Public models (e.g., Chat GPT online): allow you to simply access a website over the internet and chat with an LLM via your web browser. For example, the Chat GPT website or Co-Pilot online. However, these interactions are not private and typically not suitable for enterprise users.
- Plug-in tools: provide pre-packaged Generative AI capabilities. Some are highly specialised solutions for specific industries or use cases, but others aim for a wider enterprise user base. Perhaps the most famous is the Microsoft 365 Co-Pilot. 365 Co-Pilot provides Generative AI functionality via desktop Microsoft applications such as Teams, Outlook, and Word.
- Custom Generative AI Solutions: many enterprise use cases can’t be solved with public or plug-in AI tools. For most businesses, building your own LLM is the stuff of dreams. It’s too expensive and time-consuming. Thankfully, there is now a range of services that allow you to build your enterprise Generative AI applications. We could blow your mind with all of the details, but these services allow you to harness Large Language Models to build solutions and embed Generative AI into workflows. How much customisation and configuration you do is determined by your resources and chosen use case.
Always Start With The Use Case!
The Gen AI landscape is full of skeletons of proof of concepts that didn’t stick. If you want to make a difference, start with a clear use case. What is the clear and specific problem that you want to solve?
Finding the right use cases is key. But you need to know what to look for.
Every business is different. We’ve suggested some examples for different business functions further down this article. There are also some common characteristics of use cases for Generative AI that you can look out for.
In our experience, there are three key problem areas that Generative AI applications are great at solving:
- Unlocking your document mountain: the same mistakes occur time and again in businesses because knowledge isn’t shared. Often, hours are spent gathering documentation about ‘lessons learned’, best practices or incident reports. But the information is just too big and messy for anyone to use it. Important lessons are lost in the noise. If you can unlock Knowledge Management with Generative AI, you can massively improve process execution, compliance and knowledge sharing.
- Information tsunami: many businesses have invested in monitoring systems for their systems or assets, but now face an overwhelming volume of alerts and alarms. Monitoring and control teams are endlessly distracted and can’t separate the genuine alerts from the noise. Generative AI is great at scanning alarms and alerts and, crucially, relating them to operating procedures and policies so that informed decisions can be made.
- Expert shortage: data is so often meaningless without context. But, applying that context or audience-specific messaging is time-consuming. How many warning signs are lost in the data or missed because nobody spots their relevance? Many companies have implemented human ‘business partnering’ models in functions like Finance, HR and IT to connect end users to expertise, but these models are expensive and often overstretched. Generative AI is great at preparing reports, highlighting insights and answering detailed user questions in a relevant way. It has great capabilities in analyst and business partnering contexts.
Using an Intelligent Agent in AI
The buzz around generative AI is undeniable – but so many of the organisations we speak to are still struggling to find the right use cases, and prove the ROI which could be why Gartner found that nearly half of AI projects fail to meet their goals.
Finding a great enterprise use case for Gen AI is a start. But you also need the right solution. Large Language Models provide amazing capabilities, but you have to harness them in the right way. Chatbots are a great example of an LLM-powered solution, but they won’t solve every problem. If you want to really unlock Gen AI adoption at your enterprise, you need solutions that can do more.
At Oakland, we build Generative AI solutions with Intelligent Agents. Intelligent agents are AI-powered programs powered by large language models, but they also have powerful tools, careful task orientation, and context awareness. The easiest way to think of them is as highly scalable virtual workers. You can read more about Intelligent Agents and how we use them to solve complex business problems in our expert guide.
With Intelligent Agents, you stand a good chance of solving some of the use cases spelt out below.
Specific Generative AI Use Cases
Nothing beats a concrete example, so let’s explore some specific enterprise use cases for Generative AI. These are organised by business function:
Customer Experience
Customer chatbots are a classic use case for artificial intelligence and natural language processing. Generative AI can elevate these to the next level because of the ease and conversational nature of the interactions they can support. And the good news? chatbots based on LLMs require far less training and testing than previous generations of natural language processing techniques.
Complaint or feedback handling is another exciting use case. Complaints or customer feedback often means high-velocity unstructured data that could come in at any time. Generative AI is great at reading, summarising and categorising this information. This allows you to handle new complaints at speed and scale, but also extract detailed insights from the wider dataset of all complaints.
Equipping generative AI with policies, operating procedures, and communication capabilities allows you to build a ‘Complaint Manager’ intelligent agent that not only reads and categorises incoming information but also acts on it.
Knowledge Management
Unlocking knowledge management is a classic challenge for many enterprises, whether they have a dedicated Knowledge Management team or not. Most businesses have a document mountain piled high with ‘lessons learned’ documents, project updates, incident reports, guidelines, policies and the list goes on. But nobody can read and consume them – they’re too big. So the knowledge never makes it to where it’s needed.
Generative AI is a brilliant tool for unlocking this kind of information. You can use it to summarise information for users quickly. Users can query complex unstructured datasets through a conversational, natural language interface.
This unlocks and democratises information in a way that would have been prohibitively time-consuming or expensive before Gen AI.
A properly created Knowledge Management solution powered by Gen AI is like an always-on, knowledge librarian with an endless memory. If you want to go further, you can even empower the AI to proactively scan for new insights in its data store and alert users when they are found.
Health and Safety
Many businesses have complex H&S rules and policies that are difficult for front-line operatives and managers to navigate. Creating a virtual ‘Safety Assistant’ powered by Generative AI is a great way to make that information relevant and accessible.
Compliance monitoring is another example of where Gen AI “powered applications” can shine in Health and Safety. Project documents, job reports, incident logs etc, can all contain clues or early flags for non-compliance. Usually, H&S teams are too stretched to read these, but a Gen AI solution could be used to scan these documents and highlight risks or non-compliant activities before they become incidents.
Product Development and Supply Chain
Many organisations have huge volumes of customer feedback and market insights that they need to summarise quickly to drive product development. Generative AI has great potential to streamline the gathering and summarisation of this insight, putting it in the hands of product teams faster than ever before.
Within Supply Chains, even small quality issues or incidents can disrupt entire supply chains if not resolved quickly. Rapidly assessing the downstream impacts and alerting the right people is critical. But the required information maps often don’t exist and can’t be made quickly. So, communications can’t be directed, and issues spiral out of control. Generative AI capabilities can read and analyse complex product documentation, highlighting probable downstream impacts quickly when new issues are discovered.
Taking it further, a Gen AI-powered Intelligent Agent could monitor quality issues in real-time, rapidly assessing downstream impacts and proactively alert impacted teams based on its knowledge of the organisational structure of the host business.
Legal
Generative AI’s ability to generate new text content has obvious applications for legal departments. Drafting contracts, policies, or letters are all clear and well-described use cases. Generative AI’s creation of documentation following a repetitive, standard format is a clear strength.
Contract analysis and management is a more interesting use case that many businesses are exploring. Many companies have hundreds, if not thousands, of contracts with suppliers, customers and other third parties. Managing and accessing this huge volume of contracts is hugely challenging for human teams. Answering simple questions such as how many customers are on a given version of a standard contract becomes almost impossible with the resources available.
Using a Generative AI solution with access to the contract dataset, legal teams could quickly search and analyse the entire contract base at once through a natural language interface. This hugely reduces the time taken to answer queries and frees up human teams to focus on more strategic or advisory tasks.
Asset Management
Many enterprises operate complex networks of different types of physical assets. Effective management of these assets relies on having a clear view of their design, installation, maintenance requirements and historical activity.
In fault scenarios, in particular, this information needs to be rapidly available. But, in many companies, this data is scattered across different systems, often in unstructured and inconsistent formats.
Generative AI could be used to build an ‘Asset Historian’ that plugs into asset data stores to build a 360 view of an asset and answer detailed user queries or generate asset reports.
This hugely increases the speed at which asset information can be assembled, in turn shortening manual effort and response times for Asset Management teams.
Operational Control Room
Many businesses have invested heavily in alarms and telemetry to monitor their asset bases or systems. However, the volume and velocity of these alerts are so high that control room teams are often overwhelmed. Triage and prioritisation either cannot happen or take so long that valuable response time is wasted. The result is critical alerts should be addressed or handled properly.
Generative AI can be used in an ‘alert triage’ role.
You could create an AI-powered alarm triage manager by showing an LLM alert handling protocol and giving it the correct prompting. The AI would interpret and filter incoming alerts, proactively making simple fixes and passing higher-priority cases on to control operatives. This frees up human operatives from having to try and consume an overwhelming volume of incoming alerts, allowing them to focus on triaged, high-priority alarms.
Here, the near-limitless attention span and memory of an AI solution have been harnessed to support the ease and efficiency of human teams.
Finance
We’ve already discussed Gen AI’s strengths in helping users navigate large volumes of complex documentation. These strengths have clear applications within the world of finance. For example, a finance assistant could help non-accountants understand financial rules and policies to prevent errors from happening. Similarly, Generative AI could also scan internal finance policies and guidelines to check for discrepancies or gaps against external accounting policies or regulations.
Many finance teams also face the crucial but time-consuming task of providing regular financial reporting and commentary, such as monthly performance reviews or other regular financial updates. Generative AI can be leveraged to compile repeatable reporting and add descriptive commentary for users. Employed correctly, a Gen AI solution could also answer follow-up questions on reporting from Enterprise users. This frees expert finance teams to focus on strategic advice and deep insights rather than repetitive drafting.
Generative AI for Data Science
One of the clear strengths of Gen AI models/LLMs such as Chat GPT is their ability to write code in response to user requests.
Many software engineering and data teams are already harnessing this code generating ability – many liken it to having a team of junior engineers to help them.
Gen AI can rapidly write or review code, implement code changes across different codebases or even convert code from one programming language to another. You can also use LLMs to shorten the time-consuming task of creating and reviewing the technical documentation required to accompany software development.
These are classic examples of ‘AI augmentation’, where Gen AI is used under the supervision of human teams to speed up efficiency and implement repetitive workflows.
These are just some of the use cases of Generative AI there are many many more. The difficulty can be trying to find the early use cases which you can use to prove value quickly. If you need help and guidance on how to find the right use cases or are looking for an AI solution to an known problem then get in touch, and we’ll find the Generative AI solution that’s a perfect fit for you.
