Generative AI has changed how we interact with technology and has encouraged business executives to adopt it in order to improve customer and employee experiences. The capabilities of generative AI tools like ChatGPT from Open AI and Bard from Google look very impressive, but many businesses are still uncertain about ChatGPT implementation into their operations. Today, we will focus on the shift from fascination to implementation.
So, how can organisations successfully implement generative language models like ChatGPT? While it is no easy task, our general recommendation for the company’s C-suite is that their focus should not be on fully immersing in technology. Instead, the focus should be on how AI will change the way their organisations work and what strategic choices will be the most successful in helping manage the change and maximise positive outcomes.
With this in mind, we have prepared an easy-to-digest guide to help you develop an effective strategic approach to ChatGPT implementation in your organisation. It is our goal to empower and assist you in making informed decisions about ChatGPT and unlock its full potential for your business benefit.
Understanding the current trends and the transformative potential of ChatGPT will help you navigate the implementation process more effectively. Let’s take a moment to delve deeper into what has been going on.
Businesses worldwide are at the start of an innovative era driven by generative language models, which are radically transforming the way information is accessed, content is created, customers are served, and operations are managed. It’s only a matter of time before the adoption of generative AI becomes imperative for survival in the competitive landscape. Companies that are early adopters will have a higher chance of going through the transformation more smoothly and remaining ahead of the curve. With that said, let’s now briefly examine the firm evidence of the positive impact and optimistic estimations of ChatGPT.
In March 2023, Accenture, the leading global consulting company, published their research report – “A new era of generative AI for everyone,” – where they analysed the transformative potential of language models along with their recommendations to businesses. Their findings are quite impressive:
- LLMs can affect up to 40% of working hours across industries, with banking and insurance being the top two industries that benefit the most.
- In their analysis across 22 job categories, they found that at the low end, LLMs will take up only 9% of a day’s work, while at the high end, 63% of a day’s work will benefit. Administrative support and financial operations are among the top categories.
- 98% of global executives agree that generative language models will play an integral role in their business strategies in the next 3 to 5 years.
“Companies must reinvent work to find a path to generative AI value. Business leaders must lead the change, starting now, in job redesign, task redesign and reskilling people. Ultimately, every role in an enterprise has the potential to be reinvented, once today’s jobs are decomposed into tasks that can be automated or assisted and reimagined for a new future of human + machine work.”
Accenture
Global business leaders tend to agree and see the ChatGPT integration as a way to increase the personal productivity of employees by reducing the amount of routine work and optimising the internal processes. Almost six out of ten organisations plan to use ChatGPT for learning purposes in 2023, and over half will conduct pilot projects.
Implementing ChatGPT can be an exciting opportunity to drive innovation and efficiency, improve decision-making in your organisation, and unlock new possibilities across departments. However, keep in mind that this transition requires a holistic approach, involving stakeholders from different domains, and aligning them with the organisation’s strategic goals. Let’s take a step-by-step look at the process.
Before you begin integrating ChatGPT into your organisation, you need to define your use case. What problem are you trying to solve with LLM? What business processes will LLM support? By answering these questions, you can ensure that your LLM implementation is focused and aligned with your organisation’s goals.
There exists a dual approach to innovation.
The first focuses on low-hanging fruit opportunities that leverage consumable models and applications to deliver quick returns. The second one focuses on business reinvention using models that are tailored with the organisation’s data. Business-driven thinking is essential for defining and delivering on business cases. A few factors to consider:
- Analysing the existing processes. Gain a deep understanding of your organisation’s existing systems, processes, and workflows and identify the areas where the generative language model can add value. Repetitive, time-consuming tasks, requiring a lot of manual effort such as data entry, report generation, or content creation. One simple method of estimating how generative AI might affect existing roles and add value is to break them down into underlying sets of tasks. Then, evaluate how each task can be handled by generative AI – fully automated, augmented, or completely not affected.
- Cross-department collaboration. By involving representatives from various departments, organisations can gather diverse perspectives and insights to uncover opportunities where generative AI can deliver significant value. With this collaborative approach, the identified use cases align with multiple departments’ strategic goals and needs.
- Evaluation of potential outcomes. When you’ve identified use cases, you can evaluate them for feasibility, potential impact, and fit with your organisation’s goals and strategies.
- Building an implementation roadmap. After the evaluation, you can prioritise and make an implementation roadmap for each use case, including timelines and milestones.
Generally, defining use cases for ChatGPT largely depends on the industry. Each industry has unique characteristics, challenges, and opportunities that influence the potential applications of generative AI:
Below, we are sharing the universal ways of using ChatGPT for business as starting point of thinking:
- Searching across documents and answering questions about company policies, culture, procedures, and benefits
- Providing guidance on completing various forms and requests
- Drafting, editing, and proofreading emails to ensure they are polished and professional
| Need more inspiration? Read about 7 real-life use cases of Generative AI for Business we covered in our recent post.
PRO TIP: Get expert help understanding Generative AI and where you can employ this technology in your automation journey with ChatGPT Consulting services by Tovie AI. Our AI Readiness team evaluates the AI adoption capabilities of your organization. We assess factors such as infrastructure, data availability, and technical capacity to provide insights and recommendations for a successful AI journey.
Using your own data, your organisation can custom-tailor a large language model like ChatGPT to create a personalised AI chatbot that can handle a wide range of tasks. Let’s look at what customisation involves.
Depending on the type of deployed LLM, there are two methods of adapting it to perform tasks. Each one differs in its focus and application:
- Fine-tuning. By fine-tuning the model, we make it generate outputs that better align with the target situation, context, or domain by exposing it to more specific data. The technique involves training the model with a large amount of data and adjusting its parameters and weights, enabling it to generate outputs that better fit the targeted task or domain. Fine-tuning is a lengthy and expensive process.
- In-context learning allows us to quickly build models for a new use case without worrying about fine-tuning and storing new parameters for each task. The model learns tasks only on a few context examples. During training, the model is exposed to conversations or text samples that mirror the desired use case or prompt format. By incorporating these samples and training the model further, it becomes more adept at generating responses that align with the provided context or prompt.
In-context learning has gained popularity over fine-tuning due to its simplicity. However, fine-tuning can still be a powerful tool for improving the performance of generative LLMs when used correctly.
Here is an example of how a customised language model performs a task of creating a job position description for the HR department:
A sample conversation between an integrated LLM and a company manager about a future job position description
AI is only as good as the data it is trained on. And it still holds true even with the new generation of AI 2.0 model. Here are some guidelines for using in-context learning for training generative LLM:
- Provide clear and concise input-output examples to demonstrate the task
- Use natural language to describe the task and provide context
- Experiment with different prompt formats to find the most effective one
- Keep adding examples and tweaking the description to refine the prompt
PRO TIP: Models cannot be expected to function indefinitely in the same way as they did in sandboxes during the training because the data keeps changing. Put processes in place to index new data in real-time to ensure its reliable performance.
Step 3: Integration and deployment
This step is key as it involves Integrating the fine-tuned model into your organisation’s workflows. There are several important factors to consider:
- Integrating with your existing systems or processes. Our AI Readiness consultants will assist you to determine the specific integration requirements for the generative language model and assess whether it needs to be integrated within an existing software application, website, or communication channel. This may involve collaborating with your IT team on developing Application Programming Interfaces (APIs) or interfaces that facilitate seamless interaction between the generative language model and existing systems. These APIs should enable data input, output, and communication with the model.
- Ownership and leadership. In order to get ChatGPT up and running, it’s important to assign a department like HR or P&O to do it. The department should be proficient in the technology and help the organisation integrate it, such as setting the vision, aligning key stakeholders, and making sure that the implementation aligns with the organisation’s strategic goals.
- Сross-functional collaboration. It’s a smart idea to have a cross-functional team with HR, IT, legal, and other relevant departments to ensure various perspectives are taken into account, and organisational needs are met during ChatGPT. It can help address potential challenges, optimise resources, and foster new knowledge transfer.
- Compliance. Depending on your industry and the type of data you handle, you may be subject to regulations such as GDPR. If you implement generative LLM in a controlled environment, or on-premise, there is no reason for concern as with this implementation type you have full control over the data and the model output. You may still want to consider engaging a cybersecurity expert to establish a regular monitoring and audit schedule to identify any potential security breaches or compliance issues.
There is a greater chance of the deployment running smoothly if these primary aspects are considered from the outset.
PRO TIP: Scale incrementally. Begin with small-scale pilots or proof-of-concept projects to test potential use cases’ viability. This approach ensures the technology is integrated effectively and sustainably and allows you to evaluate the feasibility, estimate the impact, and build confidence across the organisation before scaling up the implementation.
| SaaS or On-Premise? Take a look at our overview of both types of generative AI deployment [a cheat sheet for easy decision-making inside].
Implementing an LLM in an organisation will likely involve changes to existing processes and workflows. It is important to have a plan in place for managing these changes and training employees on how to use ChatGPT effectively in their daily tasks, including:
- Defining learning objectives ensure that your training is focused and effective. What do you want employees to be able to do with the LLM? What specific skills do they need to develop?
- Providing comprehensive training materials that cover all aspects of the LLM, from its basic functions to more advanced features. This can be step-by-step instructions, screenshots, and other visual aids to help employees understand how to use the technology
- Offering hands-on practice with the LLM to help employees build their skills and confidence. Encourage employees to experiment with the LLM and explore its capabilities.
- Provide ongoing support to employees as they begin to use the LLM in their daily work. Resources such as FAQs, forums, and chatbots, to help employees troubleshoot
It is also important to educate leadership teams about ChatGPT to enable swift, informed decision-making. Offering workshops and seminars can provide an in-depth understanding of the technology, its potential benefits, and its limitations. This empowers leaders to make quick, informed decisions that will shape the organisation’s adoption and use of the technology.
With the right training and support, your employees can easily adapt to the new technology and fully realise the benefits.
PRO TIP: Consider crafting a comprehensive communication strategy that addresses employees’ possible concerns about ChatGPT, such as its impact on their job security, workload, or job responsibilities. That will help build a strong culture of innovation in an organisation and ensure that employees are more receptive to the implementation.
Step 5 – Monitoring and evaluation
Implementing LLMs is not a “set it and forget it” process but rather an ongoing one. Once your tech team has deployed a generative language model, it is essential to regularly revalidate its effectiveness and impact by:
- Collecting feedback from employees and customers
- Evaluating the output quality
- Setting up metrics and monitoring key performance indicators (KPIs)
When it comes to setting KPIs for your LLM, we can compare it to directing an orchestra. Just as a conductor sets each musician’s tempo, dynamics, and overall performance standard, establishing KPIs for an LLM involves defining the desired outcomes, accuracy, response time, and specific metrics that determine its performance. This way, LLM will operate in harmony with the organisation and deliver desired results.
These activities will keep you aware of any issues or opportunities for improvement so that you can ensure that the use of generative AI continues to support the organisation’s goals and priorities. Make any necessary adjustments to achieve this.
PRO TIP: If you have little understanding of how to set specific KPIs for your new LLM, a good starting point could be to make an analogy with the human role you “hired” the LLM for. You might want to translate your desired outcomes and metrics for a human employee in a similar role into measurable goals and metrics for the LLM.
We have covered all the key steps for implementing ChatGPT. By now, you should have a good understanding of how to effectively integrate a generative language model to drive growth and innovation for your organisation.
If you’re looking at ChatGPT implementation into your business but uncertain about where to start, Tovie AI is here to help. Our AI Readiness experts will work with you to understand your strategic motivations for investing in AI, define use cases best suited for AI, and cases where simple automation will have an equal or adequate impact.
Contact us today to learn more about how Tovie AI can help you unlock the power of ChatGPT and other AI language models within your business:
Is your organisation ready for Generative AI?