A deep dive into the benefits and challenges of using ChatGPT, Copilot, and Copilot Studio for your business
Introduction
AI assistance tools are becoming more popular and accessible for businesses of all sizes and domains. These tools use natural language processing and machine learning to generate text, code, or other outputs based on the user’s input and context. Some of the most prominent examples of these tools are ChatGPT, a conversational AI platform that can create engaging and natural chatbots, and Microsoft’s Copilot, a code completion tool that can suggest and write code for various programming languages and frameworks.
These tools are undoubtedly revolutionary, as they can automate and enhance many tasks and processes that would otherwise require human effort and expertise. They can also improve the quality, consistency, and efficiency of the outputs, as well as reduce errors and bugs. However, despite these advantages, many businesses face a common challenge when it comes to adopting and using these tools: how to map out their business value and justify their investments.
In this blog post/document, we will explore this challenge in more detail, and discuss some possible solutions and best practices. We will also compare the off-the-shelf tools like ChatGPT and Copilot with the low code development tools like Copilot Studio, and explain how the latter have a greater opportunity to show value, as they can be integrated with other tools and processes that drive revenues directly.
How to Measure the Time Savings of AI Assistance Tools
One of the most obvious and tangible benefits of using AI assistance tools is the time savings they can provide. By automating and enhancing tasks and processes that would otherwise require human effort and expertise, these tools can free up time and resources for other activities that add more value to the business. For example, ChatGPT can create chatbots that can handle customer queries, feedback, and support, while Copilot can write code that can perform various functions and features, without requiring the user to type or search for the syntax or logic.
However, measuring the time savings of these tools is not as straightforward as it may seem. There are several factors and variables that need to be considered, such as:
· The baseline or benchmark of the task or process before using the tool. This can be based on the average time, effort, and cost required to complete the task or process manually or with other tools.
· The learning curve and adoption rate of the tool. This can depend on the user’s familiarity, skill level, and preference for the tool, as well as the availability and quality of the training and support provided by the tool provider.
· The accuracy and reliability of the tool. This can vary depending on the quality and quantity of the data and context provided by the user, as well as the complexity and specificity of the task or process. The tool may also require human supervision, validation, and correction, which can add to the time and effort required.
· The scalability and flexibility of the tool. This can depend on the capacity and capability of the tool to handle different types and volumes of inputs and outputs, as well as the adaptability and customizability of the tool to suit different scenarios and requirements.
Therefore, to measure the time savings of AI assistance tools, businesses need to conduct a thorough and objective analysis of these factors and variables, and compare the results with the baseline or benchmark. They also need to monitor and evaluate the performance and impact of the tool over time, and adjust the parameters and expectations accordingly.
How to Align the Investments in AI Assistance Tools with the Companys P&L
Another challenge that businesses face when it comes to adopting and using AI assistance tools is how to align the investments in these tools with the company’s profit and loss (P&L) statement. The P&L statement is a financial report that summarizes the revenues, costs, and expenses of a business over a period of time, and shows the net income or loss of the business. It is a key indicator of the financial health and performance of the business, and influences the decisions and actions of the stakeholders, such as the owners, investors, managers, and employees.
However, the investments in AI assistance tools are not always easy to quantify and justify in the P&L statement. There are several reasons for this, such as:
· The upfront and ongoing costs of the tool. These can include the licensing or subscription fees, the hardware and software requirements, the maintenance and upgrade costs, and the security and compliance costs. These costs can vary depending on the type, size, and duration of the project or process that uses the tool, as well as the features and functionalities of the tool.
· The indirect and intangible benefits of the tool. These can include the improved customer satisfaction, loyalty, and retention, the enhanced brand reputation and awareness, the increased innovation and creativity, and the reduced risk and uncertainty. These benefits can be difficult to measure and attribute to the tool, as they may depend on other factors and variables, such as the market conditions, the competitive landscape, and the customer behavior.
· The long-term and strategic value of the tool. These can include the competitive advantage, the market differentiation, the growth potential, and the future readiness of the business. These values can be hard to capture and communicate in the P&L statement, as they may require a longer time horizon and a broader perspective to realize and appreciate.
Therefore, to align the investments in AI assistance tools with the company’s P&L, businesses need to adopt a holistic and balanced approach that considers both the costs and the benefits, both the short-term and the long-term, and both the quantitative and the qualitative aspects of the tool. They also need to communicate and demonstrate the value proposition and the return on investment (ROI) of the tool to the stakeholders, and align the goals and expectations of the tool with the vision and mission of the business.
How to Correlate the Use of AI Assistance Tools with Business Growth
A third challenge that businesses face when it comes to adopting and using AI assistance tools is how to correlate the use of these tools with business growth. Business growth is the increase in the size, scale, and scope of the business over time, and can be measured by various metrics, such as the revenue, the profit, the market share, the customer base, the product portfolio, and the employee count. Business growth is the ultimate goal and outcome of any business, and reflects the success and sustainability of the business.
However, the use of AI assistance tools is not always directly and positively correlated with business growth. There are several reasons for this, such as:
· The trade-off and opportunity cost of the tool. These can refer to the alternative uses and benefits of the resources and capabilities that are allocated and dedicated to the tool, such as the time, money, talent, and technology. These resources and capabilities could have been used for other purposes and projects that could have generated more or different value and growth for the business.
· The disruption and transformation of the tool. These can refer to the changes and challenges that the tool can bring to the existing processes and practices of the business, such as the workflows, the roles, the responsibilities, and the culture. These changes and challenges can create friction and resistance among the stakeholders, and affect the performance and productivity of the business.
· The uncertainty and complexity of the tool. These can refer to the unpredictability and variability of the outcomes and impacts of the tool, as well as the interdependence and interaction of the tool with other factors and variables, such as the data, the context, the user, and the environment. These factors and variables can influence and modify the behavior and results of the tool, and create unexpected and unintended consequences for the business.
Therefore, to correlate the use of AI assistance tools with business growth, businesses need to adopt a dynamic and adaptive approach that monitors and evaluates the outcomes and impacts of the tool, and adjusts and optimizes the use and implementation of the tool. They also need to integrate and align the tool with the overall strategy and objectives of the business, and leverage and enhance the competitive edge and value proposition of the tool.
How to Compare the Off-the-Shelf Tools with the Low Code Development Tools
A final aspect that businesses need to consider when it comes to adopting and using AI assistance tools is how to compare the off-the-shelf tools with the low code development tools. Off-the-shelf tools are ready-made and standardized tools that can be used for various tasks and processes, without requiring much customization or configuration. Low code development tools are tools that allow users to create and customize their own applications and solutions, without requiring much coding or programming. Some examples of off-the-shelf tools are ChatGPT and Copilot, while some examples of low code development tools are Copilot Studio, a platform that enables users to build and deploy conversational AI applications, and Microsoft Power Platform, a suite of tools that enables users to build and automate business applications and workflows.
These two types of tools have different advantages and disadvantages, depending on the needs and preferences of the business. Some of the factors and criteria that can be used to compare them are:
· The ease and speed of use. Off-the-shelf tools can be easier and faster to use, as they do not require much customization or configuration, and can be accessed and applied immediately. Low code development tools can be more complex and time-consuming to use, as they require some degree of customization and configuration, and can take longer to build and deploy.
· The flexibility and functionality of the tool. Off-the-shelf tools can be less flexible and functional, as they have limited features and options, and may not suit the specific needs and requirements of the business. Low code development tools can be more flexible and functional, as they have more features and options, and can be tailored to the specific needs and requirements of the business.
· The integration and compatibility of the tool. Off-the-shelf tools can be more difficult and costly to integrate and compatible with other tools and systems, as they may have different standards and protocols, and may require additional interfaces and adapters. Low code development tools can be easier and cheaper to integrate and compatible with other tools and systems, as they can use common standards and protocols, and can be built on top of existing platforms and infrastructures.
· The value and growth potential of the tool. Off-the-shelf tools can have lower value and growth potential, as they can be more generic and commoditized, and may not provide a unique or distinctive advantage or benefit for the business. Low code development tools can have higher value and growth potential, as they can be more customized and differentiated, and can provide a unique or distinctive advantage or benefit for the business.
Therefore, to compare the off-the-shelf tools with the low code development tools, businesses need to adopt a comprehensive and comparative approach that considers the various factors and criteria, and weighs the pros and cons of each type of tool. They also need to assess and align the tool with the current and future needs and goals of the business, and choose the tool that best fits and supports the business.
Conclusion
AI assistance tools are revolutionary tools that can automate and enhance many tasks and processes that would otherwise require human effort and expertise. However, mapping out their business value is a problem that many businesses face, as they need to measure the time savings, align the investments with the P&L, correlate the use with the business growth, and compare the off-the-shelf tools with the low code development tools. In this blog post/document, we have discussed some possible solutions and best practices for each of these challenges, and provided some insights and recommendations for businesses that want to adopt and use these tools effectively and efficiently.