Maximizing Returns: Navigating AI Productivity's ROI for Your Workforce
In this blog
Businesses were initially on the fence about AI adoption, leaving workforces to navigate the journey alone with tools like OpenAI's ChatGPT. Meanwhile, more future-focused organizations have taken the necessary steps to embrace AI tools, leading to efficiency benefits and a competitive edge in the marketplace.
If you are looking to invest in AI solutions to drive workforce productivity gains, don't just throw your resources away by acquiring the latest AI tool to make headlines. The key to harnessing the full potential of AI lies in a framework that systematically answers the question: Does this AI tool make sense for my workforce?
Evaluating your particular AI use case quickly is key. To help, I've broken out how to assess workforce productivity use cases through a simple three-step framework:
- AI proficiency: Are there AI platforms or solutions designed to handle your specific task or use case?
- Task appropriateness: Is your specific task appropriate for this AI? This is distinctly different than #1 and is situation-dependent.
- Productivity ROI: Does the time saved by introducing AI deliver the ROI to justify the investment? This includes any workforce training needed to use AI for the task in question.
Let's dig into each step in more detail.
AI proficiency refers to the ability of an AI tool to competently perform a specific task or a range of tasks. This is where we need to see past the hype. We are still at the beginning of AI maturity, with some AI tasks needing additional investment to become proficient. AI can risk becoming a magical cure-all answer if you buy into the hype.
Always start with selecting an AI solution that excels in the functions it's designed for. Consider key benchmark areas such as technical capabilities, reliability of it's outputs, and track record in similar tasks or industries. Perhaps there isn't a tool that exists. Maybe it's worth an investment in developing AI capabilities. The key question to answer: is your organization looking to invest additional funding to develop AI proficiency for a competitive edge? Great, we can help with that. Many organization, however, are not necessarily cutting edge are are looking to adopt readily accessible AI tools for general workforce productivity. If that's your workforce and you can find an off-the-self proficient AI productivity tool, let's move onto the next step, task appropriateness.
Not every workforce task is suitable for AI intervention. Just because you can, doesn't mean you should use AI in workflow. Businesses need to carefully evaluate whether their specific tasks can be effectively and appropriately handled by AI. This involves considering the complexity, uniqueness and specific requirements of each task.
Let's examine this idea more closely.
- Enhancing your subject matter expertise with AI: When evaluating AI adoption, it's essential to focus on the unique value of your organization's subject matter expertise to the market. An organization's vertical knowledge and experiences are what set it apart in the marketplace. AI, in this context, should be seen as an asset that complements and enhances your expertise, not a substitute. It should help streamline and organize tasks, allowing you to focus more on areas that require specialized insight and intuition. The goal is to integrate AI in a way that augments judgment and expertise, providing a more comprehensive and efficient service to your clients. Remember, in a market where knowledge is king, AI is a powerful ally that can help showcase and amplify, not replace.
- Certification and compliance considerations: In many professional realms, both individual workers and workforce tools must adhere to strict certification requirements. This isn't merely a preference but a legal necessity. For instance, an architect must use tools that comply with specific codes and regulations. Similarly, healthcare professionals are often restricted to the exclusive use of FDA-approved tools for diagnostics. As the world of AI evolves, particularly in highly regulated sectors, it's essential to ensure that any AI tool in use is not only effective but also compliant with industry standards and certifications.
- Handling sensitive data with AI: The management of sensitive data warrants careful consideration. If your work involves NDAs or data clearances, any AI tool employed must align with such confidentiality agreements. It's imperative to ensure that using an AI solution does not inadvertently lead to data breaches. Approved AI tools should have robust data control mechanisms, ensuring that sensitive information is accessed only by authorized individuals. This is especially crucial in fields like government and healthcare, where data is not just confidential but often classified. The principle is clear: Only use AI tools that are equipped to handle sensitive data within the bounds of legal and ethical guidelines.
Is your workforce use case appropriate for the task based on the above? Great, let's move on to the last evaluation step: productivity ROI — the bane of technologists everywhere.
The ultimate measure of AI success in a productivity workflow is the ROI. Answer this: Does the time you spent investing upfront result in time spent over the course of the tasks? This includes considering the time saved in task completion (without postfixes), output accuracy, the quality of work, and the cost of AI implementation and ongoing maintenance.
I've seen some pretty slick AI workflows that work really well in proof of concept but fail when it comes to repeatable workplace tasks. Businesses should conduct a thorough cost-benefit analysis, looking beyond immediate financial gains to long-term efficiency and scalability. This includes factoring in the time and resources spent on training and adapting the workforce to use the AI tool.
Here's a non-exhaustive list of things to consider when evaluating the ROI of AI productivity tools:
- Repeatability and task structure: Evaluate if the task is structurally repeatable. AI excels in automating repetitive tasks, especially those that combine routine processes with data analysis or creative elements. The more a task varies or increases in complexity, the less suitable it becomes for AI.
- Data accessibility: Consider the availability and quality of the data needed to make your workforce's workflows effective. AI relies on quality data to function effectively. For example, if you are looking to develop sales briefings and product data is not readily available in a format for AI consumption, how can you expect AI to provide value? If data availability requires a significant uplift, there may be little immediate ROI from adding AI into your workflow.
- Collaboration requirements: Assess the level of collaboration needed for the task. AI may be less effective in scenarios requiring extensive collaboration and shared vertical knowledge. Tasks that depend heavily on team-based insights might not benefit as much from AI.
- Experimentation and integration time: Consider the time and effort required to adapt to and integrate the AI tool into your workflow. While some AI tools like ChatGPT might require significant experimentation to yield useful outputs, others like Grammarly are designed for quick integration and immediate productivity gains.
- Cost of implementation: Evaluate the direct costs associated with implementing the AI solution, including purchase, subscription fees and any additional infrastructure or support systems needed.
- Workforce training and development: Account for the time and resources needed to train staff to use the AI tool effectively. This includes ongoing education as AI tools evolve and improve.
- Scalability and future-proofing: Assess how well the AI solution can scale with your business and adapt to future needs. Consider whether the AI tool can accommodate growing data volumes, changing business models and emerging market trends. With how fast AI is developing, if the ROI for workforce productivity tools is beyond one year, there's significant risk in investment. Many OTS AI productivity tools are designed to be brought on with minimal overhead upfront, making the ROI much simpler to justify.
- Impact on customer experience: Consider how AI implementation might affect customer interactions and service quality. Positive impacts on customer experience can translate to increased customer satisfaction and loyalty.
- Maintenance and upkeep costs: Factor in the ongoing costs associated with maintaining and updating the AI system. This includes the expenses for technical support, software updates, and potential infrastructure upgrades to keep AI tools functioning optimally.
- Long-term efficiency gains: Beyond immediate time and cost savings, evaluate the potential for long-term efficiency improvements, such as faster decision-making, reduced error rates and enhanced analytical capabilities. These gains are more difficult to measure, but it's important to consider that many small efficiency gains can compound to deliver long-term competitive advantages.
Incorporating AI into workforce productivity is a decision that requires carefully considering AI solution proficiency, task appropriateness and productivity ROI. By meticulously evaluating AI solutions against this framework, businesses can make informed decisions that not only enhance their operational efficiency but also drive sustainable growth.
As the AI landscape continues to evolve, staying informed and adaptable is key to leveraging AI as a powerful tool for your organization's success.