Think Twice, Leap Once: AI’s 2 Key Questions

We love talking to folks who are excited to explore the potential of AI for their businesses. What might not be so apparent at first, though, is how deep the conversation has to go before we can generate a proposal. It’s all for a good reason – AI is like a puzzle with countless pieces. To create the perfect picture, we need to spend time discovering which pieces fit your needs best. 

But before we dive in, there are a couple of key points that can really shape the direction of your AI project. So, while AI can bring tons of benefits, it’s worth taking the time to ponder over these two questions, either alone or with your team, before you jump on the AI bandwagon for your business. 


Why Do You Want to Use AI?  

When engaging in discussions with potential or existing clients, we frequently ask numerous ‘why’ questions.  Specifically, why have you decided to use AI and why have you chosen to do so now? Asking these ‘why’ questions isn’t merely a conversation filler for us, but a necessary step in identifying the precise pain point or issue that a well-implemented AI solution can effectively resolve for your business.  When it comes to AI projects, we don’t expect you to come to the initial call(s) with a clearly defined articulation of the deliverable and its requirements. The possibilities and options with AI are so vast and complex that it’s unrealistic to have such expectations. By focusing on the ‘why’ we can tease out what you may need and how AI can solve it. Ultimately, we look to identify the heart of what you’re up against and determine if and how AI can help sort it out. To this end, it’s helpful if you can frame your thoughts around these prompts to articulate your challenges and needs:  

  • It’s frustrating when  _________. 
  • It would be great if we could _______ because _______. 


The reality is AI isn’t a simple purchase—it’s an investment. Whether you’re using a ready-made, open-source, or custom AI solution, there’s a significant cost involved. We’re not just talking about financial costs here. The time and effort required to properly and successfully implement AI are equally significant. You also need to consider committing your team to making sure it works for your needs. While they are not responsible for the technology behind the solution, your people need to be part of the process to ensure the solution delivers value. It’s a serious consideration, but with the right approach and support, it can lead to impressive results. 


What Data Will Train The AI? 

Let’s be clear – AI’s efficiency is heavily reliant on the quality and quantity of data that you feed it. This is an absolute truth that we cannot stress enough. Data can indeed be a game changer. The quality and volume of data you provide will directly influence the precision and efficacy of your AI solution. 

As the client, it falls to you to supply the necessary data. It’s a common occurrence for projects to hit a standstill due to a scarcity of data or the lack of quality data. Before we even begin sketching out a proposal, or during the early stages of our conversations, we’ll need to discuss your data situation. Most importantly, we need to understand what relevant data your business already holds that could be utilized for the project. By working together, we can identify the valuable data within your existing operation that could significantly enhance the success of your AI implementation. Being able to identify what types of relevant data you have is essential to estimating the costs of the project. Below are some tips to help you identify relevant data.  

  1. Assess your targets: Reflect on the main goals of implementing AI in your business. More often than not, the type of data that will be most relevant to your AI efforts closely mirrors these objectives. For example, if you aim to improve customer service, then data related to customer interactions and feedback can be highly influential.
  2. Look into your current processes: Start by examining the way your business functions. Do you collect customer information, sales data, feedback forms, or other forms of data during your typical business flow? If it’s in a tangible form that can be processed, it’s data.
  3. Check your archives: Often, there’s a wealth of information tucked away in your company’s historical records. Archived files and old databases could hold potentially valuable data.
  4. Consider external sources: Data doesn’t just have to come from within your company. Look into external sources such as social media analytics, industry reports, and market research data that could complement your internal data pool.
  5. Consult closely with your teams: Your employees, especially those directly affected by or using the AI, can provide insight into useful data points you may have overlooked.

Remember, the goal is not just having loads of data, but having the right data that can effectively train your AI to address your specific pain points and drive your business forward. 


Bottom Line
Before you speak to AI providers—whether Quantilus or otherwise—we highly recommend you and your team take these few reflective steps to prepare for the conversation. Understanding where your project could falter initially can save you significant time, money, and resources in the long run. When implementing AI, unclear goals or improper data can easily send your project down the wrong path. Given the complexity of AI, setting clear objectives and confirming the relevance, sufficiency, and quality of your data are not just preliminary precautions but essential steps. Ensuring these bases are well covered will give your project a substantial advantage even before receiving the estimate and proposal. This proactive approach will prevent potential pitfalls and also guide your business toward a successful AI journey.



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Wednesday, June 19, 2024

12:00 PM ET •  9:00 AM PT