?? De-hyping AI: Realizing True Business Value

In recent weeks, I've engaged in several thought-provoking LinkedIn discussions about the low adoption rates of AI (?? A survey of businesses in Austria) , the common misconceptions surrounding it (?? ??????'?? ?????? ???? ???? - ?????? ???????? ?????? ?????????????????? ?????? ??????% ???? ????????????), and where our focus should truly be in this rapidly evolving field (?? Are we even talking about the same thing?). These conversations highlight a crucial point: we need to de-hype "AI" and focus on its practical applications.

??Clarifying Misconceptions

Let's first address a common misconception. What we often refer to as "AI" today is not true Artificial Intelligence in the most literal sense. Instead, it is a spectrum of technologies, including Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV). While these tools are powerful, they are not genuinely intelligent entities but rather sophisticated algorithms and applications. Like any other technology, they have both benefits and drawbacks. Understanding this distinction is crucial for managing expectations and achieving tangible business value.

??? Unlocking the Value of Your Data

Among the many potential applications for these technologies in business, one promising use case is leveraging the vast troves of data that organizations accumulate.

Companies gather enormous amounts of data in various forms—PDFs, emails, Notion documents, recorded conversations, meeting notes, and more. Often, this valuable information is scattered and underutilized. This is where a combination of Generative AI, Retrieval-Augmented Generation (RAG), and NLP technologies can create a significant impact.??

By implementing such systems, businesses can:

1. ?? Gain valuable insights from previously fragmented data.

2. ?? Improve employee productivity by reducing time spent searching for information.

3. ?? Identify patterns and trends that might otherwise go unnoticed.

It's important to note that this is just one of many possible applications. The key is to identify the use cases that align best with your organization's specific needs and goals.

?? Real-World Applications

Here are a couple of examples from recent conversations with colleagues and clients:

1. A large company is monitoring internal data to identify drops in employee productivity and correlating them with other ongoing factors. By analyzing these correlations, they can proactively intervene and resolve potential issues, keeping their teams moving forward.

2. A medium size company is using these technologies to categorize and extract insights from diverse data sources.

3. A recruiting agency, whose data was historically scattered across various systems, now has access to all its data in one place, regardless of its location, saving time and effort.

?? Starting Your Journey

You don't have to start big. Begin with a Minimum Viable Product (MVP) to understand the required investment in effort and costs. Low-code platforms like Make, n8n, Flowise and similar ,allow for prototyping at a fraction of the cost. Some of these such as n8n can be deployed on-premises or in a private cloud, addressing data privacy concerns.

?? The Critical Thinker's Approach

As I mentioned in a recent comment, it's crucial to remain a critical thinker when approaching these technologies. Carefully assess the ROI of implementation, harness its potential, and be mindful of its limitations.

?? Questions to Ponder

??What potential use cases do you see for these technologies in your organization?

?? What treasure trove of information is your company sitting on?

?? What’s holding you back from unlocking this untapped potential?

#BusinessValue, #AIApplications, #GenerativeAI, #Hype, #AIAdoption, #AIStrategy