AI Takers vs. Shapers: Harnessing Generative AI for Competitive Edge

With AI assistants popping up in every tool and service, it's easy to be an AI taker. However, using generally available tools does not build a lasting competitive advantage. One must learn to shape AI to make a difference.

According to a recent survey, 80% of technology and data leaders believe in the transformational power of generative AI. The majority are still experimenting with the technology, but the number of production deployments is expected to grow dramatically in the second half of 2024.

Here is a framework to assist you on your generative AI journey.

First, you need to decide whether your organization is an AI taker or an AI shaper.

The AI taker approach is relatively straightforward. Generative AI is already embedded in every software product used in your environment. Your organization can improve productivity by deploying the latest versions of these products and educating workers on using the embedded AI assistants. Most vendors also provide tools for tuning AI assistants for the maximum effect.

One must realize, however, that being an AI taker does not build a lasting competitive advantage. Instead, it is a defensive move that brings your organization on par with the rest of the industry.

To use AI as a differentiator, one must be an AI shaper. That is, one must utilize large language models (LLM) to develop unique solutions enabled by proprietary knowledge, processes, data, and insights. 

Generative AI enables many impactful business cases, and most of them can be assigned to one of the following five categories:

  1. Knowledge management
  2. Question-answering
  3. Natural-language processing
  4. Advanced analytics and data visualization
  5. Intelligent automation 

Knowledge management 

Do you have a content-rich website, internal or external, where search, not navigation, is the primary mode of information retrieval?

If the answer is yes, you hit a jackpot. Generative AI’s main superpower is processing large volumes of information and synthesizing personalized answers to specific questions. The industry term is retrieval-augmented generation (RAG).


Do you have people calling other people and asking for information or assistance in problem-solving? AI can make this process much more efficient.

Question-answering is similar to knowledge management in delivering personalized answers to specific questions but often relies on unstructured and structured information stored in systems of record. Common use cases include customer service, field service, and supply chain management.

Natural-language Processing 

Do your workers extract structured information from text, images, audio, and video? AI can make this process much more efficient.

The traditional, pre-LLM approach to automating this work involves labeling large volumes of data and training many small task-specific AI models, a labor-intensive and time-consuming process. 

With some prompt engineering, LLMs can often do this work out of the box.

Advanced Analytics and Data Visualization 

Do you have a team of data scientists building data models and visualizing data?

A data analytics team is often a shared resource overwhelmed with assignments. With generative AI, you can automate some of their work and make their services more accessible.

With some data integration and prompt engineering, LLMs can use powerful data science tools to automate data wrangling, data modeling, and visualization.

Intelligent Automation

Have you outsourced work because it is too tedious or time-consuming? This work can now be performed in-house by an LLM.

A Python data library is not the only tool that a large language model can use. A large model can be tuned to employ any tool with an API to perform all sorts of tasks.

For instance, an LLM can be instructed to research publicly available information and query proprietary information in CRM, CMS, and marketing automation to qualify and enrich leads based on their browsing history. The model can also draft personalized prospecting messages and design optimal outreach sequences.

In summary, as we stand on the cusp of the generative AI revolution, the distinction between AI takers and AI shapers becomes increasingly significant. While AI takers embrace generally available tools to catch up with the status quo, AI shapers are the pioneers using AI to create unique services and advantages. Every journey starts with the first step. Start by asking questions outlined in this article and reach out if we can help.  

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