Generative AI has captured the attention of the entire business world. The term Large Language Model has become a part of everyday language, and companies know that now is the time to form an AI strategy.
But what are the challenges that lie ahead? This article will cover some of the AI and data challenges that are prevalent today, as well as tangible, strategic steps to take as you and your organization move forward in your AI journey.
Key Challenges for AI Adoption
1.Is My Company’s Data Ready?
The past few years have seen an incredible rise in the use of data lakes and analytics platforms, creating a centralized approach to capturing important company information and making it available to anyone that wants to analyze it. For companies that have invested in these analytics platforms, they are now in a position to immediately dive into AI. However, if your company has not made these investments, or if there has been a failed rollout, you may not be ready for a full-fledged immersion into AI.
Issues like missing data, low data integrity, a lack of transformations, and little to no defined canonical models need to be addressed first. AI assumes that good data is ready and available for use, so a solid analytics platform is crucial. Unfortunately, there is no cheating here. Architectural designs must consider the entire enterprise and all systems that generate critical data. Budget considerations, ongoing operational costs, and data management must also be considered to avoid inefficiencies and higher costs. Expertise is needed to ensure a successful rollout of a solution and to raise your level of data maturity. With a solid analytics platform in place, you are ready to take the next step into the world of artificial intelligence.
2.What Are My Competitors Up To?
So, you have good data and a platform that can readily enable AI solutions, but what’s the rush? Do you really need to move now? Well, the answer to this question really depends on knowing what your competitors are doing. In some industries, machine learning models are already extensively used. For example, in Banking & Finance, Generative AI solutions are employed in customer service, fraud detection, and regulatory compliance. If you haven’t begun executing an AI strategy in these industries, you are already behind!
Not every industry is as advanced as FinTech, but it is important to understand that these technologies can act as major disruptors. If you are not first to market with a critical service or feature that only AI can deliver, then your company will need to respond immediately or risk losing market share. In the past, companies would have months or years to react to an innovation brought to market by a competitor. Today, responses are measured in days and weeks, not in months or years. An awareness of the pace of change in business is very important. This means executing AI strategies promptly.
3.What Are the Use Cases?
After ensuring good data and understanding industry impacts, the next step is identifying high-value AI use cases. Generative AI solutions are easier to implement because the use cases are simpler, and the business case is clearer. Examples include:
- Sales Assistants: Providing insights into prospects and customers.
- Customer Success Solutions: Offering a 360-degree profile of customers.
- Automation Opportunities: Particularly in manufacturing and supply chains.
- Computer Vision: Using video or images to monitor activities and ensure high quality and error prevention.
For examples of additional use cases, check out some of Headstorm’s AI & Data case studies and AI-focused videos.
4.What Technologies Should I Use?
With the rise of AI solutions, companies now face the challenge of identifying the best techniques and technologies for developing AI solutions. For example, there are different approaches to training language models for Generative AI solutions. Fine-tuning involves training models on a subset of data relevant to the specific business, while Retrieval-Augmented Generation (RAG) involves retrieving relevant documents and using them as context.
Large Language Models (LLMs) like OpenAI’s GPT, Meta’s Llama, Google’s Gemini, and others are popular, but research into Small Language Models for specific industries or use cases is ongoing. These models could be deployed on devices like phones and notebooks, eliminating the need for internet connectivity and opening new use cases.
Partner with Headstorm
Headstorm can guide you through these opportunities with our thought leadership, products, and services. Our AI STAR service works with senior leadership to develop AI strategic roadmaps. An AI STAR can help a company become proactive and use AI to provide first-to-market products and services.
Our Generative AI engine, AIpilot, acts as an accelerator, providing necessary technologies right out of the box and saving time and money. If your company needs to enhance its analytics platform capabilities, Headstorm can partner with you to architect and construct the right solution to fuel your AI initiatives and provide visibility for better decision-making.
Partner with Headstorm to architect and construct the right solution to not only fuel your AI initiatives, but also provide visibility to information that helps companies make better decisions.