While much of the discussion surrounding artificial intelligence (AI) centers around how to build powerful new AI-based products, many companies struggle to integrate AI into their existing products, services and systems. To remain competitive, it is imperative for companies to incorporate AI into their operations. Those who do not keep up with the latest technological advances risk having their businesses disrupted by more tech-savvy startups.
The key for companies confused about AI integration and scrambling to develop a workable strategy is to utilize software solutions powered by large language models (LLMs). While pre-LLM technology is “brittle” and typically comes with cumbersome and error-prone change processes, LLMs are more powerful and flexible. For this reason, LLM-based products dramatically increase efficiency and reduce costs while empowering employees and organizations to get more done faster and easier than ever.
Why LLMs are Crucial to Future Business Success
One of the most significant advantages of LLMs is that they are trained on massive amounts of data and based on deep learning architectures. This foundation allows them to simplify and speed up content generation, extraction and summarization, boosting a company’s efficiency and productivity. At this point in the evolution of AI, LLMs can also assist or even replace the work of human agents in many knowledge worker scenarios. This is important because LLMs can do the work faster and without the errors that frequently occur because of fatigue, distraction and other factors that arise when humans are involved.
This ability to produce a higher quality and consistency in knowledge work has many companies rushing to embrace LLMs. For example, Klarna, a fintech firm, just announced that it is severing its partnerships with software-as-a-service (SaaS) providers Salesforce and Workday and replacing them with internally built AI tools. In discussing the move, a company spokesman told Inc., “We have a number of large internal initiatives that combine AI, standardization, and simplification to enable us to shut down several software-as-a-service providers.” Like many others, the company leans on AI to cut costs and improve workplace efficiency. Still, this doesn’t mean adopting AI solutions is smooth sailing and challenge-free for most companies.
Challenges Companies Face When Integrating AI Into Their Operations
Integrating AI into a company’s operations presents several significant challenges, including dealing with insufficient data, data silos, poor quality data, talent gaps, costs, employee resistance, integration with legacy systems, regulation and compliance issues, scalability and maintenance. Addressing these challenges requires a strategic approach that includes strong human leadership, a clear vision for AI’s role in a company, cross-functional collaboration, comprehensive training and continuous learning.
For organizations with limited AI expertise, combining tools from reputable vendors with human oversight is the best approach for integrating LLM-based solutions without causing significant disruptions to existing operations. Only companies that interact with foundational LLMs directly need to hire AI engineers. Because LLMs have ushered in a new era in AI, most organizations can excel by relying on existing AI tools and hiring and training employees who are well-versed in evaluating software. Early AI models didn’t have much intelligence and required significant training to support limited use cases. Today’s LLMs, however, have considerable flexibility and the power to be applied in various use cases.
In choosing the right AI tools, companies need to stay focused on the end goal and look for tools that “supercharge” human performance by automating mundane and repetitive tasks rather than tools that eliminate human oversight. It is also essential for companies to find tools that offer a higher degree of accuracy, even at the expense of fewer features. Selecting highly accurate LLMs helps companies produce higher-quality, consistent outputs and provide better customer service. Here are some examples of how LLM-powered AI is producing powerful benefits for companies today:
- Zoom Video Communications. This company implemented an AI-driven virtual assistant, “Zoom Virtual Agent,” to help address customer queries automatically. Powered by natural language processing (NLP), the system resolved 80% of issues without human intervention, allowing the company to reduce operational costs and speed up response time.
- H&M. The fashion retailer turned to AI for inventory management and demand forecasting. The technology has resulted in fewer stockouts, improved customer satisfaction, reduced overproduction, and cost savings of up to 25%.
- Siemens employed AI to improve its supply chain logistics, using predictive analytics to forecast demand, optimize delivery routes, and reduce transportation costs. The company reported a 20% reduction in logistics costs and a 15% improvement in delivery times.
Finding the Best LLM-Powered Tools
In the pre-LLM world, companies had to hire a team of expensive consultants to customize software like SAP for their business. That dynamic has changed due to the creation of LLM-based business applications that are easier to implement and more responsive to end-user needs. Companies that take advantage of this to integrate AI more quickly into their operations will stay ahead of the competition and improve their customer service, which will increase customer retention.
The good news is that most organizations do not need to integrate with AI models directly. They can utilize software solutions through LLMs and sales and marketing platforms to power their AI strategy with minimal interruptions. It is no longer about building AI products from scratch. Instead, it’s vital to discover and leverage the best LLM-based AI tools available.
Finding optimal AI tools involves research, evaluation, and carefully ensuring the tools align with business objectives and offer a high return on investment (ROI). To ensure the most appropriate tools are utilized, it is essential for companies to define their needs, conduct extensive market research, and systematically compare the available options. This helps companies achieve higher quality and consistency in their outputs and supercharge workers to be more effective. In addition, these companies are proactively preparing for the future and positioning themselves at the forefront of their marketplace. That means instead of chasing the competition, the competition chases them.
