Generative AI is advancing at an incredible speed, and businesses are trying to keep up. Many companies are eager to use it, but they struggle with understanding how to apply it effectively while also dealing with risks and biases. The adoption of generative AI has skyrocketed. Back in November 2022, very few businesses were using it. At that time, most available tools were just basic AI image or text generators. But by May 2023, 65% of companies had started using generative AI. By September, that number increased to 71%, with another 22% planning to adopt it within a year.
Companies are using generative AI in many ways. Some of the most common applications include automating IT processes, enhancing security, managing supply chains, and improving customer service. Cloud-based AI tools like ChatGPT have made it easier for companies to integrate AI into their workflows. In fact, AI is now built into platforms like Microsoft Office 365, Google Docs, and Salesforce.
Despite the enthusiasm, implementing generative AI at a deeper level is challenging. Companies that want to build their own AI systems face difficulties with fine-tuning models, managing large data sets, and integrating AI into their business processes. AI technology is changing rapidly for this reason businesses are struggling to keep it up to date because they need stability but in this rapid technological era AI is changing day to day.
Anand Rao, an AI professor at Carnegie Mellon University, explains that organizations are facing difficulties in use of generative AI because there are constantly new tools, models, and research being released. Due to this factor anfd high cost rate they are unable to use it, only 10% of companies successfully launched AI models in 2023.
However,a too long period of waiting is not good for the business. Companies need to set AI for the future. Building strong data and API infrastructure, setting security measures, and creating ethical guidelines for AI use can help businesses stay ahead.
Importance of Data and Infrastructure
Data carries the core part of AI but most of the companies are struggling with managing the data in a proper manner. According to an IBM survey, data complexity management is the second most problem in adoption of AI, right after a lack of expertise. Another study by cnvrg.io found that infrastructure is the main obstacle preventing companies from using large language models in production.
Many businesses struggle to manage data in an effective manner, which makes it harder for them to meet customer demands. One major problem is that companies experiment with AI and see what works without any proper strategy. But generative AI services like OpenAI’s APIs allow businesses to adopt AI without needing to train their own models.
Some companies use AI as a service, meaning they don’t need to build models from scratch. However, AI models will be only productive when they will be integrated with the company data.For example, a retail business might have detailed customer data. This data is analyzed using AI to create personalized recommendations. Almost every industry is working to integrate AI in similar ways.
Nayur Khan from McKinsey & Company says that AI is forcing businesses to accelerate their digital transformation. Companies are now focused on fixing issues they previously ignored, such as data management and AI governance. To succeed, businesses must not only invest in AI but also build the right data structures and pipelines to support it.
For companies wanting to create custom AI models, data architecture is crucial. They must be sure about the model they want to use,how to process the data,and how to integrate in their existing system. like adding an external data source, requires careful planning and technical adjustments.
Managing AI Models and Tools
Selection is another challenge. Over the past year, many new models have entered the market. OpenAI’s ChatGPT was the most popular early on, but then Meta introduced Llama 2, followed by Anthropic’s Claude 2 and Google’s Gemini 1.5, which can process much larger amounts of text. Additionally, there are open-source models tailored for specific industries like finance, healthcare, and science.
In this rapid change era business needs flexibility. A “model garden” approach allows companies to switch between different AI models according to their needs. This helps them to free from the single use model and also they can manage changes with the evolving change.
Beside selection companies also need tools to monitor AI usage. Which helps to track how AI interacts with data, what prompts it processes, and how long it takes to generate responses. Businesses can also introduce metering controls and methods to manage AI costs by limiting token usage.
Subha Tatavarti, CTO at Wipro Technologies, emphasizes the importance of these controls. Wipro, a company with 245,000 employees, must use AI because its clients expect it. She explains that businesses need a way to control AI spending while also ensuring security and compliance.
Access control is another major concern. AI should not expose sensitive company data. For example, an HR AI system should allow employees to ask about their own salaries but should restrict access to other employees’ salaries unless the user has special permissions.
Raj Gupta from Xebia IT consultancy suggests that businesses go for a single AI gateway.which can handle everything across all AI applications, preventing the need to rebuild the system for a specific use case. Tools like MLflow AI Gateway and Arthur Shield can help manage AI security and reduce risks.
Ethical AI and Compliance
Companies must also consider the ethical implications of AI. According to a cnvrg.io survey, compliance and privacy are top concerns, even more than cost or technical challenges. Many businesses worry about AI transparency, data security, and bias. An IBM study found that 85% of consumers prefer companies having ethical AI policies, but less than 50% are working on it to reduce the bias.
Ethical AI isn’t just about technology—it involves legal, compliance, and corporate values. CIOs and AI leaders must ensure their companies practise the strict rule and regulation,This includes setting up policies to prevent bias, ensuring transparency, and tracking data sources to maintain accuracy.
AI completely varies from the traditional old technology, because old technology always functions in the same way where AI does not and also AI is not deterministics.Matt Barrington from EY explains that businesses must rethink their approach to technology as AI becomes more integrated into their operations.
Beside new challenges, it also creates opportunities. No doubt those Companies are investing in AI infrastructure, security, and ethics will be better positioned for the future. The shift toward AI-driven business operations is happening rapidly, and those who adapt will have a great advantage.
