Generative Artificial Intelligence Integration: What It Is and Why It Matters
In less than a year, generative artificial intelligence (AI) has gone from the fantastical plots of science fiction to a transformative force in our daily lives. Today, generative AI is helping businesses, from creating unique content to providing industry insights–and companies are discovering that if they are not utilizing generative AI, they are quickly falling behind their competition.
However, integrating generative AI into a legacy system and maintaining it can be a complex challenge. Companies often encounter various technical, organizational, and strategic trials when attempting an AI implementation. However, there are skilled contractors, including nearshore developers, who have the technical expertise to overcome all AI integration hurdles.
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What Is Generative AI?
Historically, traditional AI systems were designed to perform specific tasks, like data classification or prediction. Generative artificial intelligence (or gen AI) is different. It takes massive amounts of data and restructures it into new content. The results of generative AI mimic human creativity.
Generative AI is increasingly becoming an essential tool for businesses to remain competitive. Generative AI can:
- Refine marketing copy, product designs, and customer interactions at scale.
- Simulate scenarios, such as market trends, customer behaviors, or financial projections, that can help leadership teams make informed decisions.
- Tailor communications based on individual customer preferences.
- Speed up the artistic design process by refining and altering prototypes.
Generative AI Examples
Businesses (and consumers) use generative AI for several purposes, including:
- Text generation. Natural language processing (NLP) models create text for customer support, content creation, and more.
- Images. Depending on the program, generative AI can create artistic illustrations in a variety of styles based on text prompts or existing photographs.
- Video creation. Some generative AI programs allow users to create and edit video content using synthetic or animated avatars.
- Music Composition. Generative AI can create musical arrangements in various styles and genres.
- Code writing. Generative AI assists developers by suggesting code snippets and completing tasks based on context.
AI Integration Challenges
When a business attempts to integrate and maintain a new AI system, it can encounter many technical challenges. However, some companies will also encounter strategic and organizational obstacles, as well.
Technical challenges
One of the biggest hurdles to incorporating modern technology, especially something as complex as generative AI, is integrating the programming into a business’ legacy software. Although cutting-edge when they were developed, many legacy systems now utilize outdated programming and rigid architectures. It can be both costly and time-consuming to upgrade or re-engineer these systems.
Another hurdle is how data is stored in a legacy system. Generative AI needs vast amounts of data to operate properly. Legacy systems may store data in inconsistent formats, have siloed databases, or include a series of incomplete records, which means the AI will not be as effective as possible or generate incorrect results.
Additionally, adding generative AI to a company’s existing system can expose it to cybersecurity risks. Most legacy systems were not designed with modern security threats in mind, and adding new programming may inadvertently expose vulnerabilities.
Organizational and Strategic Challenges
As much as a legacy system can hold an AI integration back, legacy employees can also pose a significant roadblock. A successful generative AI integration often demands a shift in the corporate culture. Not only can negative attitudes and skepticism about the value of AI impede implementation, but without clear goals, the generative AI project may not align with business needs.
Also, even if employees embrace the idea of generative AI, they may not have the skills to support it. Implementing and maintaining generative AI requires specialized expertise, and many organizations lack in-house talent with these skills.
Overcoming AI Integration Challenges
Often, looking outside of the organization is the best solution to the technical, strategic, and organizational challenges a company faces when attempting generative AI implementation and maintenance. A nearshore software developer (located in a nearby country, typically within the same time zone) is frequently the ideal solution.
Nearshore developers offer specialized expertise and often have a team experienced in integrating generative AI with legacy systems and maintaining them once the integration is complete.
Another reason to consider partnering with a nearshore developer is cost. Hiring in-house gen AI experts can be costly and time-consuming. Nearshore software developers offer access to highly skilled professionals at extremely competitive rates. This also reduces the burden of hiring and training new staff.
If your business is interested in incorporating the benefits of generative AI into your systems, Kibernum USA, one of Latin America’s largest nearshore IT providers, is ready to help. Reach out to Kibernum to discover how a partnership can modernize your business.