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Traditional AI is built on supervised learning that can help accurately notice patterns, while generative AI runs on unsupervised datasets and can create brand new outputs from scratch. How this impacts your business depends on four things:

1. What your business specialises in, and various objectives/KPIs that need to be met,
2. Any challenges and bottlenecks,
3. Any customer complaints (especially persisting ones),
4. Any budget and duration constraints (especially around the utilisation of cloud resources).

In this article, we explore how traditional AI and generative AI impact each of the areas above, from a business standpoint.

As a leading software development services company in Australia, EFutures partners with businesses the world over, guiding them with the strategies, workflows, and resources they need to adopt AI technologies.

By working with existing systems, constraints, and compliance requirements, our 100-member team delivers solutions that meet niche objectives.

Contact us today for a free and no-obligation business assessment, as well as strategic direction on how you can transform your business with the right combination of AI-powered innovation.

Traditional AI vs generative AI, for meeting business objectives and KPIs

For analytics and pattern recognition, choose traditional AI

Traditional AI depends on supervised learning, which involves manually labelling data to train algorithms on what something signifies. This is time consuming, no doubt. But having human resources label each item of data can increase accuracy of outputs. This makes traditional AI a great option for identifying patterns within large amounts of data, and making predictions on historical data.

This high level of accuracy also makes it reliable for round-the-clock monitoring, thereby being useful for detecting security incidents and triaging them based on severity.

For creative outputs and a faster time to scale, choose generative AI

For use cases that require composing something from scratch, generative AI is a worthwhile technology to consider. This also explains why mainstream generative AI tools such as ChatGPT, Claude, and Perplexity are popular. Generative AI functions through unsupervised learning, which groups data that it identifies as similar, together. It then self-learns from the clustering it has done, to deliver writing, code, images, and even music from scratch.

As a result, generative AI is a useful assistant for marketing and communication teams that require written copy, while engineering teams can rely on it for building new code. However, it is important to note that generative AI can make mistakes, so human moderation is necessary. For custom generative AI models, human intervention is also best applied at the testing stage, to determine whether data clustering done via unsupervised learning is accurate, before proceeding with development.

Traditional AI vs generative AI, for addressing challenges and bottlenecks

A combination of AI and genAI for powering intelligent recommendations, and human-like context

Hardly is one option better than the other, when it comes to meeting business challenges and bottlenecks through AI. With constraints tight and some crises urgent, discussions are better centred around which combination of technologies are optimal for problem solving, as opposed to being austere about using (or not using) certain technologies themselves.

In essence, a hybrid of sorts between traditional and generative AI for problem solving can be facilitated by meeting compounding (yet previously tackled) issues with traditional AI. For instance, a large yet untouched dataset may require granular analysis, which previously labelled datasets can assist with. However, for many unknown data points, generative AI can be put to task to determine possible classifications, and subsequently deliver analyses.

The same also goes for testing new software features. Previous use cases automated by traditional AI may be helpful here, especially where regression testing is concerned. But for completely new features, generative AI can evaluate all possible test cases to ensure no angle is missed during the quality assurance phase. For software development teams who are often functioning with large backlogs and bottlenecks, such a combination can significantly help reduce launch times for both new software, and periodic updates.

Traditional AI vs generative AI, for addressing customer complaints

Use traditional AI for customer scores and profiling

Customer databases are the heart and soul of any organisation, and as a result, frequent starting points for AI-powered analytics and automation. Whether its records in your CRM or a fully-fledged CDP (Customer Data Platform), utilising traditional AI to deliver accurate scores and profiles on customers can help sales and service teams alike.

Some ways that traditional AI helps for customer scoring and profiling includes (but isn’t limited to):

• Sentiment analysis, based on engagement patterns and feedback,
• Lead scoring that helps determine how likely an existing customer is bound to repurchase, or whether a prospect may finally convert,
• Cleansing, enrichment, and overall transformation of customer data according to Ideal Customer Profiles (ICPs), so sales, RevOps, and marketing teams have complete and up-to-date records for campaign and outreach purposes.

Use generative AI to manage customer inquiries with natural and contextually relevant interactions

Powered by accurate, AI-powered customer scores and profiles, layer on an AI agent across all customer touchpoints that can address customer inquiries at scale. This is a fitting solution for growing inquiry volumes that existing service teams are manually unable to attend to, within a reasonable timeframe.

Thanks to Natural Language Processing (NLP), AI agents today can deliver human-like responses, provide answers that are contextually relevant, and thereby reduce wait and resolution times for customers. For complex inquiries, customers are forwarded to a human representative, which enables service teams to focus on situations that require most of their attention.

Traditional AI vs generative AI, depending on budget and timeframes

Have time and budget to spare? Traditional AI is your best bet

As traditional AI is powered by supervised learning, which requires time and human expertise to correctly label data prior to any processing, this is best reserved for use cases where both time and budget are available. However, situations are seldom ideal this way. So semi-supervised learning, where only an initial batch of data is labelled, can help run future analyses.

Generative AI technologies essentially take over after the initial bout of supervised learning, by extrapolating from the supervised batch of data. This is another example of how the two technologies can be used together, for maximum performance.

Need to detect anomalies in a short span of time? Generative AI can help

Generative AI technologies can be a good option for large and completely new datasets that need evaluation. It’s also a great way for non tech-savvy teams to learn about patterns within the data, in order to facilitate quick decision making. Thanks to NLP, many agents powered by genAI are able to understand context and deliver precise results, also making them a powerful tool for internal use, and not just for customers.

Traditional AI vs generative AI: key takeaways

Both traditional AI and generative AI have their strengths, and combining them to create a custom AI solution that meets the needs of your business is the most recommended way forward. In essence, traditional AI and generative AI are best at the following:

• Traditional AI: Data analysis, forecasting, and pattern detection, through supervised learning,
• Generative AI: Creative outputs, such as writing text, building images, and generating code, owing to unsupervised learning.

Additionally, generative AI, thanks to Natural Language Processing, also understands simple and contextually relevant language, enabling it to deliver precise and accurate answers.

If your business already partners with a custom software development company, they may already be able to undertake AI development by training models and integrating these with tools currently in use. Additionally, your software development partner also needs to rethink strategies around project lifecycles, infrastructure, tools, and expertise, in order to facilitate AI-powered solutions for your business.

All of this is best achieved by combining traditional AI and generative AI, as opposed to attempting the adoption of both technologies in silos.