The Real Impact of Generative AI: Beyond the Hype Cycle
Why You Should Act Now to Leverage Generative AI for Business Growth and Stay Ahead of Competitors
Introduction
The release of ChatGPT in December 2022 ignited a boom in Generative AI (Gen AI). This breakthrough spurred a race among companies to develop increasingly advanced large language models (LLMs), including multimodal ones. Startups have flourished, building products with enhanced capabilities leveraging these LLMs. Major firms – such as NVIDIA, cloud providers, and even phone and laptop manufacturers – are pouring investments into Gen AI to avoid missing early adopters who could become future customers. Industries across the board are keen to capture this wave of innovation.
The Gartner Hype Cycle and Its Dangers
With any new technology, there are believers and skeptics. The Gartner Hype Cycle suggests that new tech undergoes inflated expectations before plummeting into the “trough of disillusionment.” Many believe that Gen AI has just passed its peak of inflated expectations and is poised for a fall. While the Hype Cycle accurately describes many new technologies, focusing solely on it can be misleading.
Exaggerated claims often stem from enthusiasts, developers who haven’t fully assessed limitations beyond prototype use cases, or vendors eager to promote their platforms, a group I called hyper-inflater. Consequently, organisations attempting similar feat to build production-grade systems may encounter challenges, leading to disillusionment.
The Risk of Staying on the Sidelines
This blog offers a different perspective: the danger for organisations that, fearing over investment due to the Hype Cycle, choose to wait and stay on the sidelines — the skeptics. This groups expectation of Gen AI capabilities is represented by the grey lines (skeptic fallacy) — very low expectations and eventually as many organisation sharing success stories it started to catch up at the plateau of productivity point.
The thing is… not all capabilities are hype; a significant portion is real, offering tangible benefits that will improve over time. Gen AI’s real capabilities, represented by the green curve are substantial enough to uplift business operations by unlocking new ideas, enhancing processes, boosting team efficiency, and improving customer experiences.
More importantly, these capabilities are potent enough to disrupt and threaten businesses, even from smaller competitors who effectively harness them.
Organisations that over-rely on the Hype Cycle may miss out on leveraging Gen AI’s true potential, finding themselves too late if competitors have already capitalised on these technologies which is represented by the difference between missed out opportunity gap (real capability minus their existing non Gen AI capability)
If you’re among those hesitant to invest in Gen AI, this blog aims to encourage you to reconsider and focus on the real capabilities you can leverage now.
My Experience with Gen AI
To avoid being labeled as a hyper-inflater, let me share my experience where I based my information in this blog from. I lead an AI division that has successfully built and deployed three LLM-powered consumer-facing technologies, including Retrieval Augmented Generation (RAG). As part of our AI Governance Committee, I rigorously review AI developments, especially those powered by Gen AI, ensuring safety and responsibility. Our organisation uses ChatGPT’s team license, which has demonstrably improved employee productivity.
In the rest of this blog, I’ll share my experience how Gen AI has already delivered tangible benefits in four areas.
Gen AI Accelerates AI Development
Gen AI is replacing many traditional NLP AI techniques by offering better accuracy, shorter development times, and significantly reduced maintenance. For example, consider detecting popular topics and sentiments from customer product reviews.
Traditionally, a data scientist would spend weeks collecting and cleaning data, splitting reviews into paragraphs or sentences, labelling each chunk, training models, and tuning hyper-parameters. An ML engineer would then spend additional weeks productionising the model, building continuous training pipelines, and maintaining the system.
With Gen AI, specifically LLMs, this process is streamlined and can be at least three times cheaper:
- Data Collection and Labelling — Data scientists still collect and label datasets but primarily to evaluate prompts against an LLM.
- Prompt Engineering — They perform prompt engineering iterations, evaluating prompts against validation sets until acceptable accuracy is achieved.
- Simplified Production — Productionising the model becomes much simpler – essentially an API call to an LLM provider. There’s no need to handle large model binaries or complex ML frameworks, significantly reducing maintenance and infrastructure costs.
Often, the LLM solution provides higher accuracy, as it can process entire reviews without losing context – a common challenge with traditional NLP techniques.
In the past six months, LLMs have become our first choice for many NLP tasks like sentiment analysis, entity extraction (e.g., detecting brand names, locations), and text classification. This shift has tremendously boosted our productivity.
Addressing Common Concerns
- Cost — While some use cases of LLMs can be expensive, various sizes are available, and costs are proportional to size. You should evaluate and choose the smallest model that meets your acceptable accuracy. When factoring in labor costs for building, productionising, and maintenance, the LLM option is often way more cost-effective.
- Speed — LLMs can be slow, but selecting the appropriate size can improve response times. Moreover, for applications like sentiment analysis of customer reviews, real-time processing isn’t always necessary.
With the extra time saved, your team can support your business better by building more AI technologies. Similarly, your competitors who are leveraging this capability may be building AI solutions at three times the speed.
Enhancing Customer Experience with Gen AI
Gen AI doesn’t just improve AI models’ accuracy; it also enables you to deliver highly personalised web or app experiences. For instance:
- Amazon — introduced Rufus, a chatbot that answers user questions about specific products and even compares items like running and hiking shoes.
- Carsales — implemented an AI search feature for automotive editorial articles, returning relevant content and direct answers when applicable. This resulted in increased click-through rates and positive user feedback.
Empowering Software Engineers with AI Skills
In many organisations, software engineers outnumber data scientists and ML engineers. Some organisations might not have any data scientists or ML engineers at all, limiting their ability to build AI technologies.
I’m a strong advocate for AI democratisation. While software engineers can be trained to become citizen data scientists or ML engineers, barriers exist – many may not be proficient in Python and ML techniques, for example.
With Gen AI, many LLM use cases involve simple API calls accessible through frameworks available in most programming languages. This accessibility allows software engineers to leverage AI capabilities with proper guidelines on evaluating LLM models.
Imagine the immense capabilities and efficiency gains when all your software engineers can leverage AI. Similarly, your competitors might already be building AI tech more cheaply by utilising their existing software engineers.
Boosting Non-Technical Team Productivity
Gen AI significantly boosts productivity for non-technical teams as well. While many productivity tools like Jira, Microsoft Office Suite, Confluence, and Miro are integrating Gen AI capabilities, standalone chatbot tools like ChatGPT, Claude for Work, or Perplexity.ai are highly useful. They help team members perform tasks such as:
- Summarising Long Documents — Quickly distill essential information from lengthy reports.
- Brainstorming Ideas — Generate creative concepts or solutions to problems.
- Analysing Large Datasets — Examine extensive data like product reviews without waiting for specialised teams.
- Paraphrasing Documents — Reword content for clarity or different audiences.
- Conducting Market Research — Gather insights on market trends and competitors efficiently.
Our test group have reported an over 5% productivity increase by using tools like ChatGPT. For a $20 monthly license, this offers a high return on investment for businesses. Tasks like analysing large datasets no longer require creating support tickets and waiting for data science or analytics teams, significantly improving efficiency.
However, guidelines are necessary to ensure teams know how to validate outputs and practice effective prompt engineering. The last thing you want is a business decision based on inaccurate analysis produced by an LLM.
Again, consider that your competitors leveraging these tools may have over 5% productivity boost.
Summary
Gen AI is not just hype; it’s already delivering real business value. While exaggerated claims are common, ignoring its potential could leave organisations at risk. Gen AI accelerates AI development, enhances customer experiences, and boosts productivity by over 5%, even allowing software engineers to build AI solutions faster. The pace of innovation is swift, with advancements doubling every three months (GPT4o is now 50x cheaper compare to original GPT4 in March 2023). Testing Gen AI with low-risk pilots is the best way to understand its potential.
Caution is important — while many use cases may not be direct customer-facing and thus have less emphasis on safety, customer-facing chatbots do require strict safety measures to prevent reputation damage when providing misleading answer. Establishing strong AI governance and data validation practices can help mitigate these risks, which I will cover in a future blog. However, the risk of inaction is greater, as staying on the sidelines may leave your business vulnerable to competitors already leveraging Gen AI’s powerful capabilities. Balancing caution with innovation is key to thriving in this rapidly evolving landscape.
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