Generative AI is undoubtedly at the forefront of the most transformative technology forces to have emerged in recent years.
As a key component of the digital revolution, enterprise companies are eagerly seeking to understand and harness the power of this rapidly developing AI domain.
And with the global AI market size set to grow from nearly £100 million to over £1,48 trillion by 2030, it’s a clear indication that they’re heading in the right direction.
In a recent article by VentureBeat, we’re given access to pivotal insights from their VB Transform event that brought together AI experts from across various industries.
Here are four pivotal insights enterprise leaders need to know about generative AI:
4. Primacy of the Data Layer
“Data is the new oil”. “Garbage in, Garbage out”.
When it comes to generative AI, these adages couldn’t be more true.
Just as oil fuels industries, data is the powerhouse for large language models (LLMs) – the core pillar of generative AI.
But with challenges in the collection, organisation, and cleaning of this vital resource, to harness the benefits of generative AI, enterprises must prioritise and invest in their data layers.
“Legacy systems are often outdated and may not have the necessary infrastructure to support the latest AI and ML technologies. This may result in slower processing times, increased downtime, and increased maintenance costs,” reveals Deloitte.
Enterprises like Intuit provide an excellent example of this, constructing a new operating system centred around generative AI and putting data at the heart of their strategy. “Rewire your entire organisation around data,” advised Intuit’s chief data officer, Ashok Srivastava, during his VB Transform talk.
3. The Shift Toward Large Language Models (LLMs)
As we traverse further into the digital age, LLMs will inevitably become the default interface for all computing.
It will ultimately revolutionise the way we interact with technology, much as the phone and touchscreen did before them.
Nick Frosst, co-founder of LLM-building company Cohere, highlights this during his VB Transform talk.
The paradigm of Google-led search could be on its way out, he suggested, replaced by intuitive, natural language interactions with data and information sources. This shift could redefine user experience design, personalization, and privacy, leading to a myriad of opportunities and challenges.
2. Multiple Paths to LLM Implementation
With the rising adoption of LLMs, decision-makers are exploring the best strategies for building chatbots and other LLM-driven applications.
Enterprises can choose to build, borrow, or piggyback, depending on their resources, proprietary data, and urgency to reach the market.
While large organisations with ample resources may choose to build their models or partner with firms like Teraflow.ai, others might opt to fine-tune open-source models like LLaMA to suit their specific use cases.
For smaller-scale applications not requiring proprietary data, ready-to-use solutions like ChatGPT’s raw API or a customised version of it chained to a vector database (via a framework like Langchain) may be the way to go.
Regardless of the AI solution, choosing to partner with external professionals helps fast-track the process.
1. Expansive Use Cases: Think Big
LLMs present a plethora of applications for enterprises:
- Content generation
- Creating new software
- Personalisation
- Collaborative problem-solving
- Building entirely new products and companies
A discussion featuring Amazon AWS VP of product Matt Wood and Google VP of data and analytics Gerrit Kazmaier, articulated a vibrant tapestry of use cases for LLMs in enterprise settings.
“Generation, including not only content but new software; ranking, personalization and relevancy apps; apps to allow experts and others to learn more efficiently about new fields; collaborative problem-solving through automated decision-support; new customer experiences; building entirely new products; and building new companies.”
In line with the internet boom, which spurred the emergence of groundbreaking companies like Amazon, Netflix, and Airbnb, they predicted an even faster pace of innovation driven by LLMs.
As Generative AI continues to evolve and Mature
With the maturity of this powerful technology, opportunities for businesses to innovate and redefine their products, experiences, and business models will crop up like never before.
The key to harnessing this power lies in:
- Understanding and leveraging the potential of large language models
- Placing data at the heart of organisational strategy
- Exploring the use-cases presented by this transformative technology
And fast-tracking the process with us. Make your (Generative) AI Work today!
