Exiting the North-South Highway 101 onto Mountain View, California, reveals how mundane innovation can appear in person. This Silicon Valley town, home to some of the most prominent tech giants, reveals little more than a few sprawling corporate campuses of glass and steel. As the industry evolves, its architecture naturally grows less inspiring. The most imposing structures, our modern-day coliseums, are massive energy-rich data centres, recursively training LLMs among other technologies. Yet, just as the unassuming exterior of the Googleplex conceals a maze of shiny new software, GenAI harbours immense untapped potential. And people are slowly realising that.
It has been over a year that GenAI burst onto the scene, hastening AI implementations and making AI benefits more identifiable. Today, we see successful use cases and collaborations all the time.
Finding Where Expectations Meet Reality
While the data centres of Mountain View thrum with the promise of a new era, it is crucial to have a quick reality check.
Just as the promise around dot-com startups reached a fever pitch before crashing, so too might the excitement surrounding AI be entering a period of adjustment. Every organisation appears to be looking to materialise the hype.
All eyes (including those of 15 million tourists) will be on Paris as they host the 2024 Olympics Games. The International Olympic Committee (IOC) recently introduced an AI-powered monitoring system to protect athletes from online abuse. This system demonstrates AI’s practical application, monitoring social media in real time, flagging abusive content, and ensuring athlete’s mental well-being. Online abuse is a critical issue in the 21st century. The IOC chose the right time, cause, and setting. All that is left is implementation. That’s where reality is met.
While the Googleplex doesn’t emanate the same futuristic aura as whatever is brewing within its walls, Google’s AI prowess is set to take centre stage as they partner with NBCUniversal as the official search AI partner of Team USA. By harnessing the power of their GenAI chatbot Gemini, NBCUniversal will create engaging and informative content that seamlessly integrates with their broadcasts. This will enhance viewership, making the Games more accessible and enjoyable for fans across various platforms and demographics. The move is part of NBCUniversal’s effort to modernise its coverage and attract a wider audience, including those who don’t watch live television and younger viewers who prefer online content.
From Silicon Valley to Main Street
While tech giants invest heavily in GenAI-driven product strategies, retailers and distributors must adapt to this new sales landscape.
Perhaps the promise of GenAI lies in the simple storefronts where it meets the everyday consumer. Just a short drive down the road from the Googleplex, one of many 37,000-square-foot Best Buys is preparing for a launch that could redefine how AI is sold.
In the most digitally vogue style possible, the chain retailer is rolling out Microsoft’s flagship AI-enabled PCs by training over 30,000 employees to sell and repair them and equipping over 1,000 store employees with AI skillsets. Best Buy are positioning themselves to revitalise sales, which have been declining for the past ten quarters. The company anticipates that the augmentation of AI skills across a workforce will drive future growth.
The Next Generation of User-Software Interaction
We are slowly evolving from seeking solutions to seamless integration, marking a new era of User-Centric AI.
The dynamic between humans and software has mostly been transactional: a question for an answer, or a command for execution. GenAI however, is poised to reshape this. Apple, renowned for their intuitive, user-centric ecosystem, is forging a deeper and more personalised relationship between humans and their digital tools.
Apple recently announced a collaboration with OpenAI at its WWDC, integrating ChatGPT into Siri (their digital assistant) in its new iOS 18 and macOS Sequoia rollout. According to Tim Cook, CEO, they aim to “combine generative AI with a user’s personal context to deliver truly helpful intelligence”.
Apple aims to prioritise user personalisation and control. Operating directly on the user’s device, it ensures their data remains secure while assimilating AI into their daily lives. For example, Siri now leverages “on-screen awareness” to understand both voice commands and the context of the user’s screen, enhancing its ability to assist with any task. This marks a new era of personalised GenAI, where technology understands and caters to individual needs.
We are beginning to embrace a future where LLMs assume customer-facing roles. The reality is, however, that we still live in a world where complex issues are escalated to humans.
The digital enterprise landscape is evolving. Examples such as the Salesforce Einstein Service Agent, its first fully autonomous AI agent, aim to revolutionise chatbot experiences. Built on the Einstein 1 Platform, it uses LLMs to understand context and generate conversational responses grounded in trusted business data. It offers 24/7 service, can be deployed quickly with pre-built templates, and handles simple tasks autonomously.
The technology does show promise, but it is important to acknowledge that GenAI is not yet fully equipped to handle the nuanced and complex scenarios that full customer-facing roles need. As technology progresses in the background, companies are beginning to adopt a hybrid approach, combining AI capabilities with human expertise.
AI for All: Democratising Innovation
The transformations happening inside the Googleplex, and its neighbouring giants, is undeniable. The collaborative efforts of Google, SAP, Microsoft, Apple, and Salesforce, amongst many other companies leverage GenAI in unique ways and paint a picture of a rapidly evolving tech ecosystem. It’s a landscape where AI is no longer confined to research labs or data centres, but is permeating our everyday lives, from Olympic broadcasts to customer service interactions, and even our personal devices.
The accessibility of AI is increasing, thanks to efforts like Best Buy’s employee training and Apple’s on-device AI models. Microsoft’s Copilot and Power Apps empower individuals without technical expertise to harness AI’s capabilities. Tools like Canva and Uizard empower anybody with UI/UX skills. Platforms like Coursera offer certifications in AI. It’s never been easier to self-teach and apply such important skills. While the technology continues to mature, it’s clear that the future of AI isn’t just about what the machines can do for us—it’s about what we can do with them. The on-ramp to technological discovery is no longer North-South Highway 101 or the Googleplex that lays within, but rather a network of tools and resources that’s rapidly expanding, inviting everyone to participate in the next wave of technological transformation.
When OpenAI released ChatGPT, it became obvious – and very fast – that we were entering a new era of AI. Every tech company scrambled to release a comparable service or to infuse their products with some form of GenAI. Microsoft, piggybacking on its investment in OpenAI was the fastest to market with impressive text and image generation for the mainstream. Copilot is now embedded across its software, including Microsoft 365, Teams, GitHub, and Dynamics to supercharge the productivity of developers and knowledge workers. However, the race is on – AWS and Google are actively developing their own GenAI capabilities.
AWS Catches Up as Enterprise Gains Importance
Without a consumer-facing AI assistant, AWS was less visible during the early stages of the GenAI boom. They have since rectified this with a USD 4B investment into Anthropic, the makers of Claude. This partnership will benefit both Amazon and Anthropic, bringing the Claude 3 family of models to enterprise customers, hosted on AWS infrastructure.
As GenAI quickly emerges from shadow IT to an enterprise-grade tool, AWS is catching up by capitalising on their position as cloud leader. Many organisations view AWS as a strategic partner, already housing their data, powering critical applications, and providing an environment that developers are accustomed to. The ability to augment models with private data already residing in AWS data repositories will make it an attractive GenAI partner.
AWS has announced the general availability of Amazon Q, their suite of GenAI tools aimed at developers and businesses. Amazon Q Developer expands on what was launched as Code Whisperer last year. It helps developers accelerate the process of building, testing, and troubleshooting code, allowing them to focus on higher-value work. The tool, which can directly integrate with a developer’s chosen IDE, uses NLP to develop new functions, modernise legacy code, write security tests, and explain code.
Amazon Q Business is an AI assistant that can safely ingest an organisation’s internal data and connect with popular applications, such as Amazon S3, Salesforce, Microsoft Exchange, Slack, ServiceNow, and Jira. Access controls can be implemented to ensure data is only shared with authorised users. It leverages AWS’s visualisation tool, QuickSight, to summarise findings. It also integrates directly with applications like Slack, allowing users to query it directly.
Going a step further, Amazon Q Apps (in preview) allows employees to build their own lightweight GenAI apps using natural language. These employee-created apps can then be published to an enterprise’s app library for broader use. This no-code approach to development and deployment is part of a drive to use AI to increase productivity across lines of business.
AWS continues to expand on Bedrock, their managed service providing access to foundational models from companies like Mistral AI, Stability AI, Meta, and Anthropic. The service also allows customers to bring their own model in cases where they have already pre-trained their own LLM. Once a model is selected, organisations can extend its knowledge base using Retrieval-Augmented Generation (RAG) to privately access proprietary data. Models can also be refined over time to improve results and offer personalised experiences for users. Another feature, Agents for Amazon Bedrock, allows multi-step tasks to be performed by invoking APIs or searching knowledge bases.
To address AI safety concerns, Guardrails for Amazon Bedrock is now available to minimise harmful content generation and avoid negative outcomes for users and brands. Contentious topics can be filtered by varying thresholds, and Personally Identifiable Information (PII) can be masked. Enterprise-wide policies can be defined centrally and enforced across multiple Bedrock models.
Google Targeting Creators
Due to the potential impact on their core search business, Google took a measured approach to entering the GenAI field, compared to newer players like OpenAI and Perplexity. The useability of Google’s chatbot, Gemini, has improved significantly since its initial launch under the moniker Bard. Its image generator, however, was pulled earlier this year while it works out how to carefully tread the line between creativity and sensitivity. Based on recent demos though, it plans to target content creators with images (Imagen 3), video generation (Veo), and music (Lyria).
Like Microsoft, Google has seen that GenAI is a natural fit for collaboration and office productivity. Gemini can now assist the sidebar of Workspace apps, like Docs, Sheets, Slides, Drive, Gmail, and Meet. With Google Search already a critical productivity tool for most knowledge workers, it is determined to remain a leader in the GenAI era.
At their recent Cloud Next event, Google announced the Gemini Code Assist, a GenAI-powered development tool that is more robust than its previous offering. Using RAG, it can customise suggestions for developers by accessing an organisation’s private codebase. With a one-million-token large context window, it also has full codebase awareness making it possible to make extensive changes at once.
The Hardware Problem of AI
The demands that GenAI places on compute and memory have created a shortage of AI chips, causing the valuation of GPU giant, NVIDIA, to skyrocket into the trillions of dollars. Though the initial training is most hardware-intensive, its importance will only rise as organisations leverage proprietary data for custom model development. Inferencing is less compute-heavy for early use cases, such as text generation and coding, but will be dwarfed by the needs of image, video, and audio creation.
Realising compute and memory will be a bottleneck, the hyperscalers are looking to solve this constraint by innovating with new chip designs of their own. AWS has custom-built specialised chips – Trainium2 and Inferentia2 – to bring down costs compared to traditional compute instances. Similarly, Microsoft announced the Maia 100, which it developed in conjunction with OpenAI. Google also revealed its 6th-generation tensor processing unit (TPU), Trillium, with significant increase in power efficiency, high bandwidth memory capacity, and peak compute performance.
The Future of the GenAI Landscape
As enterprises gain experience with GenAI, they will look to partner with providers that they can trust. Challenges around data security, governance, lineage, model transparency, and hallucination management will all need to be resolved. Additionally, controlling compute costs will begin to matter as GenAI initiatives start to scale. Enterprises should explore a multi-provider approach and leverage specialised data management vendors to ensure a successful GenAI journey.
