Barclay Pearce Capital, in collaboration with Global X ETFs, is excited to welcome Max Kaye, Head of Data, Analytics, and AI at Google AU & NZ, to our upcoming event: Artificial Intelligence in Technology.
Ahead of Max’s presentation, BPC is pleased to share insights into how Google has leveraged its expertise in data and AI to distinguish itself from other cloud AI providers and achieve remarkable capabilities.
Artificial Intelligence: How AI is reshaping profitability as companies embrace machine learning and automation to drive growth.
Consider the banking sector, where AI-driven innovation is projected to add an impressive $340 billion to operating profits in the U.S. alone. This uptick reflects how banks are leaning into AI-powered systems to streamline customer service, mitigate fraud, and fine-tune product personalisation. These efficiencies make AI a cornerstone of modern banking strategy, and they’re paying off
In the high-tech industry, AI’s influence is equally pronounced, with generative AI expected to contribute between 4.8% and 9.3% to annual revenues. Market leaders like Amazon and Google are already integrating tools like Amazon’s Bedrock and Google’s Gemini model to accelerate everything from search capabilities to visual tools, translating AI’s efficiencies into stronger engagement and faster innovation cycles. As a result, the high-tech sector is rapidly becoming a proving ground for AI’s direct impact on profitability.
Globally, the potential is even more significant. McKinsey estimates AI could add $2.6 trillion to $4.4 trillion annually to corporate profits worldwide, an indicator of just how much AI’s transformative potential is worth. The benefits here are broad: AI reduces costs by automating workflows, optimises supply chains, and enables data-driven pricing—all critical levers for profit maximisation.
Source: IEEE
There’s also a longer-term impact on labour productivity, with AI expected to boost annual productivity by 0.1% to 0.6% through 2040. The applications range from automating back-office tasks to enabling frontline employees to focus on high-value work. This productivity gain represents a steady growth engine, which translates directly to profitability as firms free up human resources for strategic tasks.
Source: QuantumBlack - AI by Mckinsey
In financial services, AI’s impact is equally robust, with an estimated $35 billion in sector-wide investments expected in 2023, including $21 billion from banking alone. This investment underlines the sector's commitment to leveraging AI for a competitive edge in areas from customer insights to automated trading and compliance management
These figures spotlight AI as a critical driver of profitability and growth, reshaping how companies across sectors achieve operational efficiency and expand revenue streams. As industries integrate AI at unprecedented scales, the technology’s role in enhancing the bottom line appears to be only in its early stages, promising sustained economic and competitive advantages across the corporate world.
The Giants and Their Kingdoms: How Amazon, Microsoft, and Google are leveraging AI to drive efficiency and enhance customer experiences in significant ways.
Amazon has scaled its AI capabilities with tools like Bedrock and Amazon Q. Bedrock allows companies to access advanced AI models, including those from Anthropic and Meta's Llama, enabling quick, customisable insights without the need for specialised teams. Internally, Amazon Q saved around $260 million and 4,500 developer years by migrating over 30,000 applications, showing AI’s impact in reducing costs and streamlining workflows.
Microsoft uses AI in productivity and development tools. Microsoft 365 Copilot, used by 70% of the Fortune 500, saves Vodafone employees an average of three hours weekly. GitHub Copilot Auto Fix also enhances software speed and security by automating vulnerability detection and repair, which companies like Asurion have adopted. Azure AI powers sector-specific solutions, like GE Aerospace’s employee query management system, showcasing AI’s versatility across industries.
Google drives AI advancements with its Gemini model, powering search and visual search tools like Google Lens, which handles over 20 billion visual searches monthly, boosting user engagement and ad opportunities. Additionally, Vertex AI and BigQuery help companies make real-time decisions; for instance, Hiscox reduced insurance quoting time from days to minutes, highlighting AI’s role in improving data processing and decision-making efficiency.
Across these companies, AI is not just a tool for innovation but a strategic asset that drives efficiency, enables better customer experiences, and opens up new revenue streams. Each example underscores how AI is reshaping the tech sector, bringing powerful, tailored solutions to companies and transforming how they operate.
Enter Google AI and the TPU
In the world of Cloud AI, Google has strategically positioned itself as a leader by leveraging its extensive expertise in machine learning, vast data resources, and unique ecosystem. Unlike its competitors such as AWS and Azure, Google has taken a different approach that combines precision, adaptability, and comprehensive integration.
Google's entry into the Cloud AI sector, though later than some of its competitors, has been marked by strategic advancements rather than sheer computational power. This strategy is exemplified by their development of the Gemini models. These models are integrated across all seven of Google’s major products and platforms, which collectively serve over 2 billion monthly users. This extensive application is a testament to the robustness and versatility of Google’s AI solutions.
The Gemini 1.5 Pro model, for instance, boasts a 2-million-token context window, allowing it to process vast amounts of information in a single session. This capability significantly enhances its performance in long-form data processing and extended user interactions, making it ideal for applications that require consistent tracking of details, such as legal analysis, customer support, and data review.
Source: Google
Google’s differentiation from the competition is further highlighted by their approach to AI development. Rather than focusing on generic solutions, Google crafts models tailored to the specific needs of various industries. This precision is achieved through decades of machine learning experience, resulting in AI that is both highly effective and adaptable.
A critical component of Google’s AI strategy is its data advantage. Google has access to an unparalleled volume and diversity of data, sourced from an array of platforms including Search and YouTube. This rich data pool allows Google to train AI models that are remarkably accurate and capable of identifying patterns that others may overlook.
Source: IEEE
Moreover, Google’s proprietary hardware, particularly their Tensor Processing Units (TPUs), plays a crucial role in their AI capabilities. These TPUs, now in their sixth generation, are specifically designed to meet the demands of Google’s AI models, offering significant performance improvements and cost reductions. For instance, LG AI Research has reported over a 50% reduction in inference times and a 72% decrease in costs using these advanced TPUs.
In summary, Google’s leadership in AI is driven by a combination of strategic innovation, extensive data resources, and specialised hardware. Their approach distinguishes them from competitors by emphasising precision, adaptability, and integration, setting a high standard in the evolving field of artificial intelligence.