The Financial Services industry can benefit greatly from leveraging Data and AI technologies to enhance client value and innovation. BFSI organisations want to deliver AI-driven outcomes.
However, many AI projects fail to deliver long-term business value. Leaders in the industry must overcome challenges such as
- Converting proofs of concept to scalable implementations
- Deploying end-to-end AI and Data strategies
- Evolving business requirements
- Responding to emerging trends such as Generative AI.
As a technology leader in BFSI, here are 5 ways you can help deliver on your organisation’s AI ambitions.
- Think in terms of outcomes – not use cases
- Identify and eliminate digital debt
- Build the right data platform architecture
- Adopt a dual AI strategy
- Be part of an innovation ecosystem
Read on to find out more.
Click here to download ‘5 Actions to Achieve Your AI Ambitions’ as a PDF
Google recently extended its Generative AI, Bard, to include coding in more than 20 programming languages, including C++, Go, Java, Javascript, and Python. The search giant has been eager to respond to last year’s launch of ChatGPT but as the trusted incumbent, it has naturally been hesitant to move too quickly. The tendency for large language models (LLMs) to produce controversial and erroneous outputs has the potential to tarnish established brands. Google Bard was released in March in the US and the UK as an LLM but lacked the coding ability of OpenAI’s ChatGPT and Microsoft’s Bing Chat.
Bard’s new features include code generation, optimisation, debugging, and explanation. Using natural language processing (NLP), users can explain their requirements to the AI and ask it to generate code that can then be exported to an integrated development environment (IDE) or executed directly in the browser with Google Colab. Similarly, users can request Bard to debug already existing code, explain code snippets, or optimise code to improve performance.
Google continues to refer to Bard as an experiment and highlights that as is the case with generated text, code produced by the AI may not function as expected. Regardless, the new functionality will be useful for both beginner and experienced developers. Those learning to code can use Generative AI to debug and explain their mistakes or write simple programs. More experienced developers can use the tool to perform lower-value work, such as commenting on code, or scaffolding to identify potential problems.
GitHub Copilot X to Face Competition
While the ability for Bard, Bing, and ChatGPT to generate code is one of their most important use cases, developers are now demanding AI directly in their IDEs.
In March, Microsoft made one of its most significant announcements of the year when it demonstrated GitHub Copilot X, which embeds GPT-4 in the development environment. Earlier this year, Microsoft invested $10 billion into OpenAI to add to the $1 billion from 2019, cementing the partnership between the two AI heavyweights. Among other benefits, this agreement makes Azure the exclusive cloud provider to OpenAI and provides Microsoft with the opportunity to enhance its software with AI co-pilots.
Currently, under technical preview, when Copilot X eventually launches, it will integrate into Visual Studio — Microsoft’s IDE. Presented as a sidebar or chat directly in the IDE, Copilot X will be able to generate, explain, and comment on code, debug, write unit tests, and identify vulnerabilities. The “Hey, GitHub” functionality will allow users to chat using voice, suitable for mobile users or more natural interaction on a desktop.
Not to be outdone by its cloud rivals, in April, AWS announced the general availability of what it describes as a real-time AI coding companion. Amazon CodeWhisperer, integrates with a range of IDEs, namely Visual Studio Code, IntelliJ IDEA, CLion, GoLand, WebStorm, Rider, PhpStorm, PyCharm, RubyMine, and DataGrip, or natively in AWS Cloud9 and AWS Lambda console. While the preview worked for Python, Java, JavaScript, TypeScript, and C#, the general release extends support for most languages. Amazon’s key differentiation is that it is available for free to individual users, while GitHub Copilot is currently subscription-based with exceptions only for teachers, students, and maintainers of open-source projects.
The Next Step: Generative AI in Security
The next battleground for Generative AI will be assisting overworked security analysts. Currently, some of the greatest challenges that Security Operations Centres (SOCs) face are being understaffed and overwhelmed with the number of alerts. Security vendors, such as IBM and Securonix, have already deployed automation to reduce alert noise and help analysts prioritise tasks to avoid responding to false threats.
Google recently introduced Sec-PaLM and Microsoft announced Security Copilot, bringing the power of Generative AI to the SOC. These tools will help analysts interact conversationally with their threat management systems and will explain alerts in natural language. How effective these tools will be is yet to be seen, considering hallucinations in security is far riskier than writing an essay with ChatGPT.
The Future of AI Code Generators
Although GitHub Copilot and Amazon CodeWhisperer had already launched with limited feature sets, it was the release of ChatGPT last year that ushered in a new era in AI code generation. There is now a race between the cloud hyperscalers to win over developers and to provide AI that supports other functions, such as security.
Despite fears that AI will replace humans, in their current state it is more likely that they will be used as tools to augment developers. Although AI and automated testing reduce the burden on the already stretched workforce, humans will continue to be in demand to ensure code is secure and satisfies requirements. A likely scenario is that with coding becoming simpler, rather than the number of developers shrinking, the volume and quality of code written will increase. AI will generate a new wave of citizen developers able to work on projects that would previously have been impossible to start. This may, in turn, increase demand for developers to build on these proofs-of-concept.
How the Generative AI landscape evolves over the next year will be interesting. In a recent interview, OpenAI’s founder, Sam Altman, explained that the non-profit model it initially pursued is not feasible, necessitating the launch of a capped-for-profit subsidiary. The company retains its values, however, focusing on advancing AI responsibly and transparently with public consultation. The appearance of Microsoft, Google, and AWS will undoubtedly change the market dynamics and may force OpenAI to at least reconsider its approach once again.
Healthcare has transformed rapidly in the last few years – processes have become more agile; clinicians, administrative staff, and patients have changed their views on how healthcare can and should be delivered; and there is a greater reliance on technology today.
Despite challenges such as healthcare inequality and limited access to care for underserved populations, the future of Healthcare looks promising.
We will see continued advancements in technology, increased collaboration between healthcare providers and patients, and a clear shift in focus on preventative care.
Read on to find out how South Australia Health, Tan Tock Seng Hospital in Singapore, Montana State University and Billings Clinic, Microsoft, Epic, Zuellig Pharma, and iHIS Singapore are innovating to improve patient and employee experiences and clinical outcomes.
Download “The Future of Healthcare” as a PDF
The Retail industry has faced significant challenges in recent times. Retailers have had to deliver digital experiences and delivery models; navigate global supply chain disruptions; accommodate the remote work needs of their employees; and keep up with rapidly changing customer expectations. To remain competitive, many retailers have made significant investments in technology.
However, despite these investments, many retailers have struggled to create market differentiation. The need for innovation and constant evolution remains.
As retailers cope with hypersonalisation trends, supply chain vulnerabilities, and the rise of ESG consciousness, the industry is seeing several instances on innovation.
Read on to find out how brands such as Clinique, Gucci, Tommy Hilfiger, Nike, Woolworths, Prada, Levi Strauss, Mahsenei Hashuk and Instacart are using emerging technologies such as the Metaverse and Generative AI to create the much-needed market edge.
Download “The Future of Retail” as a PDF