Tag: Data & AI

Setting and achieving Sustainability goals is complex in BFSI. To be truly sustainable, organisations need to:​

• Reduce internal energy consumption and carbon footprint​
• Fund the transition to decarbonisation in high emission industries​
• Introduce “green” customer​ products and services 
• Monitor carbon data for financed emissions​​

Data and AI have the potential to assist in achieving these objectives, provided they are used effectively.​ Here is how.

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While there has been much speculation about AI being a potential negative force on humanity, what we do know today is that the accelerated use of AI WILL mean an accelerated use of energy. And if that energy source is not renewable, AI will have a meaningful negative impact on CO² emissions and will accelerate climate change. Even if the energy is renewable, GPUs and CPUs generate significant heat – and if that heat is not captured and used effectively then it too will have a negative impact on warming local environments near data centres.

Balancing Speed and Energy Efficiency

While GPUs use significantly more energy than CPUs, they run many AI algorithms faster than CPUs – so use less energy overall. But the process needs to run – and these are additional processes. Data needs to be discovered, moved, stored, analysed, cleansed. In many cases, algorithms need to be recreated, tweaked and improved. And then that algorithm itself will kick off new digital processes that are often more processor and energy-intensive – as now organisations might have a unique process for every customer or many customer groups, requiring more decisioning and hence more digitally intensive.

The GPUs, servers, storage, cabling, cooling systems, racks, and buildings have to be constructed – often built from raw materials – and these raw materials need to be mined, transported and transformed. With the use of AI exploding at the moment, so is the demand for AI infrastructure – all of which has an impact on the resources of the planet and ultimately on climate change.

Sustainable Sourcing

Some organisations understand this already and are beginning to use sustainable sourcing for their technology services. However, it is not a top priority with Ecosystm research showing only 15% of organisations focus on sustainable procurement.

Technology Providers Can Help

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.

1. Think in terms of outcomes – not use cases
2. Identify and eliminate digital debt
3. Build the right data platform architecture
4. Adopt a dual AI strategy
5. Be part of an innovation ecosystem

Read on to find out more.

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Organisations will continue their quest to become digital and data-first in 2023. Business process automation will be a priority for the majority; but many will look at their data strategically to derive better business value. 

As per Ecosystm’s Digital Digital Enterprise Study 2022, organisations will focus equally on Automation and Strategic AI in 2023.

Data & AI initiatives are firmly at the core of any organisation’s tech-led transformation efforts. Businesses today realise the value of real-time data insights to deliver the agility that is required to succeed in today’s competitive, and often volatile, market.

But organisations continue to struggle with their data & AI initiatives for a variety of reasons. Organisations in ASEAN report some common challenges in implementing successful data & AI initiatives.

Here are 5 insights to build scalable AI.

1. Data Access a Key Stumbling Block. Many organisations find that they no longer need to rely on centralised data repositories.
2. Organisations Need Data Creativity. A true data-first organisation derives value from their data & AI investments across the entire organisation, cross-leveraging data.
3. Governance Not Built into Organisational Psyche. A data-first organisation needs all employees to have a data-driven mindset. This can only be driven by clear guidelines that are laid out early on and adhered to by data generators, managers, and consumers.
4. Lack of End-to-End Data Lifecycle Management. It is critical to have observability, intelligence, and automation built into the entire data lifecycle.
5. Democratisation of Data & AI Should Be the Goal. The true value of data & AI solutions will be fully realised when the people who benefit from the solutions are the ones managing the solutions and running the queries that will help them deliver better value to the business.

Read below to find out more.

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In the rush towards digital transformation, individual lines of business in organisations, have built up collections of unconnected systems, each generating a diversity of data. While these systems are suitable for rapidly launching services and are aimed at solving individual challenges, digital enterprises will need to take a platform approach to unlock the full value of the data they generate.

Data-driven enterprises can increase revenue and shift to higher margin offerings through personalisation tools, such as recommendation engines and dynamic pricing. Cost cutting can be achieved with predictive maintenance that relies on streaming sensor data integrated with external data sources. Increasingly, advanced organisations will monetise their integrated data by providing insights as a service.

Digital enterprises face new challenges – growing complexity, data explosion, and skills gap.

Here are 5 ways in which IT teams can mitigate these challenges.

1. Data & AI projects must focus on data access. When the organisation can unify data and transmit it securely wherever it needs to, it will be ready to begin developing applications that utilise machine learning, deep learning, and AI.
2. Transformation requires a hybrid cloud platform. Hybrid cloud provides the ability to place each workload in an environment that makes the most sense for the business, while still reaping the benefits of a unified platform.
3. Application modernisation unlocks future value. The importance of delivering better experiences to internal and external stakeholders has not gone down; new experiences need modern applications.
4. Data management needs to be unified and automated. Digital transformation initiatives result in ever-expanding technology estates and growing volumes of data that cannot be managed with manual processes.
5. Cyber strategy should be Zero Trust – backed by the right technologies. Organisations have to build Digital Trust with privacy, protection, and compliance at the core. The Zero Trust strategy should be backed by automated identity governance, robust access and management policies, and least privilege.

Read below to find out more.

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In recent years, businesses have faced significant disruptions. Organisations are challenged on multiple fronts – such as the continuing supply chain disruptions; an ongoing energy crisis that has led to a strong focus on sustainability; economic uncertainty; skills shortage; and increased competition from digitally native businesses. The challenge today is to build intelligent, data-driven, and agile businesses that can respond to the many changes that lie ahead.

Leading organisations are evaluating ways to empower the entire business with data, machine learning, automation, and AI to build agile, innovative, and customer-focused businesses. 

Here are 7 steps that will help you deliver business value with data and AI:

• Understand the problems that need solutions. Before an organisation sets out on its data, automation, and AI journey, it is important to evaluate what it wants to achieve. This requires an engagement with the Tech/Data Teams to discuss the challenges it is trying to resolve.
• Map out a data strategy framework. Perhaps the most important part of this strategy are the data governance principles – or a new automated governance to enforce policies and rules automatically and consistently across data on any cloud.
• Industrialise data management & AI technologies. The cumulation of many smart, data-driven initiatives will ultimately see the need for a unified enterprise approach to data management, AI, and automation.
• Recognise the skills gap – and start closing it today. There is a real skills gap when it comes to the ability to identify and solve data-centric issues. Many businesses today turn to technology and business consultants and system integrators to help them solve the skills challenge.
• Re-start the data journey with a pilot. Real-world pilots help generate data and insights to build a business case to scale capabilities.
• Automate the outcomes. Modern applications have made it easier to automate actions based on insights. APIs let systems integrate with each other, share data, and trigger processes; and RPA helps businesses automate across applications and platforms.
• Learn and improve. Intelligent automation tools and adaptive AI/machine learning solutions exist today. What organisations need to do is to apply the learnings for continuous improvements.

Find more insights below.

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