Building a Data-Driven Foundation to Super Charge Your AI Journey

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AI has become a business necessity today, catalysing innovation, efficiency, and growth by transforming extensive data into actionable insights, automating tasks, improving decision-making, boosting productivity, and enabling the creation of new products and services.

Generative AI stole the limelight in 2023 given its remarkable advancements and potential to automate various cognitive processes. However, now the real opportunity lies in leveraging this increased focus and attention to shine the AI lens on all business processes and capabilities. As organisations grasp the potential for productivity enhancements, accelerated operations, improved customer outcomes, and enhanced business performance, investment in AI capabilities is expected to surge.

In this eBook, Ecosystm VP Research Tim Sheedy and Vinod Bijlani and Aman Deep from HPE APAC share their insights on why it is crucial to establish tailored AI capabilities within the organisation.

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AI Research and Reports
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Putting Data at the Core of CX Transformation

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In today’s digital world, data is an essential part of almost everything we do. From making informed business decisions to providing the best customer outcomes, data plays a crucial role in shaping organisations’ actions and strategies. With the increasing availability of customer data, companies can now gain valuable insights into customer behaviour, preferences, and expectations; and offer personalised experiences to build long-lasting relationships.

Ecosystm Principal Advisor, Audrey William talks about 5 things to keep in mind when working on your data strategy to improve customer experience.

  1. Build a data-driven CX culture. If you don’t have a Chief Experience Officer, appoint one.
  2. Understand your data needs. Blindly gathering data without evaluating significance or utilisation, can cost you.
  3. Evaluate your data repositories. Invest in a CDP or an Intelligent Data Platform for a unified view of customer data.
  4. Use Speech Analytics to truly understand your customer. Go beyond traditional metrics to gather data-driven insights.
  5. Aim to achieve hyperpersonalisation. Make it the goal and core of your data and customer strategies.

Read on to find more.

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The Experience Economy
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5 Insights to Help Organisations Build Scalable AI – An ASEAN View

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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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The Future of Business: 7 Steps to Delivering Business Value with Data & AI

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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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