The Future is now for Artificial Intelligence
Apac CIOOutlook | Friday, December 27, 2019
Artificial Intelligence (AI) has been around for a few decades. However, it has made its way out of the research labs and is making its presence felt in our lives now. From Spotify to Uber, there is an intelligent algorithm at work trying to make our lives seamless. Customers’ perception of businesses and the services offered are being influenced by the ease of interaction they have with them. While there are obvious benefits to customers, AI can help businesses solve complex problems faster. Improvement in data analytics and computing power has ensured that there is hardly any industry that is immune to the impact of AI.
The current uses of AI have been focused on improving human productivity and decision making through a term we know as Narrow AI or Machine Learning. This is the form of intelligence that is being used by most organizations to process enormous amounts of data to provide insights to humans. Organizations have begun to understand the impact that AI can have on their workforce by reducing the countless hours of mundane activity to a few seconds. A contact center associate already knows why the customer is calling and has a solution ready or a mortgage underwriter only needs to review a decision on a complex file because the intelligent algorithm has already processed the rules and compared it to the outcomes of millions of previous interactions or decisions. The impact could be as minimal as automation of a few process steps to as extensive as completely re-imagining the way a process should work; for instance, an image recognition and analysis algorithm could completely transform auto claims processing by not needing the vehicle to be physically inspected by an insurance assessor.
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Organizations need to have an AI strategy in place to reap the most benefits from the advancements in this technology. Tangible benefits are already evident within industries with a high level of customer interaction like financial services and retail.
Organizations have begun to understand the impact that ai can have on their workforce by reducing the countless hours of mundane activity to a few seconds
However, the momentum is lacking due to a myopic view of a perceived ROI limited to cost savings. While the cost savings through improved efficiency can be huge, there is a long-term play in improving customer satisfaction significantly and locking them into your services. A recent research by Salesforce—“State of the connected customer”, reported that 51 percent of customers’ expectations of companies were influenced by AI.
The ideal use cases for AI would be problems that solve for the most important customer pain points as well as improve key business metrics. The most talked about use of AI has ranged from fraud and risk mitigation to cross-selling or up-selling the right product to the customers based on their needs. Every industry will have some pioneers who have worked on a few use cases that provide the highest value for them. This can vary according to the industry or the size of the organization. While it is important to not follow the herd, it is a good starting point if your processes and the problems faced by you are similar.
So how do you really begin to build your AI roadmap?
1. Be clear about the business problem that you want to solve. Are you sure that AI is the only way to solve this problem? AI helps to solve problems faster allowing humans to focus on analyzing the output and making the right decisions. However, it is important to be clear about what the outcome means for the organization and ensuring that there is leadership support. AI also needs to be part of the emergent application landscape of the CIO team.
2. Invest in data. Your intelligent algorithm is only as good as the data being fed to it. The algorithm learns through the right data being fed to it and the ongoing interaction with humans. The results will not be immediate. However, it is important to know that the model is learning and improving with every interaction.
3. Build a cross functional team with data, engineering and product/business skills. It is important to start with a central team that can work with the business units to understand their problems and then be able to build a solution and show the benefits through a business case. The structure of the team within the organization is important and should report to the CIO/CEO to ensure that the leader of this team has visibility of challenges across the enterprise.
4. Invest in capability uplift of your people. As AI starts to make large parts of the current processes redundant, it is important to have a talent strategy in place to redirect your people’s effort to more value adding activities.
If you are not seriously considering investing in AI, your competitor or a nimble new entrant definitely is. The time to think of AI as a strategic competitive advantage has gone. It is now fast reaching a stage where any organization without a clear AI strategy/roadmap will start losing market share rapidly.
The future is now for artificial intelligence.
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Finance companies are exploring quantum algorithms to optimize portfolios, manage risk, and detect fraud faster than traditional systems. Even logistics and manufacturing are seeing advantages, with quantum-based optimization improving supply chain efficiency and resource allocation in ways that were once impossible at scale.
Beyond computational speed, industries are also benefiting from advances in quantum sensing and communication. Quantum sensors offer unprecedented precision in fields like navigation, energy exploration, and environmental monitoring, while quantum encryption promises highly secure communication channels that could transform data security.
By integrating these capabilities, businesses can make smarter decisions, reduce costs, and innovate faster. While adoption is still in early stages, the combination of computation, sensing, and secure communication is already giving forward-looking companies a competitive edge, showing that Quantum technology is moving from theory into tangible industry impact.
What Innovations Are Shaping the Future of Quantum Technology?
The future of Quantum technology is being driven by innovations that make quantum systems more stable, scalable, and practical for real-world use. Advances in qubit design, materials, and error-correction techniques are improving reliability, while hybrid quantum-classical algorithms allow businesses and researchers to harness quantum capabilities even before fully fault-tolerant machines are ready. This is opening early applications in areas like finance, logistics, and healthcare, where complex problems can be solved faster and more accurately than ever before.
Moreover, breakthroughs in quantum communication, sensing, and software are expanding its reach. Quantum networks promise ultra-secure data transfer, while quantum sensors deliver unmatched precision in navigation, energy, and environmental monitoring. Cloud-based platforms are also making quantum tools accessible to smaller organizations, democratizing experimentation and innovation. Together, these advancements are positioning Quantum technology as a transformative force that can reshape industries, accelerate scientific discovery, and unlock entirely new possibilities. ...Read more
Infocus International is delighted to bring one of their best virtual workshops, Public-Private Partnership (PPP) that will be set to commence 17 August 2026 .
We need new infrastructure. Roads, airports, schools, hospitals and housing: the list is enormous and growing. Yet severely limited budgets, economic uncertainty caused by volatile commodity prices, and deficits continue to prevent government at all levels from delivering the kinds of structural change that has always been needed. In response, some countries have developed successful PPP programmes. Merely grasping the concepts of PPP does not do justice to our great responsibility of having an ownership in the country’s future. We already know what we need to do, now is the time to really discover HOW.
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To begin with, data pipeline is the process concerned with the movement of data through an organization. Once data enters into an organization, the stage is referred to as data ingestion. Next in the process is the stage of data transportation from the data ingestion stage to the other stage is known as data collector. Followed by this, the data moves through the processing stage wherein appropriate measures are taken to store it. Data storage stage steps up to the data query stage where it is analyzed through interactive queries. The final stage is concerned with valid presentation of data in different forms of business infographics, such as graphs or statistics.
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