Nicholas Ismail

About Me

Nicholas Ismail is Editor at Information Age.  He writes original articles for the magazine and website, interviewing business leaders both on and off-camera while providing analysis for IT and business leaders on the latest technology trends disrupting the industry.

A guide to artificial intelligence in enterprise: Is it right for your business?

Board members and decision-makers are increasingly aware of the benefits of AI and automation, but the question should always remain: Is it right for my business? How does it solve a problem? With the general rise of this technology into business operations also comes challenges, dangers and potential risks to the human workforce. This feature will examine all these aspects and hope to give an overall look at AI and automation in the enterprise.

AI predictions: how AI is transforming five key industries

Over the next three years, embedded artificial intelligence (AI) will dramatically reshape the customer experience across nearly all industries, with most major businesses providing some type of AI-centred customer experience. AI is a way of providing new, novel experiences that are useful and helpful to people. While every industry will be affected by AI, specifically this technology will drive the greatest transformation over the next three years in healthcare; travel, transportation & hospitality; and manufacturing, banking, and insurance. 

The CTO view on AI: business critical or hype monster?

It’s clear the umbrella of artificial intelligence has advanced in areas like machine learning, which is already impacting areas such as image recognition, one of the biggest current viable use cases of AI-based technology. Any CTO looking at the technology will have to define a relatively narrow use case — ‘what problem will AI help me solve’, they should ask?, and  focus on making sure there is enough data, and the right kind of data, to support that use case.

The use of AI in robotics and hardware - what CTOs need to know: integration, job loss and privacy concerns, and investment

In today’s ultra competitive environment, every business must implement advanced technologies to stay ahead of disruption. If your business is not part of that journey, if you don’t innovate, it’s likely the business will go bust. The use of AI in robotics and hardware is something that every CTO, especially in industries like manufacturing, must be exploring. Integrating software with hardware is necessary to stay ahead of the curve. Integrating AI (software) into robotics, or hardware, is important, because of how the architecture of computers works.

The blockchain guide for CTOs and tech leaders: suitability, adoption, regulation, hype and future predictions

CTOs and tech leaders need a blockchain guide. From what is the blockchain, to adoption issues and advice surrounding regulation, compliance and more, blockchain guide is an invaluable tool for CTOs and other tech leaders looking at blockchain for their business. When blockchain adoption is no longer an issue and the technology becomes fully mature, various forms of new ideas and business models can be instantly generated, tested and realised with rapid adjustment via this autonomous DMS structure, where collaboration can be automated. The possibility is infinite with blockchain!

Best practice tips for adopting blockchain technology

Adopting blockchain technology will become a growing priority for CTOs, and equivalent positions, in end user organisations, ranging from property to retail. Adopting blockchain, as with any new technology, will be a challenge. There are numerous suitability and adoption issues. One commonly universal benefit that blockchain technology can bring is the ability to incorporate an efficient financial inclusion mechanism into any business or utility model. Here are some of best practices towards achieving wider adoption.

How to secure, manage and monitor edge devices

As more edge devices proliferate different enterprise and IoT networks, it becomes increasingly important to secure and manage all of those devices. Without the ability to secure and manage edge devices easily, edge computing won’t be accessible to many of the industrial applications that it is most beneficial to. How can organisations secure their edge devices, which allow enterprises to take steps towards the real-time and proactive management of applications? This is one of three challenges when it comes to edge devices; do I trust it/is it secure? Can I manage it? Can I monitor it?

Achieving a data-centric approach to security requires homomorphic encryption

Data breaches are becoming more frequent and damaging. This failure to solve the growing security crisis is crippling the confidence of large enterprises in their ambition to move to the cloud, which can be a risky, but necessary venture. Why is it necessary? The legacy implications of not moving to the cloud are affecting data. A data-centric approach to security and homomorphic encryption is required to solve this problem and give companies the confidence to move to the cloud.

How do you solve a problem like mass IoT connectivity?

IoT could, become a great opportunity to align mass market with the best interest of a sustainable future. Before tackling the problem of IoT connectivity, it’s important to understand the mass IoT market: what does it compose of? Will the IoT market largely be made of industrial or consumer connections, or both? What organisations are the providers of mass IoT connectivity? Is it systems integrators, IoT specialists, communications service providers, telecoms operators, IT vendors or others?

Competitive advantage: how to put machine learning models into production

Those companies that can put machine learning models into production, on a large scale, first, will gain a huge advantage over their competitors and billions in potential revenue. But, there is a huge issue with the usability of machine learning — there is a significant challenge around putting machine learning models into production at scale. Organisations can create incredibly complex machine learning models, but it’s problematic to take huge datasets, apply them to different iterations of ML models and then deploy those successful iterations into production.

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