Is Your Business Ready To Embrace Artificial Intelligence?

Uploaded on 2023-09-25 in TECHNOLOGY-Key Areas-Artificial Intelligence, FREE TO VIEW

There’s so much hype around the future of AI, but what are the practicalities of making use of AI in your business to gain competitive advantage? Automation is already embedded in many apps and tools that businesses use every day; from the way it can track your location anywhere in the world to the way that it automates the ads that are served every day.

But that’s just one piece of the puzzle to win market share by innovating products and services. So, what is AI and how soon will it make things different?

AI – An Overview

Machine learning is where computer systems learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data. AI goes further and makes recommendations on the data it gathers from multiple sources so that it forges an opinion like a decision engine. For example, Microsoft’s Copilot will make recommendations as to what actions you need to take having heard your meeting dialogue.

The applications are endless and key for businesses is discerning in what ways they can use AI to improve efficiencies in their own organisations.

What Is The Current Appetite For AI Adoption?

AI is where cloud was 15 years ago. It’s a buying market; excitement around AI is palpable, but no one really understands it enough yet to know how we will use it for maximum benefit. Even investment companies are now looking at AI strategies when valuing a business, so there is no ignoring it for a growing company.

Right now, the quest for AI is largely being driven by marketing departments wanting to create a better customer experience; the operations teams who want to improve efficiencies across the business, and the business analysts who want AI to extract new insight and information from business data.

Many FTSE enterprises already use automation and elements of AI and machine learning and have the budgets to test and learn at a greater scale and pace. These businesses depend on staying ahead of the curve, after all, AI is critical to staying in the competition. Yet, mid-tier companies are currently at a stage where there remains confusion as to what they need. This group is evaluating the benefits AI will bring versus the cost of implementing it.

Not only is this evaluation critical for a business, but it also depends on the maturity of its supply chain and whether its customers are ready to interact with AI. These varying factors mean that businesses will be at different stages of their AI development. Assessing where in the business AI will bring real benefit rather than be a nice to have is imperative. We see a need for a rapid rise in AI consulting services to guide businesses on their AI strategy and how they deliver on that.

Key Challenges Businesses Face Around AI

There is no doubt that AI is set to enhance user experience and contribute to improved, deeper business insights. Nevertheless, a business is still answerable to industry regulations and emerging obligations, like ESG reporting. Not only do AI initiatives need to be cost-effective for the business, they also need to be safe.

We all need to respond to this challenge. AI presents itself in a different way – it’s an entirely new area that requires continuous development and a new set of rules.

As AI develops, we need to assess what new policies and considerations are required. For example, does the AI pose Intellectual Property (IP) risks or privacy concerns? As strategies develop, businesses must also remain committed to environmental and regulatory considerations as well as the security of the business, staff and customers.

For businesses taking their first tentative steps towards their own AI development, the key will be to formulate a strategy that allows it, in bite-sized chunks, to test and deliver and then enhance as it develops.

Preparing Your Infrastructure To Support AI Applications & Tools

For some businesses, AI will be transformative. Others may prefer to start small and scale. No matter the AI strategy, the foremost concern is making sure that your infrastructure can handle the compute power needed to drive and deliver an AI system and its analytics demands whilst ensuring that the platform can scale as it continually evolves.

If we take a customer experience initiative for example, it’s business critical that the system can’t go down. Therefore, you will need to make sure that the platform is fit-for-purpose before it moves into production. This largely depends on reliability, availability, security and performance.

If your infrastructure is currently hosted in a public cloud, such as Microsoft Azure, you should assess how cost-effective this will be to host an application that will rapidly drive up your number of licenses. You are likely to find that your costs will quickly spiral out of control.

Servers need to withstand the analytics demands of AI strategies. For example, IBM Power Systems are designed to sustain the most demanding, data-intensive computing. With the rapid pace of AI, the newest Power10 server has never been more relevant to business needs due to its price, performance, security, scalability and, crucially, its power consumption footprint helps to meet the ESG agenda.

Enabling Faster AI Proposition Development

The next avenue for businesses on the starting blocks of AI is to consider the apps available that will enhance AI development. There are various AIOps (Artificial Intelligence for IT Operations) solutions that will take an organisation along this journey and choosing the right one for your business will help you to get there faster.

The growth of low-code platforms has thrived due to a lack of skilled developers and a need to improve turnaround time for developing projects to solve business problems. For example, IBM’s Watsonx is an accelerator of AI, allowing a business to fine-tune AI to the enterprise's unique data and domain knowledge. Not only does it ensure that the AI is specific to the organisation’s needs, but it also essentially makes the coding less involved for the business, helping to create a competitive advantage with little to no knowledge of programming language.

To evolve an AI solution from here, you then need to understand which data supports the business problem you are trying to solve.

This is the time to retrieve and prepare the data into a format that can be used for model development, measurement, and training a machine learning model. Most AI models require data to be combined and ‘denormalised’ into one large analytical record.

Everything is on the table for discussion around what AI can do for businesses. Yet, without assessing your infrastructure and compute needs, and ensuring your data is accessible, you won’t be ready to take advantage of the latest AI technology for your business.

Andy Dunn is CRO at CSI Ltd Image: Mariia Shalabaieva

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