Dec 11, 2020

Did we forget to think commercially about AI?

Raoul-Gabriel Urma
5 min
There is very little evidence that many of the organisations that have invested in AI projects to date are actually seeing any measurable benefits
There is very little evidence that many of the organisations that have invested in AI projects to date are actually seeing any measurable benefits...

Everyone is talking about AI. But is AI universally delivering value? We know that the largest tech companies such as Amazon and Facebook, are regularly announcing new AI projects that are “revolutionising” their businesses, while AI is being pitched to add $13 trillion to the world economy – an increase of 16% of global output by 2030 (McKinsey Global Institute, 2018). Yet, there is very little evidence that many of the organisations that have invested in AI projects to date are actually seeing any measurable benefits from them. In fact, there’s a wide spectrum of “AI maturity” as reported in a recent whitepaper conducted by ElementAI. Most organisations get stuck at the exploring and experimenting stage, never able to deploy AI into production systems. 

There are many reasons for this, including technical, as reported in a recent paper I’ve co-authored on the “Challenges in deploying machine learning in Production” with researchers at Cambridge University. However, the most fundamental is that few organisations, to put it bluntly, think about how the project will make, or at the very least, save the organisation money. 

Unlike other serious investment decisions, investing in AI projects seems to be undertaken without a thought for how this will be used commercially. The result is typically a proof of concept that never makes it to deployment in front of real users and stakeholders, which, at best, is a publicity stunt for the corporation. At worst, it is a drain on technology investment that could have been better spent on upskilling the workforce. However, if done properly, AI projects implemented for the right reasons and in the right environment can have a positive impact on a business’ bottom line.  

Driving insights

Using AI to drive actionable insights or predict business trends is the most well-known use of the technology. However, it is still rare for a business to proactively feed these insights into their decision-making process. This means the initial investment into AI is not realising its full commercial potential or delivering hard business benefits.

A recent survey by the research house Gartner, found that until 2022, only 20 percent of analytic insights will ever actually deliver business outcomes. Using AI to process large volumes of data and provide insights can help businesses to guide strategic decisions but if these insights are not used or were not designed to influence decision making, then what is the point? 

You can think of insights generation at the macro ambitious level but also at the micro down-to-earth level. For example, at the macro level, Chevron has announced a new initiative with Microsoft, which will involve building a cloud-based platform that will leverage data analytics to monitor and optimise field performance. At the micro level, a single employee can develop a summarised dashboard to save everyone time and effort by providing access to the information for all - as exemplified at GSK with their Data Analyst apprenticeship scheme. This idea of reducing time to make informed decisions is important because it could allow organisations to take advantage of market price ahead of competition or get recommendations to key stakeholders in a timely manner so it can be acted upon. All of these benefits will have a direct impact on business performance.

Operational efficiency

Operational efficiency is something that all businesses want to achieve. You can think of two categories for driving efficiency:

  • OPEX efficiency: reducing operational expenditure by exposing more efficient ways of operating, reducing costs or improving scale
  • CAPEX efficiency: improving impact of capital projects by exposing strategic efficiency such as number of assets required 

Saving money through automation or by reducing error rates, and increasing productivity at scale are solid ways to ensure that a company survives and thrives, even through testing times. This might be a less sexy use of AI but it is the best way to use it to make the business money or, perhaps more accurately, save money.

According to a UK Government case study for AI in improving operationational efficiency, a UK-based bank used machine learning to reduce the duration of a compliance process by 80%.

Within manufacturing and logistics, you tend to have specialised equipment that often requires planned preemptive servicing. Where historical data is available from vibration sensors or historic maintenance records, predictive maintenance strategies can be developed to keep key machinery in a better state of health and thereby save maintenance costs.

Developing the skills in the workforce to be able to implement these sorts of processes is key to the successful levelling up of UK businesses. We are seeing an increasing number of job postings for these sorts of roles with demand for data scientists and data engineers tripling over the past five years. Upskilling existing workers and educating children in schools including in programming languages like Python will be key to filling this demand from businesses.

Revenue protection

One less understood commercial application of AI, is that it can help protect revenue for the business. This can be achieved in a number of ways including fraud prevention, supporting compliance and churn modelling.

Although the adoption of AI and big data projects is often associated with data privacy concerns, AI can really help businesses to meet regulation and compliance requirements by enabling corporates to better manage data as well as detecting potentially fraudulent activities.

By having algorithms that can automatically clean data sets, reduce the burden of data maintenance, and detect fraud, companies can better protect customer data and other sensitive information against would-be hackers. This could save a business from ruin in a world where a data breach can result in millions of pounds worth of fines that can cripple a business as we saw with the £20 million fine given to British Airways for failing to protect the personal and financial details of more than 400,000 of its customers. AI not only prevents these sorts of breaches but also streamlines the management of the data, saving the business money as well.

We are seeing businesses begin to adopt policies that allow the deployment of AI, but educating key decision makers is still a task that the industry must continue to undertake to drive wider adoption of the technology. By acting on insights that AI can pull from big data sets, improving operational efficiencies and protecting the businesses with fraud prevention, supporting compliance and churn modelling, AI can be used to improve revenues and decrease costs rather than simply being namechecked.

By Dr Raoul-Gabriel Urma, CEO and founder of Cambridge Spark

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 

The benefits of AI in cloud computing


Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 

The drawbacks of AI in cloud computing


Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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