Dec 17, 2020

Overcoming the challenge of AI

Machine Learning
Ursula Hardy
4 min
 AI will streamline complex tasks, identify insights that can truly make a difference in the world and make incredible leaps in our levels of productivity
AI will streamline complex tasks, identify insights that can truly make a difference in the world and make incredible leaps in our levels of productivi...

When it comes to AI, it’s easy to be daunted by the complexity, the mystery and the possibilities. The reality is that AI will streamline complex tasks, identify insights that can truly make a difference in the world and make incredible leaps in our levels of productivity. In the US, 68% of businesses increased investment in AI technologies during the Covid pandemic, whilst one in four CTOs thinks that AI and machine learning will have the greatest impact on global recovery. 

But how do businesses begin on the path towards adopting AI when it is such a complex subject? 

The foundation of AI

AI depends on a large, healthy set of data. The only way for accurate patterns and insights to be uncovered is for AI algorithms to analyse carefully collected data that reflects reality, otherwise the output will not be reliable. It is from that carefully collected data that valid interpretations can be produced. For example, Football coaches are now adapting AI to analyse hours of video footage from past games to provide insights on how to train their teams to compete better. This is only possible with the data collection practice in place. 

In order for businesses to incorporate a similar AI strategy, brands need to first invest in a strong data strategy - data that is captured with intent, consistency and transparency is key. Football clubs have a policy of always recording each match, and the process has been for analysts and coaches to spend time watching and taking note of observed patterns and manoeuvres. Now, instead of only relying on human eyes diligently reviewing, football clubs have the additional power of AI to help identify patterns that may have previously been missed. 

Data strategy fundamentals

Many companies do not have the luxury to begin anew. A subset of these companies find themselves with a vast amount of data, but are now catching up to understand what their data can do for them. It is a common pain point for existing data collection to be done without a purpose, or for the data itself to be inconsistent in format, or even that data is hidden within the business across various teams and channels. 

In order to take advantage of AI, your data strategy needs to address a few fundamental principles. 

  1. Take a step back: what are you trying to achieve with your data collection? Are you tracking metrics across KPIs? Are you gathering data to understand user behaviour? Be aware of the intent behind data capture and categorise accordingly. 
  2. What type of data are you collecting? Is this data in the correct format?
  3. Be sure that data captured across the business is not siloed. Valuable insights can be found across areas within the business. Transparency is important.

Surfacing the data so that it is collected with intent, in a consistent format for analysis and made transparent across the organisation is fundamental. Utilities companies are incorporating AI driven chat bots but in order for these chatbots to work,  they must be able to access the data stored in different areas of the business such as account information, payment history, existing charges, etc. Imagine if a customer were trying to access their account balance, but the chatbot didn’t know they changed addresses? The resulting confusion creates more work and poor results. Basic data strategies need to be in place to solve these day-to-day problems. 

Don’t start from scratch

Rather than starting from a blank slate when tackling how to implement a healthy practice in data collection, or retroactively taking advantage of the vast amounts of collected legacy data for value, there are solutions to help guide the process. Finding the right Digital Experience Platform (DXP) means that businesses must think critically about how their overall business strategy fits with their digital strategy, before finally settling and determining their data strategy. In this practice, the complex questions of determining the best solution will come up naturally if done correctly. Furthermore, the additional burden of keeping up with sporadic digital policy regulation changes, can be handled through the use of the DXP - saving organisations from collective headaches of developers to the C-suite. 

A well established DXP will establish a strong connection point with customers, as well as enforce a deliberate data collection policy - as a result, producing a healthy data set that accurately represents reality. With this foundational step established, organisations are poised to integrate AI and can begin to evolve their processes. 

By Ursula Hardy, Platforms Solution Manager at Kagool

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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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