Dec 16, 2020

Is now the time for companies to invest in automation?

Automation
CRM
AI
Nicholas Blake-Steele
3 min
An obvious benefit of investing in automation is its ‘always on’ nature. Programmes don’t take breaks, even during public holidays.
An obvious benefit of investing in automation is its ‘always on’ nature. Programmes don’t take breaks, even during public holidays...

Automation of systems plays to the strengths of using computers. They can tackle mundane or routine tasks efficiently, without complaint, and without the avoidable mistakes that can occur when humans repeatedly perform manual tasks. Embracing automation ensures your engineers and developers can focus instead of solving problems and adding value to clients. 

When should you be automating? 

If you’re repeating something more than two or three times and the task is tightly defined (e.g. the data fields never change), it's time to automate, especially if the tasks are very straightforward (such as adding data to a database).  

Automation is often cheaper than you think, and the efficiencies it delivers can vastly outweigh the cost. Plus if investing in automation saves someone 1-2 hours a day or week carrying out a task, that time can be reinvested in solving other problems and delivering more work. 

The pros of investing in automation  

An obvious benefit of investing in automation is its ‘always on’ nature. Programs don’t take breaks, even during public holidays. 

Automating can differentiate your services, meaning you can add value by solving clients' challenges based on your unique knowledge and experience rather than performing mundane tasks that anyone could do. Automating routine work also gives you the space and time to tackle the harder problems – if you play to the strengths of the computer you can do 80% of the work with 20% of the effort.  

The cons of investing in automation  

Automation requires detailed planning to ensure the solution integrates with existing systems and is futureproofed for any future developments. This will take time from experts. Depending on your existing systems, there may also be some software to purchase and bespoke programming to complete. 

If implementation is rushed and manual processes pushed through first, it could take longer to unpick things and automate at a later date. Finally, automation does not mean automatic forever - maintenance will be needed from time to time. Context changes, systems change, data changes, clocks change - all of this needs to be managed and monitored. 

So, spend some time thinking about the data you’re handling. How manual is it? Are you repeatedly discovering that data is missing when someone is on annual leave and only they know how to do it? Do you find that occasionally data is missing because someone copied and pasted into the wrong column? Are you frustrated that some data is in one system but not another, meaning you can never get a full picture? 

If your answer to any of these is ‘yes’, and you want to streamline your customer data workflows, get campaigns, reports and insights delivered faster, it could be time to get automating.

By Nicholas Blake-Steele, Technology Director at CRM agency Armadillo

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
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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