Dec 16, 2020

How can intelligent automation drive digital transformation?

Digital Transformation
Automation
Peter Walker
5 min
Business leaders who best understand their operational challenges and demands should make judgments about where robots will have the greatest impact
Business leaders who best understand their operational challenges and demands should make judgments about where robots will have the greatest impact...

Across every boardroom, the big question is how to simultaneously tackle a perfect storm of extreme global uncertainty and ever-changing stakeholder demands, with increasingly constrained resources.

These pivotal challenges are compounded by the fact that work hasn’t been delivering anywhere near its potential, evidenced by diminishing global productivity. What’s also clear is those organisations still relying on slow, inefficient processes, siloed data and disconnected legacy IT infrastructure are increasingly experiencing negative growth.

Our 2020 global automation report that surveyed 6,700 knowledge workers and senior IT decision makers 92% identified software robots as an important factor in driving digital transformation - with nearly half indicating that it allows them to scale the deployment of other emerging technologies. We’re specifically talking about intelligent automation running smart software robots, trusted by business leaders to perform evermore complex work across front, middle and back office operations - at a speed, accuracy and integrity unmatched by humans.

Enabling digital work transformation 

Let’s be clear. Intelligent automation should be a business-led initiative. While IT and technical leaders are vital to ensuring smooth rollouts and the ongoing running of the platform, it's down to business leaders who best understand their operational challenges and demands, to make judgments about where robots will have the greatest impact on swiftly delivering positive business outcomes. 

Intelligent automation should also enable business users to swiftly respond to market demands, so they don’t want to waste time and effort building robots. It’s far better to swiftly deliver automated work using an intuitive operating system to train and manage ‘pre-built’ robots. We’re talking about simply drawing work process flowcharts that orchestrate an underlying ‘process definition’ language that both robots and humans understand - which also removes the need for coding and any associated risks too.

Ambitious business leaders are going further by embedding highly advanced robots at the core of their transformation strategies. It’s because these robots perform joined up, data-driven, work across multiple operating environments of complex, disjointed, difficult to modify legacy systems and manual workflows. They deliver work without interruption, automatically making adjustments according to obstacles - different screens, layouts or fonts, application versions, system settings, permissions, and even language.

Another major enabling capability is that these robots solve the age old constraints of system interoperability – which has traditionally limited digital transformation aspirations. These robots uniquely achieve this by reading and understanding applications’ screens in the same way humans do. They’re effectively re-purposing the human interface as a machine usable API – and crucially without touching underlying system programming logic.

This ‘universal connectivity’ capability also means that all current and future technologies can be used by these robots – without the need of APIs, or any form of system integration. No legacy systems are ripped out, and no major process change or mass data migration is required.

As well as breathing life into any age of technology, it means that these robots can be continually augmented with the latest cloud, artificial intelligence, machine learning and cognitive capabilities that are simply ‘dragged and dropped’ into newly designed business process flows, to deliver evermore complex work.

Ultimately, digital transformation that would traditionally be cost and resource prohibitive becomes feasible. In fact, work is now being achieved in months that would take IT programs and vast teams of people, years to complete.

What enhanced working looks like

We’re seeing organisations not only experiencing millions of multi-currency savings, new levels of operational agility and efficiencies, but improved business performance, delivering more value-generating roles – with the saving and re-utilisation of millions of hours to fulfil ever greater demand. Also being reported, is faster expansion of new services being delivered at unprecedented speed - even throughout COVID-19 lock-down, and even via remote working too.

For example, a pharmaceutical multinational combines robots, optical character recognition, analytics and data visualisation tools to greatly reduce clinical cycle times, product labelling and shipment reconciliation – so patients benefit from medicines much faster. We’ve seen a major insurance company use machine learning and visual processing in tandem with robots to reduce the time to assess an accident claim from 56 minutes to a staggering five seconds. The early pilot alone was saving five million dollars a year and freeing up 39,000 hours of work time.

To compound even more value, all automated work is being collaboratively multiplied by organisations across their businesses. For example, there’s a growing community of NHS Trusts that are further benefiting by uniquely sharing their tried and tested work automations via a newly formed NHS Digital Exchange.

Crucially, intelligent automation done well frees people to perform more valuable work that’s best suited to them. We’re talking about training, managing and interacting with robots, making critical judgements, applying insights from process automation data to continually improve work, spending more time with customers, serving them faster, enhancing their experiences and innovating to drive growth.

Final thoughts

The big challenge for organisations is to adopt intelligent automation strategically - to make the enterprise smarter, more agile or efficient - rather than focusing on short-term tactical savings. Key is securing ‘C’-suite sponsorship and using that powerful voice to pave the way for wide-scale adoption. A strategic vision is required too that sees intelligent automation as a vital tool for delivering these broader goals. This also means taking time to evangelise the benefits right across the organisation to educate potential internal customers to turn them into positive proponents and adopters of the technology.  

By Peter Walker, CTO EMEA, Blue Prism 

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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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