Oct 23, 2020

RPA Q&A with Chris Huff, Chief Strategy Officer, Kofax

intelligent automation
William Smith
3 min
We hear from Chris Huff, Chief Strategy Officer at intelligent automation firm Kofax, on the topic of RPA and its future
We hear from Chris Huff, Chief Strategy Officer at intelligent automation firm Kofax, on the topic of RPA and its future...

What are the main benefits of RPA?

Organisations and individuals adopt RPA for a range of reasons and see varying degrees of success based on how and where they apply the solution. When approaching RPA as a task automation tool within a broader Intelligent Automation platform approach, typically they see value in terms of capacity through the creation of Digital Workers that can transform rules-based repetitive work, operating in stable environments. This capacity can serve as a lever to optimise costs, drive efficiency and generate new revenue. From an individual employee perspective, the value really resides in the increased purpose they feel as a result of the additional space and time RPA frees up to enable them to focus on higher value work, such as providing personalised customer service and crafting strategies to improve their work.

Do you think the COVID-19 pandemic will accelerate or slow the rollout of RPA technology?

As of October 2020, we have several studies from investment banks to industry analysts that have confirmed that a Digital Awakening is occurring. For decades we’ve discussed Digital Transformation, but only few invested in this thesis as a way to drive competitive advantage. Those industries, such as Financial Services that did invest in automation platforms are very clearly doing well in this environment and have no problems digitally connecting and servicing their customers. Industries such as Retail that did not invest in Digital Transformation are facing significant challenges as they attempt to play catch up. IDC cites that over $7 trillion will be spent on Digital Transformation between 2020-2025. This represents a 55% Compound Annual Growth Rate and will serve as a significant tailwind to the adoption of Intelligent Automation platform solutions.

What are the main challenges preventing companies making better use of automation?

There are three primary challenges preventing companies from realising maximum value and returns from their Automation and AI investments. First, Boards and C-level executives need to create a Digital-first culture, and this begins with tone from the top. Second, a federated model should be considered for maximum speed and risk mitigation. A federated model centralises strategy and governance while decentralising operations. This balance creates guardrails for security and network stability while enabling the lines of business to become citizen developers capable of using intuitive ‘drag and drop’ intelligent automation software to rapidly build solutions for their immediate business problems. Third, smart investments need to be made in Platform and Ecosystem software providers that aim to ‘connect your enterprise’ vs ‘become just another IT stack or tool’. Gartner calls this Hyper Automation, which is a platform of automation technologies, such as RPA, Cognitive Capture and Workflow that provide the flexibility to address simple and complex use cases while creating digital workflows to connect disparate systems, people and data.

Share article

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.

Share article