Jun 5, 2021

Breaking down the barriers to AI adoption in business

AI
Technology
Data
ML
Jon Payne
5 min
In a world where consumers demand increasing personalisation of services, artificial intelligence (AI) holds enormous potential for businesses to deliver

In a world where consumers demand increasing personalisation of services, artificial intelligence (AI) holds enormous potential for businesses to deliver. 

In most cases this is about using AI to make it easier for customers to get the goods and services they want. Sounds simple enough, but this comes at a time when customer expectations have changed dramatically, not just because of the pandemic. Take the travel market, for example where online platforms allow consumers to build their perfect get-away by sourcing different options for flights, hotels, car rentals and so on, threatening the traditional package holiday. People now have personalised options they can create for themselves on-demand with the help of streamlined online tools. 

In the context of all these different expectations, companies that offer personalisation will be the winners, while competitors that lack sufficient agility will fall by the wayside as they become less meaningful to consumers. To be competitive, more organisations should be providing a customer experience that steps beyond what is commonplace now, into a more intelligent understanding of people’s needs and how to meet them. But this can only be founded on good data. 

This requires more than the simple correlative recommendations found on retailers’ websites. It requires the application of AI to enable far greater personalisation and more persuasive targeting with offers, recommendations, guidance and advice. The challenge is to provide information, goods and services to customers based on their personal preferences but in a way that is not too intrusive or insistent. And of course, it must be accurate and relevant. 

In financial services, there is much debate about the effectiveness or appropriateness of using AI and machine learning (ML) models to make lending decisions. The fear is that they could be discriminatory or increase risk. But in China, Ant Group used AI to make robust lending decisions that processed applications for small loans very quickly and effectively, giving millions of people access to finance who would otherwise have struggled to obtain it. Other institutions can significantly reduce the risks in AI decision-making in similar customer-facing applications through governance and feedback that constantly improve their AI and ML models.

The need for AI education and democratisation  

However, while the majority of businesses know they should be using AI to provide better and more personalised services, they find it very difficult to put it into practice. Not only do they lack the understanding of data science to train and develop AI models, many are also behind the curve when it comes to data collection. While organisations tend to be confident about the data they collect, most do not collect enough. Others may not understand what kind of data they should be collecting. 

As well as this gap in understanding of the technology, many businesses are unaware of where they should be applying AI to gain broader benefits. Cultural barriers also remain high in some businesses where senior teams have yet to grasp just what AI will deliver. Those closer to the shopfloor in some industries may want change but fear the implementation of AI is full of risks, with inertia also being commonplace. So, businesses are left pondering how to adopt the right solutions.

In small or medium-sized businesses, leaders often see no role for AI in what they do, and collect very little data. They may not see how predictive capabilities would help them prepare better for seasonal variations, or for events and trends that trigger surges or troughs in supply and demand. 

Broad-scale adoption of AI is often very difficult for people to understand, especially if they work outside the digital technology sector. Smaller businesses need access to more pre-built, off-the-shelf AI solutions that make it easier for them to implement. To democratise AI, software and solutions providers need to build in capabilities that enable more businesses to realise its significant benefits in their day-to-day operations. 

Overcoming the hurdle of access to data 

Data remains one of the biggest barriers to AI adoption. An AI solution is only as good as its training model, but many organisations still do not know how to obtain the quality and quantities of data required to feed such models. However, some organisations are now at the pivotal stage of this journey, on the brink of understanding how they can achieve the vital next step. To help them and others struggling to harness their data, they can deploy a smart data fabric, a new architectural approach.  

Smart data fabrics interweave data from multiple sources and different formats, using a multi-tier approach that cleans data and employs an integration layer to make it usable. The fabric does this while leaving the data where it is, with lineage tracked for every item, enabling users to see where it has come from. Machine learning incorporated in the fabric enables dynamic queries and data analytics, along with API management capabilities. Organisations find it easier to gain critical insights from their data which they can deploy for a wide range of purposes including new services and products. By allowing existing applications and data to remain in place, the smart data fabric approach enables organisations to gain business value from all their data sources quickly and flexibly so they can power their business initiatives.

Once businesses have implemented a smart data fabric, they have clean, dependable data they can use for more advanced applications that meet the more demanding expectations of today’s customers. Applications will use the data to adapt services to each customer’s preferences, history and potential, optimising interactions and streamlining processes.

Addressing the data problem will also transform decision-making, moving away from reliance on experience and assumptions. As well as using the data inside their heads, senior leaders will have access to hard evidence and new predictive capabilities based on reliable data. Gut feel has a lot going for it, but AI provides recommendations and predictions based on data that everyone can access.

Once organisations have changed how they approach and manage their data, even small AI implementations will help overcome internal cultural barriers. Everyone from those in the boardroom to employees on the frontline will see how AI applications enable them and the business to meet the heightened expectations of their markets and adapt rapidly and more profitably to sudden shifts in demand.

 

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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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