The future gold standard: AI-enhanced digital transformation
If you haven’t had your head buried under a rock in recent years, you’ll already know that the buzz-term ‘digital transformation’ is on the tip of every industry-leading executive’s tongue these days. It has become a crucial factor in business and, arguably, the most important driver in defining how companies can competitively deliver value to their customers in an ever-evolving, oversaturated market.
Digital transformation represents a conglomerate of digital technologies that can be leveraged to create new and enhance existing processes to better the end-user experience and business processes ─ subsequently meeting the changing demands and needs of a global consumer base.
The unofficial chief of this conglomerate is artificial intelligence (AI) ─ a technology that is starting to underpin the successful development and rollout of all others.
Why Is AI Chief?
AI has been helping companies become more adaptive, flexible, and innovative than ever before. Interestingly, even though AI’s power is becoming one of the key enablers of digital transformation across a myriad of industry, including supply chain, manufacturing, and healthcare, it’s widely assumed that AI, like every other grandiose, over-popularised technology, is a futuristic or visionary technology.
However, the fact of the matter, as Kim del Fierro, Vice President of Marketing at Aisera puts it, is that “AI is already being cost-effectively deployed in numerous companies, accelerating productivity and competitiveness, whilst helping speed [up] digital transformation.”
On the topic, del Fierro adds that “More than a concept, digital transformation is a dynamic movement; it attracts organisations looking to review processes, innovate and gain competitiveness with the help of technology. Artificial intelligence is a critical strategic factor for businesses to expand their impact.
“Aside from boosting productivity, these technologies are essential because they enable better use of the data collected by a company. With useful data, businesses can expand, improve their current products and services, and create innovative strategies,” del Fierro adds.
So let’s take a look at a few ways that AI can improve your company’s digital transformation efforts and success rate.
A Realistic Image of Customers
According to Louis Columbus, former Forbes contributor, “AI is helping to more precisely define customers' preferences and needs, leading to more accurate personas that guide digital transformation projects from the very beginning. Organisations that are the most successful with digital transformation initiatives can see improvements in customer loyalty rates and customer satisfaction based on a more well-defined persona quickly. Using AI to better understand customers’ personas need to be the foundation of any digital transformation initiative. The most advanced uses of AI for persona development combine brand, event and product preferences, location data, content viewed, transaction histories, and, most of all, channel and communication preferences.”
Dynamic Data Analytics
“AI-based algorithms are making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition, rely on predictive analytics, including machine learning, to predict the probability a given customer will act on a bundling or pricing offer, email campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn,” Columbus added.
In essence, through the collection and analysis of useful data by AI-systems, companies can launch strategies that truly capitalise on the harvest. In today’s internet-powered world, consumers are looking for personalisation wherever they look ─ ŧailored ads, products, and offers to meet their buying habits and daily needs.
AI processes thousands of data points to gather the necessary insights and identify buyer trends on-the-go, allowing companies to pre-empt consumer needs rather than reacting to a market already changed. A human cannot do that alone, and companies of yesteryear couldn’t provide such a bespoke, fitted experience for their customers ─ only AI and digital transformation can unlock this potential.
Growth and Profitability
It’s a well-known fact that these days, AI helps businesses profit through their digital transformation by accelerating growth and innovation, increasing operational efficiency, and mitigating risks that were previously unpredictable and unavoidable. According to Accenture, AI can actually increase a company's profitability by an ─ and it’ll boost additional revenue by $14trn.
We could go on and on about the advantages and potential outcomes of AI-enhanced digital transformation, but we’d probably run out of space. So I’ll just leave you with one final message: digital transformation initiatives that incorporate artificial intelligence ─ and machine learning ─ as key driving forces are both redefining and revolutionising user experience as we know it. If you and your company do not jump onto these developments with haste, you’ll likely find yourselves falling behind the competition and gradually losing your market share. That’s not an outcome that anybody ─ other than the competitors ─ wants to see.
The advantages and disadvantages of AI in cloud computing
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
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.
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.
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.
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
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.
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.