Year after year, digital technologies are reshaping business models across diverse industries.
Enterprises hoping to capitalize on the recent advances in AI new trends are considering the following:
- Increasing amounts of raw data
- Transforming this raw data into valuable knowledge
- Investing in cloud technology that powers data access and transformation
The crude resource known as ‘big data’ first appears challenging: disorganized, obscure, and… well, overwhelmingly ‘big’. However, firms that successfully convert this resource into meaningful business insights, often find that it’s worth the trouble and more. The refined outcome gives way to the optimization of business processes through automation that yields time-saving adjustments to human-driven processes. It elongates knowledge lifecycles and makes people better at their jobs. This gives people access to the answers they need from data that supports business objectives.
Automation can offer these benefits today, but most notably has been seen with a direct impact in areas like security and IT incident response, for which speed and consistency are paramount. With these tasks streamlined via automation, employees can make measurable leaps forward in improving the direct line of business.
1.) The Paradox of AI Insights
When tackling more advanced issues, such as the betterment of human decision-making, there is much more required of AI-driven solutions.
For one, those harnessing artificial intelligence must receive its insightful gifts with diligence. The knowledge gained from AI technology is only valuable with a complete understanding of how the information has been derived. In other words, the AI must be able to rationalize its findings in a way that is logically sound to humans. This is a tall task; artificial intelligence is valued for the edge it affords beyond human intuition, yet its insights must be made intuitive to us.
AI developer tools and services offered by cloud hyperscalers have reached the mainstream and will continue to foster further growth of the AI ecosystem. But with any budding technology, AI adoption comes with the onus of integrity: AI solutions on the market that aren’t market-ready may predominantly impede the progress of the ecosystem. How might cloud technology play a role in the “quality assurance” of new AI developments?
2.) Cloud Enabled Automation
Interest in AI’s competitive advantages goes far beyond just the deep realm of tech. From startups to hyperscalers, firms have begun using the world’s data resources to create new revenue drivers through AI and machine learning. AI requires enormous computing power, making the public cloud -- an interminable resource of data processing power -- an ideal place to start. Major players (e.g. Amazon, Microsoft, Google, IBM) aim to create innovative AI applications for other businesses, thus driving increased traffic through their public cloud ecosystems. Their whopping investment in cloud is also a bet on the cloud’s intricate connection to AI. Take, for example, IBM’s use of Watson’s natural-language searches to develop cognitive retail as well as DNA analysis in cancer patients.
For smaller players, cutting-edge services involving big data and AI are first available by way of cloud. Thus, these cloud services effectively set industry standards for the services they provide. With far-reaching standards comes the opportunity for widespread automation, and ultimately industrial-grade service delivery. It’s no wonder that the main hyperscalers advertise cloud services addressing management and automation; assisted by AI, cloud technology offers fresh opportunities to expedite service delivery and quicken business cycles. Only the cloud boasts the scalability to let companies provide such data-intensive services to multiple clients at an affordable cost.
What’s more, most companies can only access the resource of data by way of cloud. The crudeness of this data is also being addressed by cloud solutions. When dealing with machines, the rule is “garbage in, garbage out.” The outcomes of AI are only as good as the quality of data inputted. Solutions like Gemini Data allow businesses to maintain the quality of their data from input to insight. Given the demands of AI, its consumers must have a robust and optimized cloud strategy.
Cloud technology’s relationship with AI services is symbiotic; the growth of cloud technology is fed by AI’s importance to 21st century business, much as the adoption of AI technology is enabled and accelerated by the cloud.
Provided adequate supply in talent -- data scientists, software developers, information technology operations -- the paired technologies of Cloud and AI stand to flourish together. The dearth of this supply is one of the biggest factors throttling the growth of the AI domain: the shortage of experts with the skills to implement enterprise solutions and the means to automate insights. This means that while businesses may know how they want to apply AI, they will need to leverage automation to build an application that realizes their vision.
3.) Driving AI with AI
The key to true AI-driven value is better AI. Specifically, the wave of innovation on AI-based platforms indicates an increasing demand for the capabilities of cognitive technologies without the need for in-house expertise. Automating data infrastructure, regardless of what platform you use, provides speed, efficiency, and cost reduction for your data analysis. Put differently, not enough people have the skills to use AI, but the platform-based offerings of the cloud can go a long way to address this issue.
In the end, AI-driven Cloud enterprises have the ability to scale and grow with the rapidly evolving market of digital technologies.