The Future of Software-as-a-Service
With SaaS, we think of a future where, at any given time, a user can access an application from an authorised device anywhere in the world.
Finally, they can throw off hardware and software licensing and maintenance.
Firms employ a number of SaaS tools in the cloud for storing and sharing content or files using tools such as Dropbox or Box, collaboration using tools like Quip or Slack, communication using Salesforce or similar tools, and backup of files and databases on Dropbox or Box, among others. The list grows by the day.
Artificial Intelligence
At this point, AI is a business cannot-live-without technology, with multiple forms of use cases that delight users, automate tasks and extricate relevance. SaaS companies leverage innovations in AI to automate workflows and operations, propelling more innovation and breakneck efficiency in the marketplace and thereby growth. These tools provide super-customisations to maximise product experiences and increase engagement, automate repetitive tasks and build workflows to free up human time to do more creative and complex work, as well as provide real‑time predictive analytics to help anticipate future trends and enable decision-making. At the same time, in the same way that has been true for each of the previous AI hype cycles, many narrow AI systems do not seek to replace human workers but to augment the software users already rely on, and deliver real value to those users. Polymer DLP delivers real-time nudges and in-the-moment suggestions to employees at the point of their actual, policy-violating actions; this approach not only substantially reduces repeat violations but also increases the breadth and depth of employee-developed competency. As a result, security teams can defend data more successfully while cultivating a culture of compliance within their organisations.
Machine Learning
Machine learning is a subfield of artificial intelligence that allows machines to learn with relative autonomy, and gradually improve with practice. It allows machines to learn to recognise patterns and to understand, for instance, when different data sets pertain to the same topic. It also helps machines to identify correlations and trends over time. You’ll find it already in many SaaS products including email solutions, collaboration software such as Slack and Microsoft Teams, virtual assistants like Amazon Alexa or Microsoft Cortana, and retail websites’ product recommendations or cybersecurity software. Appropriate usage of these ML-driven technologies can enhance customers’ experience and reduce churn rates. Another scenario where ML technology has the potential to outperform human effort is in detecting anomalies, for example in fighting fraud or cybersecurity threats. Such technologies need structured data, which can be a bit of a challenge to implement. An experienced saas product development company, believing in human creativity and logic, has the knowledge and skills to assist in overcoming implementation setbacks – and this enables the business to stay competitive by leveraging hyper-personalisation, automation and predictive analytics.
Internet of Things
‘Things’ such as smart watches, cars, home automation, security cameras and air conditioners are being connected via IoT technology so that they can interact – and act – with one another. AI turn use IoT technology to monitor automobile equipment 24/7, ensure enhanced operational execution by detecting and analysing abnormalities from a distance, and provide smart maintenance alerts before critical failures occur. This helps reduce equipment downtime, decrease overall costs upfront, enhance customer satisfaction thanks to improved and faster services, and increase efficiency. IoT is transforming supply chains and warehouse inventories, cutting waste via predictive maintenance, optimising the flow of goods and materials, and allowing businesses to keep people safe by monitoring construction sites for movement in the soil, cracking concrete and unstable foundations. IoT is also helping us build a more sustainable world by reducing energy use, minimising waste and improving the efficiency of everything.
Big Data
It that underlies most of these new software capabilities – but it’s expensive and complicated to work with, calling for specialised hardware and software, and requiring people skilled in how to glean insights from it. Organisations will continue to relocate their premises-based applications and customers’ data into cloud-based systems, thereby achieving cost-savings and service enhancements next year. Further, SaaS will empower firms to improve resource utilisation: predictive maintenance procedures based on data science and analytics can avoid downtime while enhancing the operations of physical assets; simultaneously, they can identify threats such as malware or online fraud that would cost the company money or hurt its reputation.
Cloud Computing
Today, cloud computing – the general term covering all the various online tools hosted in distant data centres and accessible via the web – has become the essential underpinning of business activity, together allowing firms to conduct all their omnichannel customer engagements and to run vast amounts of processing power to feed the increasingly intelligent support AI can provide. By housing SaaS applications on the servers of service providers, hardware to maintain is eliminated, updates and bug fixes can be made more easily, and IT departments can focus on more valuable work. At the same time, employees get access to software without having to go through IT for pre-approval – boosting both productivity and responsiveness. By embracing multicloud computing, organisations can hope to avoid becoming locked into a vendor’s infrastructure infrastructure, while managing the cost of innovative, complementary, best-of-breed offerings from various cloud service providers (CSPs) at scale and with strong performance for their distinctive workloads.
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