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With more and more integration of Cloud services, SaaS models continue to operate as the most popular service-based approach. However, with time, SaaS is likely to go through certain robust changes that will peak its popularity in years to come.
It’s all up in the cloud…
Yes, Cloud integrations were booming before the global business scenario thawed down because of the pandemic. But, 2020 gave cloud usage momentum like no other. With more and more businesses moving their on-premise businesses to the cloud space, the demand for service-based software or SaaS, to be precise, has shot up monumentally.
Also, we shouldn’t forget that a sizeable contributing factor behind this
demand has been the ‘new normal’ business processes and workplace conditions.
SaaS, or Software as a Service, is well into its middle age now. But going by the Gartner report, it still hasn’t lost its relevance. Their recent report says, SaaS still dominates one of the biggest market segments amongst cloud services and is expected to grow at least $138.2 billion in 2022. In terms of functionalities, versatility, and accessibility, SaaS remains a viable option for enterprises in a vulnerable business environment.
One of the core benefits of adopting SaaS is that it integrates a high level of agility and cost-optimization for cloud-based projects. The future of SaaS
looks nothing but rosy due to the massive progress made by the IT world in terms of AI and business intelligence. SaaS BI tools are increasingly finding acceptance in several untouched markets and streamlining business models to maximize efficiency.
Enterprises across the world are looking at new-age data discovery tools to boost their productivity and given the path that SaaS is shaping, it is sure to grow from strength to strength. Our observation on the growth of SaaS says that the following trends will rise to popularity.
Automation through AI has generated some game-changing waves with a predictive market value of $733.7 billion by 2027. By automating repetitive tasks and optimizing human capabilities, AI is all set to drive ultra-responsiveness between business and customers.
Modern software providers are banking on AI-supported data alerts for pattern recognition and detection of an anomaly, thus, enabling complete control over business processes. AI capabilities when paired with SaaS, boost its core characteristics. It enables rapid customization through NLP (Natural Language Processing) to process voice control, speech patterns, and has immense opportunities in Customer Service capabilities. It also improves security parameters through quick identification and troubleshooting through in-built self-recovery. Additionally, it augments human capabilities by speeding up overall responsiveness through quick forecasts.
The end goal for Horizontal SaaS was focused on clients, Vertical SaaS digs deep into the possibilities of targeting clients through customization within specific supply chains and industries. Enterprises are keenly focused to brand themselves as specialists rather than ‘jack of all trades’ and innovations in the field of Vertical SaaS are enabling them to refine the options for customization through industry-specific and cost-effective solutions.
It ropes in a host of data governance procedures by seamlessly integrating industry-specific compliances for better transparency; the pre-determined KPIs and metrics in Vertical SaaS provides analytics that businesses can utilize for accessing long-term projects.
2021 is going to be a year of acceptance for white labeling as startups are sure to find it useful for gaining market shares with fewer financials. Since white labeling allows an enterprise to sell a fully developed and optimized platform to another company, startups will benefit by not having to build solutions from the scratch.
On the other hand, software companies that sell white-label platforms capitalize by generating new ROI streams and by marketing themselves as the actual developers of the platform.
2021 is that year when markets are expected to get saturated for a lot of industries; SaaS is expected to reach a thaw in spite of its brilliant numbers. The cutthroat competition is likely to give rise to Micro SaaS businesses run by exceptionally small teams to provide an extension of services to an existing platform to create an enhanced value for the SaaS product.
This niche market for miniature extension services is likely to be very specialized to an industry or userbase and is likely to operate without external funding.
The explosion in numbers in terms of adopting SaaS solutions has left a gap between the need to and competencies to handle the integration of these solutions into an existing business system. Not every business wants to move all of their on-premise operations to the cloud, and the ones that want to harmonize the SaaS solutions with their existing solutions need smart APIs that deal efficiently with managing legacy systems and provide security against data breaches.
Machine Learning (ML), a form of AI, has empowered SaaS with automated onboarding and ultra-responsiveness with the help of AI-powered chatbots that have generated coming-off age customer service apps and reports. The new wave of ML is focused on minimizing significant internal operations through innovations.
The ML-based SaaS models are likely to improve internal collaborations, analyze data and insights in a better way, and train software solutions to learn from interactions for gaining greater levels of intelligence and efficiency.
With gigantic scope for digital transformations worldwide, businesses are looking forward to data streamlining processes in order to delve deeper for customer insights. Data Analytics is gradually becoming an important component in service-based software businesses and data-driven decisions are emerging as a necessity for futuristic businesses.
Centralized Analytics is likely to uncover hidden insights like performance dashboards. The SaaS models have an in-built centralized nature that enables data access from anywhere in the world this makes future business processes all the more transparent.
SaaS models have evolved to ‘low code’ structures that enable startups to bring alive their SaaS solutions with minimal technical expertise. The low-code structure won’t eliminate the need for developers but will simplify the integration of aligned technology so that the technical brains can actually invest their time to drive innovation.
The future of SaaS applied business solutions is now entirely driven by data-focused approaches. The more enterprises integrate business intelligence and smart data into developing service-based solutions, the more will the cloud pace see futuristic and practical cloud solutions.
Apart from being extremely convenient, SaaS solutions are still affordable across industries. The artificial intelligence plugin just makes them more invincible with building anticipated solutions backed by intelligent data.
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