Integration of artificial intelligence (AI) and the cloud have transformed business models by enabling broad access to state-of-the-art technologies.
IT leaders are refining strategies to leverage these capabilities to enhance efficiency, simplify processes, and customize products and services to individual preferences. How businesses use AI and the cloud impacts everything from cybersecurity to bridging the technology gap across different generations in the workforce.
Here are six industry trends to watch.
Data Privacy and Security
Global cybersecurity attacks are rising, so data privacy and security are major concerns for executives worldwide.
The General Data Protection Regulation (GDPR) provides a regulatory framework to enforce data protection. Collecting and processing data correctly is crucial to your organization’s future and helps you navigate today’s regulatory environment.
Additionally, safeguarding against cybersecurity threats is essential to prevent data breaches and ransomware attacks. Companies can use cybersecurity as a service (CaaS) to access a suite of services without having to invest in hardware and software infrastructure.
Digital Proficiencies and Flexibility
The hybrid work model is here to stay, so the use of digital tools is necessary to collaborate more efficiently with employees, customers, and their ecosystems. The goal is to drive efficient communication methods.
Flexibility matters, and the adoption of and ongoing support for a hybrid work model reflects a company’s ability to accommodate diverse work preferences. This has evolved significantly in response to the changes brought about by the COVID-19 pandemic.
Allowing employees to choose how they work is an acknowledgment of individual needs, which leads to fostering a satisfied and engaging workforce.
Gen Z and the Generation Gap
Different generations consume data and insights differently, so managing the generational gap is an important part of today’s leadership priorities.
In fact, according to a Pew Research Center survey, 75% of adults aged 18 to 29 – digital native Generation Z members – could identify the presence of AI in customer service chatbots, compared to 45% of those 65 and older.
AI and machine learning (ML) tools rely on data, and Gen Z is more open to sharing data, but not everyone shares that willingness. Strategies should consider regional differences in data-sharing norms or whether a geofence is set up. Education can help ensure responsible data-sharing practices, and leaders should encourage cross-generational mentoring to bridge knowledge gaps.
Uddhav Gupta
AI and the Future of Work
Work is being reshaped, empowering businesses to transform infrastructure, revolutionize data analysis, and input high levels of personalization in product development.
Enabling data-driven decisions is a key benefit of AI and ML tools. Early on, cloud migration was met with fear because despite the scalability it offers, there are potential runaway costs. AI and ML tools provide data analysis to predict and manage cloud infrastructure costs more effectively, including smarter resource allocation and elasticity.
The global cloud computing market is projected to grow to more than $677 billion this year. Cloud providers contribute to the fundamental building blocks for applications and help simplify AL and ML model development.
Edge Computing
AI and ML traditionally were associated with large-scale data sets and cloud computing. However, advancements in edge computing have made it possible to apply AI/ML locally and in real time.
This shift enables tasks like image recognition and local analytics at the edge, reducing the need for centralized processing. The democratization of advanced technologies will make integrating tools such as AI and ML into products more accessible.
As demand for real-time data processing increases, edge computing enables more efficient and responsive solutions. By processing data closer to the source, users experience faster response times, which is critical for applications where even slight delays can have severe consequences.
Edge computing also enhances privacy and security by keeping sensitive data closer to its source and reducing exposure to potential security threats during data transit. This is vital in industries that have large silos of sensitive data – such as healthcare, finance, and government.
Subscription Models
Shifting from traditional one-time sales to subscription-based revenue models offers businesses a reliable and steady stream of income.
Subscriptions also present a unique opportunity to cultivate and enhance customer relationships. By creating a more elastic delivery model, companies can significantly improve the overall customer experience.
Independent software vendors (ISVs), for example, can benefit by establishing their own digital marketplaces. By offering a broader range of products and services, ISVs can enhance their value proposition and attract new customers who want to pay as they go. Selling services through third-party marketplaces also provides access to an expanded customer base.
As businesses grow, end-to-end automation can streamline subscription management, catalog creation, order fulfillment, and billing across multiple channels.
Innovation in the Cloud
Realizing the full potential of AI and the cloud takes infrastructure and a layer of connectivity to optimize operations.
According to McKinsey, organizations with advanced cloud-native architecture see resource utilization rates greater than 60%.
Partnering with a cloud monetization platform enables your business to operate with sophisticated cloud architecture without the burden of building infrastructure from scratch. You also gain access to the platform’s catalog of products and solutions, and can collaborate with the platform’s digital ecosystem of businesses.
At this time of rapid growth of advanced technologies, staying at the forefront of AI, ML, and cloud innovation is crucial for businesses.
Uddhav Gupta is president of CloudBlue.
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