Cloud Computing 2.0: AI Drives Next-Level Productivity

Have teams organize, track and clean data to set up your organization for success as AI capabilities evolve.

Mike Jennett, Director of Platform Strategy

June 7, 2024

4 Min Read
AI and productivity
Ralf Geithe/Shutterstock

Artificial intelligence (AI) is a market disrupter, a change agent that is impacting daily operations for businesses and consumers. Coupled with cloud computing, these technologies can boost productivity and product capabilities to new heights.

Recent research finds that 70% of companies using the cloud are now using managed AI services to some degree, and more organizations are experimenting with incorporating AI workloads into their cloud portfolios.

Today's business customer is expecting more customization and tailored capabilities, all while expecting costs to remain low. Combining AI into your cloud business gives you the ability to tackle these customer needs with your existing workforce, combined with trusted advisors.

By investing in the development of in-house expertise and collaborating with knowledgeable agencies, your team can establish a set of AI-based data quality standards and protocols that allow you to automate mundane tasks and move your technical resources to more meaningful work.

The Perfect Pairing

Cloud infrastructure, with its agile framework, is inherently aligned with the nature of AI development. For example, infrastructure costs can vary from month to month based on usage and are often difficult to predict. By enabling AI and machine learning (ML) capabilities, your organization can analyze workload patterns and usage data, avoiding over- or under-provisioning. Your team can then optimize cloud infrastructure spending by increasing or decreasing resources based on insights provided by AI.

Your customers benefit because the agility of cloud computing combined with the brain power of AI can enhance the overall customer experience by delivering personalized results based on how customers interact with a software-as-a-service (SaaS)-based system. Take, for instance, the Netflix model, where AI algorithms analyze user preferences to deliver personalized recommendations in real time.

This type of customization enabled by AI can drive customer engagement and retention.

Why Data Governance Matters

While the pairing of cloud-based systems and AI can streamline costs and provide personalization to customers, it only works if there is strong data governance in place to ensure data quality in your learning models.

Data governance is the backbone of any AI framework and plays a critical role in reducing enterprise risks. Data, patterns and trends hold significant value for businesses, and AI offers great assistance in organizing data and streamlining its consumption, enabling the transformation of insights into actionable strategies.

However, the outputs are only as good as the inputs, and it requires human expertise to properly train your AI with the right data.

Poor-quality or incomplete data can significantly impact the accuracy and reliability of AI-driven insights and decisions, leading to erroneous conclusions or suboptimal business outcomes. In essence, you could wind up doing more harm than good.

Misinterpreted or misaligned data not only undermines the efficacy of AI algorithms but also exposes organizations to regulatory risks and legal liabilities.

The customer experience is also impacted by data risks because you could be trying to steer the customer in one direction, but an AI algorithm based on poor data steers the customer in a different direction and then you start losing business.

So What Should You Do?

To take advantage of incorporating AI into your cloud-based capabilities, you need to make fundamental cultural shifts within your organizational mindset and move toward continuous learning and adaptation.

Start developing your in-house expertise and collaborate with professional agencies that have a good understanding of AI technology. Your team will want to establish data quality standards and protocols to:

  •  Ensure the integrity, accuracy and relevance of the data used to train and deploy AI models

  •  Create an audit trail with documentation and version control for datasets

  •  Utilize data validation techniques, data cleansing processes and regular data quality assessments to identify and deal with issues proactively.

By focusing your team on organizing, tracking and cleaning your data, you will set your organization up for success as AI capabilities evolve and become a core aspect of business.

Is AI Coming for Your Job?

It's becoming abundantly clear that AI isn't a threat to jobs but rather a catalyst for enhancing productivity. By automating routine tasks AI frees critical resources to focus on higher-value activities, promoting innovation and creativity.

It's important to foster a company culture that embraces AI as a tool to enhance business capabilities. If you approach AI with a technology and strategic mindset, you're in a better position to identify how AI algorithms can be swiftly adapted to improve specific tasks and processes within your business.

Cloud Computing 2.0 Playbook

These are early days when it comes to AI adoption, but it's important to recognize that while technologies continue to advance, the fundamentals of best practices remain constant. As you navigate cloud computing plus AI integration in your daily operations, stick with the basics and adapt your proven recipe for success accordingly.

You make AI better when you feed it responsible data and provide good governance because, at the end of the day, best practices are at the heart of any revolution. The future belongs to those who dare to embrace the perfect blend of AI and cloud technologies, paving the way for productivity "heaven" in the digital age.

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About the Author(s)

Mike Jennett

Director of Platform Strategy, CloudBlue

Mike Jennett is director of CloudBlue platform strategy. A tech executive with a deep focus on product development and go-to-market strategy, he plays a pivotal role driving strategic growth. He previously was VP of IDC's Mobility and Digital Transformation IEP practices, and also held leadership positions at HP. He holds a bachelor's degree from California Polytechnic University and has contributed to multiple tech publications.

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