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Business leaders worldwide have recognized the transformative potential of analytics and artificial intelligence (AI). According to IBM’s report on Global AI adoption, 44% of organizations are actively embedding AI into existing applications and processes, with two-thirds already reaping the benefits. According to Forrester’s 2022 Data and Analytics Survey, 73% of decision-makers are investing in AI. The projected global spend on AI is expected to double from $33 billion in 2021 to $64 billion by 2025. This surge in adoption is driven by the desire to unlock AI’s full potential across various use cases, extending its reach beyond data science teams to integration in business operations, software development, and IT. Notably, the “service operations” and “strategy corporate finance” industries are leading the charge in AI adoption.
This surge in the adoption of Data and AI reflects a fundamental truth: AI is no longer just a futuristic concept, it’s a critical tool for building differentiated experiences and applications. Organizations need to embrace AI’s capabilities and unlock its transformative power to thrive in this era of rapid change.
The broader adoption is facilitated by powerful AI platforms that streamline development processes, empowering even new teams to implement AI solutions effectively. Modern AI services allow organizations to quickly comprehend the value of AI and strategically plan their goals.
In alignment with this paradigm shift, Microsoft Azure AI emerges as a prominent player. Azure AI goes beyond mere technology; it’s a strategic partner on your journey toward modernization, increased effectiveness and efficiency, and data-driven decision-making.
Azure AI is more than just technology; it’s a catalyst for transformation. This blog emphasizes the measurable return on investment (ROI) from implementing Azure AI, outlining advantages like cost-effectiveness, accelerated market readiness, enhanced productivity, and various other benefits.
What is Microsoft Azure AI?
Microsoft Azure AI is a comprehensive suite of cloud-based artificial intelligence services that empower organizations to build, train, deploy, and manage AI solutions. With Azure AI, you can build and deploy your own AI solutions. It offers a wide range of capabilities, including:
- Pre-built AI models: Gain access to high-quality, pre-trained models for tasks like computer vision, speech-to-text, natural language processing, and anomaly detection. These models can be used directly through simple APIs, eliminating the need to build your own from scratch.
- Machine learning tools: Develop and train custom machine learning models using Azure Machine Learning, a powerful platform with tools for data preparation, model training, and deployment. Azure Databricks is also available for large-scale ML workloads.
- AutoML: Simplify the machine learning process with Azure Automated Machine Learning, which automatically selects and configures the best algorithms for your data and task.
- Data and analytics: Prepare and analyze data for your AI solutions using Azure’s comprehensive data platform, including services for data storage, processing, and visualization.
- IoT and edge AI: Connect and analyze data from sensors and devices at the edge with Azure IoT Edge, enabling real-time AI applications.
- Responsible AI: Ensure the ethical and responsible development and deployment of AI solutions with tools for bias detection, explainability, and governance.
- Azure AI Platform: Access pre-built AI models, develop custom models, and manage your AI solutions from a unified platform.
Microsoft Azure AI: Use case and impact
In the context of the rising trend of adopting modern AI services, Microsoft commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study on Azure AI. This study aims to provide readers with a framework to evaluate the potential financial impact of Azure AI on their organizations. To comprehend the benefits, costs, and risks associated with this investment, Forrester conducted interviews with 6 key decision-makers at 5 customer organizations experienced in using Azure AI. The insights gathered were aggregated to create a composite organization, offering valuable perspectives on the potential return on investment (ROI) enterprises may realize by deploying Azure AI.
Prior to Azure AI, some interviewees were familiar with AI and ML and had previously deployed and tested their own models internally. Others relied on traditional methods like physics or economic models, while a few used basic off-the-shelf AI tools. However, these tools lacked crucial functionalities, and scaling custom models proved difficult and time-consuming.
To overcome these obstacles and gain an edge through innovation and efficiency, these organizations sought an external partner to leverage advanced AI and ML solutions, propelling their businesses forward and outpacing the competition.
They invested in Microsoft Azure AI to improve the quality and scalability of their AI and ML models and used ML predictive models to optimize costs across the business. Azure AI led to a significant reduction in error rates compared to previous models, freeing up employee time and enhancing the customer experience. By leveraging Azure AI’s Cognitive and Applied AI services, organizations applied AI to key customer touchpoints and revenue streams, achieving employee productivity and business growth.
The key findings from the study are summarized below:
Azure AI’s measurable value: A quantified analysis
Azure AI delivered significant, quantifiable benefits for the composite organization, driving growth, efficiency, and cost savings
Increased work output and business growth
- 150% increase in work output: Azure AI automated and scaled processes, enabling the composite organization to handle greater business volume. This translated to over $12.5 million in profit over three years.
- Improved customer acquisition and retention: Through AI-powered solutions, the composite organization attracted and retained customers more effectively, contributing to the overall profit increase.
- Development and sale of new services: Azure AI facilitated the creation and launch of new services, further fueling business growth.
Spending optimization and cost reduction
AI-powered analytics and models enabled informed decision-making and cost-saving opportunities. By optimizing 1-2% of operating costs, the composite organization realized $16 million in savings over three years.
Increased operational efficiency
- 25% productivity gain for ML teams: Azure AI streamlined ML model development, training, deployment, and validation, allowing the team to be more productive and deploy more solutions.
- 60% reduction in document processing errors: Replacing legacy OCR tools with Azure AI technology improved accuracy and saved over $1 million in three years.
- Modernized infrastructure: Transitioning to Azure’s cloud-based model eliminated legacy licensing and maintenance costs, resulting in savings of $57,000.
These tangible benefits demonstrate Azure AI’s significant impact on the organization’s performance and profitability.
Beyond quantifiable benefits: The unmeasurable value of Azure AI
While the study focused on quantifiable benefits, Azure AI provided additional value to the composite organization in several crucial areas:
- Optimized budgeting and reallocation: Azure AI’s predictive demand and revenue models enabled decision-makers to optimize spending and allocate resources for maximum capital gains.
- Faster and higher quality decision-making: Data-driven insights from Azure AI informed executive-level decisions, leading to faster and more effective action.
- Greater innovation and collaboration: Early successes with Azure AI-fueled cross-team collaboration, fostering innovation and uncovering new ways to unlock value from data and AI.
- Improved employee safety: Remote equipment monitoring, and hazard detection powered by Azure AI significantly reduced employee safety risks.
- Enhanced model compliance and governance: Azure AI’s tools and frameworks helped ensure responsible AI development and deployment, including fairness, robustness, and security.
- Reduced greenhouse gas emissions: Optimized resource utilization through Azure AI led to lower energy consumption and carbon footprint for the organization.
- Improved employee experience: Automation of routine tasks freed up employees’ time for more engaging and fulfilling work.
- Reduced risk: Azure AI helped identify and manage potential risks, mitigating compliance concerns and reducing penalties. Predictive analytics anticipated and prevented equipment failures and risky customer behavior.
These unquantifiable benefits highlight the broader impact of Azure AI beyond immediate financial returns. They contribute to a more efficient, innovative, and sustainable organization, fostering employee well-being and risk mitigation.
Cost efficiency: Evaluating risk-adjusted PV costs
Azure AI consumption and services costs totaling $1.6 million.
Azure AI pricing is structured based on the type of capability or use case and usage. Meanwhile, Azure Machine Learning pricing hinges on the compute resources utilized for training or inferencing. Variances exist based on region and specific organizational characteristics and needs. By Year 3, the composite organization allocated $350,000 for Azure Cognitive Services and Applied AI Services, $250,000 for Azure Machine Learning, and $30,000 for Microsoft implementation and support services.
Implementation, application building, and training costs totaling $1.7 million.
The composite organization incurred initial internal labor costs associated with implementation, change management, and training of internal employees in Azure AI. The nine-month initial phase involves tasks like data and technological infrastructure setup, application building, and model training. A team of six engineers contributes to 80% of this work. During the first third of Year 2, four engineers focused on AI applications for new product development. The AI/ML engineers and data scientists dedicated 80 collective hours to receive training and insights into Azure AI. As a managed account with Microsoft, the composite organization benefited from free structured training or guidance from the Microsoft team.
Management and quality assurance labor costs.
Ongoing administration and management of Azure AI entails solution monitoring, bug fixes, retesting, optimization, data storage maintenance, integrations, internal auditing, and reporting on models, which may result in costs totaling $4.9 million. In Year 1, 15 AI/ML engineers supported Azure AI, increasing to 30 in Year 2 and 40 in Year 3. The percentage of time spent managing and optimizing use cases decreases over time, from 50% in Year 1 to 25% in Year 2 and 15% in Year 3. A QA team of up to five employees ensured ongoing oversight to maintain the quality and usability of model outputs.
The economic benefits of innovating with Azure AI
Innovating with Azure AI can yield several economic benefits for organizations. Some of these benefits include:
Cost savings
Azure AI allows organizations to optimize processes, automate tasks, and enhance efficiency, potentially saving costs. By leveraging AI capabilities, businesses can streamline operations, reduce manual efforts, and minimize resource allocation in certain areas.
Increased productivity
Automation and intelligent decision-making facilitated by Azure AI can significantly boost productivity. With AI handling routine tasks, employees can focus on more strategic and complex aspects of their roles, leading to enhanced overall productivity.
Improved customer experiences
Azure AI enables the development of intelligent applications, chatbots, and virtual assistants that enhance customer interactions. This improvement in customer experiences can lead to increased customer satisfaction and loyalty, positively impacting the economic performance of the business.
Data-driven insights
Azure AI provides advanced analytics capabilities, allowing organizations to derive valuable insights from their data. Informed decision-making based on these insights can contribute to better strategic planning and resource allocation, resulting in improved economic outcomes.
Innovation and competitive advantage
Azure AI empowers organizations to innovate by developing and deploying cutting-edge AI solutions. This innovation can lead to the creation of new products or services, providing a competitive edge in the market and potentially opening up new revenue streams.
Faster time to market
With Azure AI, organizations can accelerate the development and deployment of AI-powered solutions. This speed-to-market advantage can be crucial in capitalizing on market trends, gaining a first-mover advantage, and realizing economic benefits sooner.
Scalability and flexibility
Azure AI services are designed to scale with the needs of the business. This scalability allows organizations to adapt to changing demands, ensuring that AI solutions can grow alongside the business without significant economic constraints.
Risk mitigation
Organizations can identify potential issues before they escalate by harnessing AI for predictive analytics and risk assessment. This proactive approach can contribute to risk mitigation and financial stability.
Conclusion
Organizations embracing Azure AI have witnessed a spectrum of advantages, ranging from heightened model quality and scalability to lower error rates in model outputs, enriched customer experiences, and amplified efficiency and innovation. These forward-thinking entities have strategically utilized Azure AI services to optimize costs, foster business growth, and automate diverse processes.
For a seamless integration of Microsoft Azure into your business, unlocking the transformative potential of AI, Confiz is here to assist. Contact us at Confiz@marketing.com to embark on your journey toward harnessing the full power of Azure AI.
