2024 Technology Recap: Expert Insights Into the Year of AI and Machine Learning
The tech landscape of 2024 was defined by groundbreaking advancements in artificial intelligence (AI) and machine learning (ML), and their convergence with automation, IoT, and cybersecurity.
These innovations reshaped business operations, enabling smarter, faster, and more efficient workflows. However, the rapid pace of change brought challenges, from managing governance and security risks to bridging the gap between technological promise and practical implementation.
In this blog, we will explore some pivotal trends from 2024 and how they set the stage for further innovation and transformation in 2025.
Here are four key development areas we saw in the tech sphere in 2024:
AI and Machine Learning
Microsoft Copilot - a new AI tool - integrates with your Microsoft 365 and Dynamics 365 tools with the right licensing in place.
Artificial intelligence (AI) dominated the tech landscape in 2024, becoming an integral enabler across industries rather than a standalone innovation. While the excitement around large language models (LLMs) like ChatGPT was a major milestone, the real story lay in how AI worked with automation to drive meaningful change.
AI as an Extension of Automation
Automation has long been a cornerstone for efficiency, but 2024 highlighted how AI enhances this by tackling tasks that require adaptive learning and contextual understanding. From summarizing contracts to generating actionable insights, AI has proven its capability to augment traditional workflows.
However, these systems thrive when paired with clear business structures and frameworks. A good example of this is enhancements to automated document generation. Companies can now use AI to standardize, analyze, and dynamically adapt contracts and other documents. This saves time and maintains greater accuracy.
Evolution of AI Tools: From Development to Business-Ready
One significant breakthrough has been the democratization of AI capabilities. Tools like Microsoft's Power Platform and Copilot Studio empower non-technical users to harness the power of AI without requiring programming expertise. Business users can now deploy AI agents for returns management or order processing, reducing dependency on professional developers and accelerating time-to-value.
Shifting Expectations: AI in Practical Use Cases
Despite its promise, 2024 exposed the gap between market expectations and AI’s current capabilities. Real-world success stories emerged by focusing on defined use cases, such as dynamic document creation or personalized customer interactions.
These practical applications underscored the need for careful planning, iterative frameworks, and collaboration between technical and business teams.
Internet of Things (IoT)
Azure IoT is a collection of Microsoft-managed cloud services, edge components, and SDKs that allow you to connect, monitor, and manage your IoT devices and assets at scale.
In 2024, IoT (Internet of Things) in business systems has experienced significant growth, transforming how organizations approach manufacturing, traceability, and enterprise management. This evolution marks a shift from consumer-focused IoT to industrial and operational applications that enhance business efficiency and data utilization.
From Consumer to Industrial IoT
While consumer-focused IoT devices like Amazon’s Dash buttons initially popularized the concept, 2024 has seen a strong pivot toward industrial IoT. Companies are increasingly leveraging IoT for manufacturing processes, such as:
- Monitoring power consumption and fluid flow rates in real-time.
- Tracking batch numbers and lot traceability in food production and other industries.
- Embedding IoT into field service systems, particularly within platforms like Dynamics 365.
This trend underscores IoT's growing role in enabling smarter, data-driven operations across various industries.
Business System Enhancements via IoT
The proliferation of affordable and versatile IoT devices, such as Raspberry Pi and ESP-based sensors, has made it easier than ever to integrate IoT capabilities into business systems. These devices gather critical telemetry data, which businesses use to enhance their Enterprise Asset Management (EAM) systems. For instance:
- IoT sensors can monitor machinery performance and trigger predictive maintenance workflows when anomalies are detected.
- Integration with PLM (Product Lifecycle Management) systems enables automated production processes, such as sending production instructions directly to machines.
AI-Driven Automation in IoT
To revisit AI, its convergence with IoT has unlocked powerful automation capabilities. By analyzing IoT data, AI agents can:
- Detect deviations from expected thresholds.
- Automatically generate work orders for repairs or adjustments.
- Monitor and optimize operational efficiency in real-time.
For example, IoT devices can send data through the Power Platform or custom APIs. AI-driven workflows take over to execute automated responses, such as dispatching a technician or adjusting production parameters.
Simplification Through Platform Consolidation
2024 also brought a notable simplification of IoT platform options. Microsoft’s decision to deprecate certain Azure IoT Services in favor of a unified IoT platform has eliminated confusion and streamlined customer adoption. This move highlights a broader industry trend toward consolidation and standardization in IoT solutions.
Business Intelligence (BI) and Data Management
Power BI is a business analytics tool by Microsoft that enables you to visualize your data, share insights, and make data-driven decisions through interactive reports and dashboards.
In today’s data-driven world, the unification of business intelligence (BI) and data storage is a critical focus for organizations. Historically, fragmented data systems like CRM and ERP posed significant challenges, requiring complex integrations to generate meaningful insights. However, recent advancements in BI tools and storage architectures are simplifying these processes and driving innovation across industries.
Breaking Down Silos: Unified Data Strategies
A common pain point for businesses has been the need to move data between systems, often through resource-intensive integrations. For example, sales opportunity data in a CRM system often needs to inform supply chain decisions in an ERP. Instead of relying on complex data transfers, modern BI solutions now leverage advanced reporting capabilities to provide actionable insights directly from source data, eliminating redundant integrations and streamlining workflows.
Key players like Microsoft Fabric are at the forefront of this evolution. By introducing OneLake and robust data lake architectures, Fabric enables seamless data storage and analysis across systems. These advancements are particularly impactful for finance and supply chain operations, where data lakes act as central repositories, enhancing reporting and decision-making capabilities.
Natural Language and AI in BI
BI tools are becoming increasingly intuitive, thanks to AI-powered natural language capabilities. Features like Power BI Copilot allow users to query data conversationally, identify trends, and easily spot outliers. This shift democratizes data analysis, empowering users across all technical skill levels to uncover insights without requiring specialized knowledge.
The integration of Microsoft SQL Server into Fabric further enhances BI capabilities. Organizations can now combine operational data with advanced reporting features, opening the door to:
- AI-driven automation
- Real-time triggers
- Autonomous agents that execute tasks based on predefined criteria
Optimized Storage for Performance and Cost
One of the most significant shifts has been the strategic separation of transactional and unstructured data storage. Transactional databases, optimized for efficiency, are kept small and performant, while unstructured data—such as documents and media files—is stored in cost-effective, scalable systems. This approach reduces costs and minimizes performance bottlenecks, particularly in cloud-based environments.
Businesses are now more empowered to choose storage solutions tailored to their needs, balancing performance, scalability, and budget. This flexibility is driving a deeper understanding of best practices, such as leveraging data lakes for unstructured data and reserving high-performance storage for critical transactional data.
Cybersecurity
2024 marked a critical turning point in cybersecurity, driven by high-profile data breaches and the rapid adoption of AI technologies. The increasing complexity and volume of cyber threats underscored the urgency for robust cybersecurity measures across organizations of all sizes.
The Cost of Data Breaches
Cyberattacks in recent years have highlighted the devastating potential of data breaches. For instance, a recent hack exposed private user information, which cybercriminals leveraged in a $53 million heist. These incidents underline the importance of safeguarding digital data, as its misuse can have far-reaching implications for individuals and businesses alike.
Expanding the Definition of Cybersecurity
Cybersecurity is no longer limited to intrusion detection systems (IDS) or technical safeguards. It now encompasses:
- End-User Training: Educating employees to recognize phishing attempts and scams that target credentials and sensitive information.
- Governance Models: Establishing policies for AI usage, data privacy, and access control to mitigate risks associated with emerging technologies like AI-driven tools.
- Cloud and On-Premise Security: Adopting advanced tools like Microsoft Purview for managing sensitive data across hybrid environments.
AI and Cybersecurity Challenges
AI's integration into business processes has been a double-edged sword. Tools like Microsoft Copilot enhance productivity but introduce new vulnerabilities if governance models are lacking. For instance, poorly configured permissions in systems like OneDrive or Azure could inadvertently expose sensitive data to AI-driven tools or external entities.
Organizations are increasingly demanding governance frameworks to regulate AI deployment. This includes setting clear boundaries on what AI tools can access, and ensuring sensitive data remains secure.
Many companies have even restricted AI use until they can implement robust security protocols.
Key Cybersecurity Developments in 2024
- Passkey Initiatives: Microsoft reported blocking 7,000 password attacks per second, emphasizing the need for passwordless authentication solutions.
- AI Governance: Companies adopted policies to regulate AI use, reflecting concerns about data privacy and ethical implications.
- Data Sensitivity Labels: Organizations leveraged tools to classify documents as confidential or unrestricted, ensuring AI systems respect data privacy standards.
Looking Ahead to 2025
As cyber threats evolve, so must cybersecurity strategies. The focus is shifting toward:
- Data Privacy Frameworks: The U.S. may follow the lead of GDPR, implementing nationwide data privacy regulations to ensure ethical AI development and secure data use.
- AI-Specific Roles: New positions, such as Chief AI Officer, will work alongside traditional IT roles to manage AI governance and cybersecurity risks.
- Ethical AI Deployment: Companies must balance innovation with privacy, ensuring AI models are trained and deployed responsibly.
Microsoft's commitment to trust and privacy, including tenant-level encryption and stringent data access policies, sets a benchmark for the industry. However, the journey to comprehensive cybersecurity remains a collective effort requiring vigilance, innovation, and collaboration.
Talk to Stoneridge Software Experts to Stay Ahead of Technology Trends
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