In the first half of 2023, there was a lot of excitement about investing in technology to achieve impactful enterprise-wide outcomes. However, the practical value of technology enablement doesn't just come from adopting innovations. It's about bringing continuous improvements to the core of company operations and business processes.
In 2024, tech executives should approach new technologies with a deliberate and realistic mindset, ensuring each effort serves a purpose and any experiment is purpose-driven. In this article, I’ll analyze the top 5 enterprise technology trends of 2024 and explore how they will impact global businesses.
Trend 1: Generative AI
Despite polarized opinions and exaggerated hype surrounding generative AI, it will progress beyond offering easy access to complex knowledge and provide new capabilities, including the below.
- Enhanced asset management.
Generative AI aids accurate budgeting and financial forecasting, empowering enterprises to plan revenue and expenses with greater certainty. Analyzing historical data, GenAI helps identify promising trends for potential profit opportunities.
- Automated data entry. GenAI tools automate tedious data entry tasks, streamlining the processing and systemization of large documents within seconds. This enhancement to data processing has around 72% of executives optimistic about boosting productivity across their organizations.
- Intelligent fraud detection. Generative AI contributes to more effective fraud and suspicious activity detection, leading to increased customer satisfaction, improved accuracy, and cost reduction, as demonstrated by JPMorgan Chase.
- Smart contextual search. Facilitating data management and information retrieval, GenAI enables employees to complete tasks and generate reports more rapidly. Customers benefit from fast and detailed responses to individual queries, bypassing static FAQ pages.
- Personalized content generation. Combining enterprise databases with generative AI opens new avenues for customer operations, offering hyper-personalization options. Employees can input relevant prompts for models to produce exclusive, specialized texts, visuals, or messaging tailored to specific partner needs.
The effectiveness of GenAI technology hinges on data quality and the skills of engineers, so it’s important to assemble a strong team of AI strategists and data scientists. They should be capable of developing tailored solutions aligned with an organization's tech and security requirements.
Trend 2: ML operations
Also called MLOps or industrialized machine learning, machine learning operations refers to the processes and practices that speed up and scale the development of ML models within a company. This includes support, performance monitoring, and continuous improvement.
MLOps functions within a system of software and hardware solutions, bringing enhanced value and functionality to enterprises. Industrialized ML helps businesses speed up analytics, identify production issues, and transform PoC projects into products faster.
The adoption of industrialized machine learning has surged, and more companies are currently actively investing in MLOps. According to IDC, approximately
We can clearly see the shift of ML technology from its pilot stage to a new phase of proactive and decisive adoption. Enterprises are now planning to achieve a competitive advantage through industrialized machine learning—and they are confident about achieving success.
Trend 3: Intelligent enterprise apps
Gartner predicts that by 2026, approximately
The rise of intelligent apps is explained by the growing use of mobile payment options, wearable apps, and apps with VR and AR features. Modern apps better the UX by ensuring more accurate command execution and providing more detailed analytics, often incorporating AI algorithms and machine learning.
So, why are intelligent applications considered valuable for the future of enterprise?
- Augmented analytics. AI-powered apps process large volumes of data from various sources and deliver real-time insights to users.
- Efficient decision-making. By omitting irrelevant data, intelligent apps provide leaders with more informative reports, enabling them to make impactful decisions.
- Streamlined sales operations. Such apps assist sales representatives in lead generation and nurturing by scoring leads based on behavior data analysis.
- Management shortcut discovery. Intelligent apps gather data on business processes and employee performance, identifying opportunities to reduce repetitiveness and enhance employee engagement.
Intelligent software applications are also praised for their continuous improvement capabilities. With ML models and AI algorithms, these apps can adapt to new requirements, updating their output and maintaining high-quality performance.
Trend 4: Web3 development
Web3 technology finds applications across various sectors from banking, financial services and insurance (BFSI), media, and entertainment to logistics and retail because of the additional trust it provides.
Key aspects of web3 adoption include:
- Reduced authority intervention. Web3 shifts power dynamics by giving businesses more control over their assets. The framework emphasizes transparency and equal responsibility among participants.
- New business model development. Web3's focus on transparency opens doors to innovative business models and ensures fairness through automation, smart contracts, and a shared truth source among stakeholders.
- Web3 regulations. Web3 integration into business requires regulatory frameworks. That’s why early adopters play a key role in normalizing and advocating web3 technology to set the groundwork for rules and standards.
The increasing investment in decentralized technology stimulates the development of energy-efficient solutions, a crucial step for organizations that want to maximize their potential in industry 4.0 before transitioning to industry 5.0.
Trend 5: Cloud computing
As businesses shift to the cloud, its influence on current and future enterprise processes remains profound. Here are key innovative directions in which cloud computing is evolving:
- Combined cloud and edge computing. Integrating edge computing with cloud technology helps organizations reduce data transfer costs, achieve better latency, and reinforce cybersecurity.
- Hybrid models. This strategy benefits developers, fostering workload distribution, cross-department collaboration, and flexibility in costs and investments.
- AI/ML integration. AI-powered tools enhance cloud computing service and security monitoring, providing real-time insights crucial for decision-making and data analytics. This combination has led to the rise of Artificial Intelligence as a Service.
Businesses will continue investing in expanding internal development teams with professionals having essential cloud computing skills to explore the potential of the latest cloud-powered directions.
How do I choose the right direction?
Picking the right enterprise technology from the tendencies of 2024 is simpler than it seems.
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Don't hyperfocus on trends. Choosing the most popular trend might not be what defines the future of an enterprise. It's about finding technology that fits the specific needs of a business.
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Keep reusability in mind. Not all trends stay relevant. Focus on innovations that maintain strong positions over time and offer continuous improvement for long-term value and ROI. Consider the results expected at least five years after implementing technology.
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Stay realistic. Top enterprise technology trends may seem promising, but the adoption journey is long and complex. Connect wishes to reality by calculating estimated ROI and evaluating potential organizational resistance.
Also published here.