2024 Mid-Year Outlook: 7 Key Trends at the Intersection of Technology and Business

The world of business technology is a whirlwind of constant innovation. Like a snowball rolling downhill, tech advances spread rapidly across dozens of business sectors, thus sparking new industry-specific innovations and unleashing ever-expanding waves of transformation and disruption.

This rapid pace of change can be both exhilarating and overwhelming for businesses and professionals striving to stay at the forefront of their industries. And while the task might seem daunting, it's an essential part of our tech-infused world: Staying informed helps you anticipate market shifts and enables you to leverage emerging technologies to drive growth and efficiency in your organization.

And that's precisely why TEAM International is here to spotlight seven of the the hottest trends in business technology. This list covers the most exciting innovations shaping our digital future, from new and imaginative Gen AI applications to transformative data practices in digital marketing. Explore these trends and gain valuable insights into the future!

1. Predictive maintenance

As the manufacturing industry experiments with several AI implementations, one of the absolute standout use cases has been the advent of predictive maintenance solutions through the use of IoT sensors and AI analytics. Simply put, IoT sensor information (which can gather anything from temperature and humidity to energy consumption and pressure) has been found to be an incredibly powerful data source for AI algorithms seeking to provide maintenance prescriptions for machinery.

According to McKinsey, predictive maintenance can reduce machine downtime by 20 to 30% and even prolong machine life by as much as 20 to 40%. Another study by Deloitte found that predictive maintenance can increase labor productivity from 5 to 20%.

Furthermore, this use of IoT sensor data has recently been supplemented by computer vision, a subfield of AI that focuses on deriving meaningful information through image analytics.

Companies utilize machine vision for predictive maintenance by gathering vast image-based data sets of their machinery. They may also manufacture the images directly through synthetic data creation techniques. Then, once the data is ready, AI models analyze the information and learn to detect faults in the machinery. These models can even learn from thermal imaging heat patterns and high-speed camera images that pick up incredibly subtle vibrations.

Predictive maintenance is quickly revolutionizing our manufacturing industries through IoT and image-based data!

2. AI-powered digital assistants

Exemplified by products like the Rabbit R1 and the AI Pin, generative AI is giving rise to a new class of digital assistant designed with simplicity in mind.

Unlike traditional digital assistants such as Siri or Alexa, which are integrated into smartphones or home devices, these new hardware products are standalone AI assistants. Their primary goal is to offer users an AI-powered device that is both easy to use and versatile, eliminating the need to navigate through numerous apps on a smartphone. Users can simply pull out the device, ask a question, and receive a prompt response.

While early reviews of these products have identified some limitations, they nonetheless represent an intriguing glimpse into how generative AI could potentially liberate us from the constraints of relying solely on smartphones and computers for digital tasks. In an era where hardware innovations are relatively rare, and many people experience smartphone fatigue, these offerings might be tapping into a genuine need for simpler, more focused digital interactions.

3. First-party data in digital ads

As the fight over data privacy heats up, digital advertisers are finding it harder and harder to access your personal data through third-party means like website cookies. As such, first-party data is increasingly dictating the strategies and methods utilized by digital advertisers to drive commerce on the Internet.

To clarify, first-party data is any and all information that is gathered through direct interactions between organizations and their clientele. Examples include website interactions, mobile app usage, customer surveys, purchase history, etc.

But what does the shift to first-party data mean for the future of digital advertising?

The most significant effect will likely be an increased focus on building stronger and more direct customer relationships. Essentially, brands will need to prioritize a deeper bond with their customers that allows them to collect valuable first-party data. Key to this new strategy will be content-based marketing, loyalty programs, personalized experiences, and a heavy investment in user analytics to help companies understand their customers beyond simple demographic data. Ultimately, those who can figure out how to encourage users to willingly share their data will be the winners in this new data collection landscape.

4. Industry blockchain solutions

Despite having a rocky start, many industries are now finding suitable applications for blockchain technology. Among these, we find:

• Art Provenance Tracking: Blockchain-ledgers can record an artwork's lifecycle and have many other benefits. They permit artist authentication, ownership transfers, and exhibition history. They're also an excellent tool to combat forgery, ensure fair artist compensation, and increase transparency in the art market.

• Sustainable Material Tracking: Blockchain solutions allow for the tracing of materials throughout the supply chain. The unique nature of blockchain ledgers ensures the veracity of sustainability metrics for these materials and thus helps promote their sustainable reuse.These platforms also enable brands to verify eco-friendly claims, consumers to make informed choices, and regulators to ensure compliance with environmental standards.

• Healthcare Patient Data Management: Decentralized blockchain networks allow for secure, patient-controlled health records and seamless data sharing between healthcare providers while maintaining patient privacy. This improves data accuracy, reduces administrative costs, and enables faster, more informed medical decisions across different healthcare systems.

5. Quantum computing in finance

Much like we saw with generative AI in 2022, quantum computing—a paradigm shift in computing power enabled by quantum physics—is slowly inching toward real-world applications.

Based on recent trends, it seems like finance will be one of, if not the first, sectors to adopt quantum computing for real-world business applications. The potential use cases of such vast computing power are vast:

• Risk management calculations
• Fraud detection
• Algorithmic trading
• Credit scoring
• Economic forecasting
• Financial market simulation
• Asset pricing

It's important to note that many of these applications are still theoretical or in very early stages. Nevertheless, many eyes are currently set on these potential innovations as the successful adoption of quantum computing in finance would almost certainly ignite a race to implement the technology in other areas like cybersecurity and climate sustainability.

6. AI governance models

In the quest to achieve the implementation of company-wide AI solutions, enterprise AI governance models have emerged as a tool that companies cannot afford to miss. While it's true that many organizations manage to achieve satisfactory outcomes when implementing a lone, haphazard AI pilot, a successful AI project that integrates various departments and spans the whole of the company's operations, as it turns out, is a completely different and much more difficult challenge.

AI governance models significantly increase the odds of successful AI ventures by clearly laying out company strategy with regards to:

• Roles and responsibilities: Defines who's accountable for different aspects of AI implementation, ensuring clear authority lines.
• Organizational model: Outlines how AI initiatives fit into the company structure, aligning with overall organizational design.
• Broader AI objective: Articulates the company's main goals for AI adoption, guiding decision-making and prioritization.
• Data and infrastructure policy: Establishes guidelines for data handling and technical resources needed to support AI initiatives.

• AI ethics and risk management: Addresses ethical implications and potential risks of AI use, including fairness and mitigation strategies.

7. RPA as a service

Cloud-based delivery, cost-affordable implementation, managed infrastructure, scalable resources... By now it's likely that we're all quite familiar with the advantages of the "as-a-service " model. This familiarity with the basic incentive structure, however, does not make the arrival of new services any less revolutionary.

Benefiting both businesses and individuals, the most recent tech integration paradigm being offered as a service is that of Robotic Process Automation, also known as RPA. This constitutes a major business disruption as RPA-enabled efficiency gains will now be massively democratized throughout the whole of the digital business landscape.

Whether it's cash-strapped startups looking to automate routine tasks and free up resources for innovation or established enterprises seeking to streamline operations and improve scalability, RPA as a service (RaaS) levels the playing field and benefits the consumer.