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Insights > Blog

5 Benefits of AI in Business in 2024 and Beyond

By Sarah Jones | Posted on April 10, 2024 | Posted in Artificial Intelligence and Machine Learning,

Artificial intelligence adoption has exploded by 300% percent in the last five years. While basic automation has many advantages, advanced AI promises much more - from coordinating complex global supply chains to launching entirely new data-driven business models.

However, these expanding capabilities depend on robust infrastructure and integrations. Too often, companies hit roadblocks trying to productionalize models or scale use cases. Legacy systems strain under the data loads. Silos prevent using insights across teams, wasting AI’s potential and how much businesses even trust using it.

Microsoft CEO Satya Nadella warns, “The real technical debt we all may be accruing is the lack of integration in the organizational fabric itself as AI capabilities adapt rapidly.”

This rapid advancement makes your technology partner one of the most crucial choices for determining AI success. The proper guidance and flexible tools are vital to overcoming blockers - both now, as today’s reality strains to catch up to ambitions, and in the future as AI’s full potential unfolds.

This article discusses five significant ways AI can benefit businesses today and explores how those applications will expand significantly. We’ll also highlight the infrastructure strategies you need to scale existing use cases and prepare for AI-driven business models in the future.

Exploring 5 Benefits of AI in 2024

Companies need the right foundations to get the most out of AI investments. This includes flexible IT architecture that can connect to innovations. They also need the ability to integrate AI systems with existing infrastructure easily. Building centralized data pipelines early makes scaling more effortless, and different computing options let developers use the best tools for different AI algorithms. 

You can avoid data and model sprawl with the right technology partner. This lets talent focus on innovation instead of infrastructure issues. The right partner ensures the IT roadmap matches the rollout of business cases. With these building blocks, you can deploy AI that drives ongoing value and quickly builds on early successes.

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1. Automating Manual Work

Many organizations use robotic process automation (RPA) to automate manual processes and improve efficiency, productivity, and accuracy. With the recent advancement in AI technology, AI can work with RPA and supercharge automation across industries. A recent study by Deloitte found that combining RPA and AI delivers results above expectations compared to RPA-only deployments.

AI enhances RPA by enabling it to process unstructured data, learn from outcomes, and make complex decisions. It transforms basic task automation into intelligent, adaptable workflows. Combining AI with RPA allows businesses to automate more sophisticated processes, improve decision-making, and provide enhanced customer interactions beyond the capabilities of RPA alone.

As technology advances, AI-powered RPA systems will become more adaptive and intelligent solutions capable of tackling semi-structured tasks like analyzing customer sentiment in emails, processing invoices with diverse formats, and making preliminary assessments in customer service inquiries. To automate manual tasks in your business with the help of AI, try platforms like AI Builder in Microsoft Power Automate to build custom solutions. 

If you take this route, plan for scalability and adopt a centralized infrastructure for AI models and resources, as AI requires greater processing power. Instead of managing scattered, individual scripts, this approach allows for more efficient sharing and management of AI models across various tasks, making it easier to expand your AI initiatives.

2. Optimizing Complex Systems

AI-based tools will soon optimize fleet management, production scheduling, logistics, and customer service. They will handle complex systems and offer better insights and predictions.

AI can optimize complex systems, analyze large data sets, identify inefficiencies, and suggest improvements. This optimization lets leaders make better decisions, reduce operational costs, and create customer experiences. We see these significant efficiency gains across industries. 

One organization used an AI scheduling agent to optimize its manufacturing plant, reducing yield losses by up to 40 percent. Another example is Google, which trained its DeepMind system to predict wind turbine energy production 36 hours in advance, boosting value through foresight by around 20 percent.  By utilizing AI to generate predictive insights and streamline processes, both companies were able to increase productivity, reduce costs, and boost output value - from factory floors to the field.

You can also train your models if you have the resources. When setting up a system for AI-powered process optimization, make sure you include these considerations:

  • Establish secure access to real-time data from various sources like transactional systems, IoT devices, and weather information to respond quickly to changes in the market, customer needs, and internal operations.
  • Plan for a hybrid cloud environment with low latency that uses both public and private cloud services. Real-time analysis and decision-making require quick data processing and transfer to avoid missed opportunities or losses.
  • Optimize your cloud environment for fast data processing and transfer speeds to balance the benefits of public cloud services (scalability and cost-effectiveness) with the security and control of private cloud services.

3. Enabling Rapid Experimentation

Already this year, next-generation AI creativity tools with recursive self-improvement features have massively expanded the scope and ease of product innovations. AI can run millions of simulations combining shapes, materials, and manufacturing methods to accelerate design innovations for products ranging from jackets to jets. 

Businesses can benefit from integrating AI into experimentation by using simulation modeling to improve designs and methodologies. These integrations help create prototypes of pricing strategies and designs and explore new research and development horizons. 

An excellent example is Microsoft Azure, which uses AI to enhance capabilities like A/B testing in its Azure Cognitive Search service. This technology enables rapid AI experimentation to determine optimal techniques, saving time and resources without compromising the integrity of your experiments or results.

No matter how you want AI to speed your iteration, you must first plan infrastructure, including high-performance computing resources with integrated model-building, to run rapid, low-latency AI experiment cycles. With the right strategy, you can use AI to drive faster, cheaper experimentation, and make step-change advances.

4. Increasing Capacity

AI in business operations increases capacity by automating tasks, optimizing workflows, and providing insights for decision-making. It analyzes large datasets, leading to quicker and more efficient outcomes.

For enterprises working with a technology provider, this can look like implementing AI to automate resource allocation, predict customer demand, and make intelligent recommendations on optimal cloud configurations. This allows adaptive spinning up and down of compute instances to meet customer needs while minimizing costs. AI optimally provides the right resources by analyzing historical usage data across CPU, memory, storage, and bandwidth, minimizing overhead and wasted resources.

And these capacities are just increasing. Soon, using AI to increase operational capacity will expand to assessing company weak signals such as inventory shifts, talent exits, and market whispers to flag potential bottlenecks before they emerge. AI will also recommend remediations to overcome these issues.

To increase operational capacity, use AI tools for customer data analysis to personalize marketing strategies and increase sales conversions. You can also use AI for predictive maintenance to minimize downtime and maximize production efficiency. Salesforce Einstein and Uptake are good tools in this regard.

Pro Tip: Implement MLOps and model monitoring when working with these AI tools to mitigate risks like \AI decision bias caused by data issues or algorithmic opacity.

5. Predicting Business and Hiring Outcomes

AI-powered business and HR analytics use historical data and current market dynamics to forecast market changes, customer behavior, and potential hiring needs. This predictive power helps firms in strategic planning, market analysis, and talent acquisition. That’s why 81 percent of HR leaders have explored or implemented AI tools in their organizations.

Hiring algorithms can forecast retention risk, employee development, and skill gaps, and preempt layoff needs. Soon, with enough quality data history, AI systems will be able to project revenue, operational KPI ranges, and talent to minimize uncertainty in planning. Harver, Pymetrics, and Eightfold are valuable tools to apply AI in hiring and workforce management. 

To improve your AI models and simplify the data prep and monitoring process, create a centralized feature store for infrastructure. This store will enhance model accuracy and streamline the process even as data expands or market conditions change.

Thinking Beyond the Benefits of AI in Business in 2024

More intelligent infrastructure tools will just continue to enhance the capabilities of AI. However, you need flexible IT and infrastructure tools to implement AI successfully. This requires increased adoption of cloud platform services, MLOps automation, model repositories, and feature stores due to their ability to support quick iterations and adaptations. And the right implementation partner to help you do it well.

Redapt provides solutions to help businesses successfully adopt and integrate AI and machine learning into their operations. We assist in data organization and deployment of AI/ML models and offer various services, including AI/ML Accelerator and MLOps Accelerator. We modernize businesses for increased innovation, decision-making, and improved customer service. 

Book a clarity call and talk to one of our experts today for more information.