With the significant advantages that AI is delivering industrial operations, speed is of the essence.
Digital teams are coming under increasing pressure to build and deploy AI solutions quickly that can enable businesses to drive process and operational innovation across a whole array of different use cases (think predictive maintenance, through resource and energy efficiency, production optimisation, and many more). Yet, many face significant barriers that slow down AI roll-out.
This article explores how you can overcome these obstacles and accelerate AI deployment to deliver immediate benefits.
Barriers to Fast AI Roll-Out.
Deploying AI rapidly can be challenging and there are a number of common hurdles seen across industrial businesses, no matter the sector:
- Slow Company Processes: Bureaucratic delays and risk-averse cultures can hinder swift AI adoption. A study by McKinsey found that 50% of companies struggle with slow internal processes when trying to implement AI solutions.
- High Costs + Long Development Times: Custom application development is often seen as expensive and time-consuming. According to Gartner, 85% of AI projects do not achieve their intended objectives due to high costs and lengthy development timelines.
- Integration Challenges: Few organisations have existing systems that can integrate with any system, device, data store, asset or technology. IDC reports that 75% of industrial companies face significant challenges in integrating new technologies with their legacy systems.
- Poor Real-Time Data Capabilities: AI is only as good as the data it can access, train on, and leverage in real-time to make complex decisions. Businesses need a platform that’s able to ETL data from anywhere, collate, and make it available for analysis by both human and AI first.
- Complex Operations: The unique needs of different business units make it difficult to find one-size-fits-all solutions. This complexity often results in delayed or stalled AI projects.
These challenges can delay the implementation of AI solutions, preventing businesses from reaping the benefits of modern technologies.
Strategies for Accelerating AI Deployment.
To overcome these barriers and roll out AI faster, digital teams can adopt several strategies.
Leverage Integrated Platforms.
Using platforms that offer seamless integration with existing systems and real-time data processing can drastically reduce deployment times. For instance, platforms that support interoperability with legacy systems allow teams to bypass lengthy integration phases, enabling faster deployment, as well as coming with al the AI and automation capabilities that you will ever need, which you can layer on top of all your existing technologies (see ours for reference! Rayven Platform).
Opt for Custom Solutions.
Solutions that allow for rapid customisation and adaptation to specific business needs enable faster deployment. Integrated platforms can be used to create and tailor solution that address unique operational challenges, quickly, ensuring that the end solution is not only aligned with business objectives, but can be easily modified going forwards as operations, asset, data streams, and needs evolve.
Focus on Scalability + Flexibility.
Platforms that grow with the business and offer flexible pricing models ensure that AI deployment is both cost-effective and scalable. Scalable solutions allow businesses to start small, prove ROI, and expand usage incrementally without incurring prohibitive costs.
Utilise Comprehensive Support Services.
Access to end-to-end development services and 24/7 support can streamline the deployment process and address any issues swiftly. Comprehensive support ensures that digital teams have the resources and expertise needed to overcome obstacles and achieve rapid deployment.
Key Benefits of Fast AI Deployment.
Accelerating AI deployment offers several key benefits for industrial businesses:
- Speed to Value: Faster deployment means that businesses can start realising the benefits of AI sooner, improving operational efficiency and decision-making. According to a report by PwC, companies that rapidly deploy AI solutions see a 20% increase in operational efficiency within the first year.
- Real-Time Insights: Real-time data processing and analysis enable businesses to respond quickly to changes and optimise operations on the fly. This agility is crucial for maintaining competitive advantage in dynamic industrial environments.
- Custom AI Models: Businesses can train AI models on their unique data, providing tailored recommendations and optimisations. Customised AI models ensure that solutions are directly applicable to specific operational contexts, enhancing their impact.
- Automation + Integration: AI can be integrated with existing technologies, enabling automation and transforming legacy systems without added costs. Automation reduces the burden on human operators, allowing them to focus on higher-value tasks and strategic decision-making.
In a competitive industrial landscape, the ability to roll out AI solutions quickly can provide a significant advantage.
By leveraging integrated platforms, customisable solutions, and comprehensive support, industrial businesses and their digital teams can overcome traditional barriers and accelerate their AI deployment. Explore how adopting these strategies can help your business deploy AI faster and start seeing immediate benefits.
Rayven is an all-in-one real-time data, AI + IoT platform with unique industrial Generative AI and custom application building capabilities. We deliver an easy-to-use, codeless toolkit that anybody can use to integrate, ETL and analyse all their data in real-time; build custom workflows and automations; leverage Machine Learning and predictive analytics; create, train, and deploy custom LLMs bespoke to their needs; and create the interfaces, alerts and reports that frontend users need to optimise, innovate and create.
Get everything you need to succeed with industrial Generative AI today and build custom AI applications fast and affordably: speak to us now to find out more and get started.
References:
- McKinsey Study on Slow Internal Processes: McKinsey & Company. (2020). "The State of AI in 2020". Retrieved from McKinsey.com
- Gartner on High Costs and Lengthy Development Timelines: Gartner. (2019). "Predicts 2020: AI and the Future of Work". Retrieved from Gartner.com
- IDC on Integration Challenges: IDC. (2020). "Worldwide AI in Manufacturing Spending Guide". Retrieved from IDC.com
- PwC on Operational Efficiency Increase: PwC. (2020). "PwC's Global Artificial Intelligence Study: Exploiting the AI Revolution". Retrieved from PwC.com