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One of the latest competitive advantages in today’s business digital landscape is the use of artificial intelligence (AI) and cloud computing efficiency. When done right, AI and cloud computing help drive business growth,  productivity and security without sacrificing costs or resource use. From innovative AI management tools to cutting-edge cloud optimization strategies, Forbes Technology Council members share some best practices below to help your business maximize performance and minimize costs.

1. Remove Silos To Prepare For A Quantum Future – “A quantum future is approaching, and leaders need to prepare now. It has the potential to drive massive productivity on its own, but when paired with AI, it can create a powerhouse of efficiency and security. Leaders need to start removing silos now to inform all teams about how strategic adoption can drive long-term success. – William Briggs, Deloitte Consulting”
2. Utilize AIOps To Predict Potential Issues –  “One holistic approach to enhance AI and cloud computing efficiency is automation through AIOps. Automation can predict potential issues before they impact business operations through data analysis, streamlined workflows and reduced risk of human error. As a monitoring and predictive capability, automation prevents downtime, optimizes resource allocation and enhances security and compliance. – Akhilesh Tripathi, Digitate”
3. Optimize Efficiency And Minimize Resource Duplication With Containerization – “Containerization is one process that optimizes resource efficiency by isolating applications while sharing the same operating system. This process also minimizes the resource duplication and reduces the cloud costs. By using this, companies can test their AI models without wasting their valuable resources. – Asad Khan, LambdaTest Inc.”
4. Reduce Waste And Costs With Serverless Computing Architectures – “One effective practice is adopting serverless computing architectures. This approach allows companies to run applications and services without managing servers, which optimizes AI workloads by dynamically allocating resources based on demand, thereby reducing waste and costs. – Savitri Sagar, Kenzo Infotech”
5. Emphasize The Importance Of Data Quality – “One best practice for enhancing AI is implementing data-driven reports with full traceability, educating all employees on the importance of data quality and fostering a data-first culture over time. This approach ensures data sufficiency for effective AI usage, transforming organizational decision-making and enhancing strategic alignment and efficiency. – Ged Ossman, Interf”
6. Look Toward The Future Of Space-Based Technologies – “In the future, space-based data centers will be used for cloud computing for terrestrial applications. These will be more efficient than earth-based data centers as they will be able to use passive radiative cooling and efficient laser-based networking, and can theoretically co-locate far more computing power than the terrestrial electrical grid allows. – Ezra Feilden, Lumen Orbit, Inc.”
7. Explore Neuromorphic Computing For Complex AI Tasks – “Neuromorphic computing is an emerging technology that mimics the human brain’s structure and function, allowing for more efficient AI processing with lower energy consumption. Neuromorphic chips can help companies perform complex AI tasks at the edge, speeding up data processing and reducing cloud computing demands. – Sriram Panyam, DagKnows”
8. Build In Escape Hatches For Common Workflows – “Companies should consider building in “escape hatches” for common workflows. For example, if you deploy an internal AI chatbot for HR questions, you could anticipate the most common requests (e.g., request a pay stub or tax docs), and rather than have an AI decide what to do, direct the user to the process you have for pay stubs and tax docs. – James Ding, DraftWise”
9. Leverage Unified Solutions Over Point Solution-Based AI – “Like past technological waves, AI is setting up to be a race to the middle. That has played out with the cloud, and now we get less from each cloud investment, as people are overwhelmed by how much we’ve thrown at them. Leveraging unified versus point solution-based AI that sits on top of our cloud software can improve the efficiency of our cloud investments and ensure we get durable differentiation from AI. – Michael Haske, Krista.ai”
10. Develop A Well-Defined Data Catalog – “A well-defined data catalog with rich metadata and semantics is crucial for spearheading GenAI-based solutions in companies. A data catalog enables more sophisticated data analytics and AI capabilities. It helps in automating the data preparation process and supports more advanced techniques like semantic search, which can be leveraged to build more powerful and effective GenAI solutions. – Faisal Fareed, Amazon Web Services”
11. Use Smaller Language Models For A Lower Carbon Footprint – “Smaller language models are more efficient, secure and able to scale economically with a lower carbon footprint. These closed models can be pre-trained on specific domains with AI workflows for the most optimized business processes while bringing a curated collection of information specific to your enterprise. – Venky Yerrapotu, 4CRisk.ai”
12. Segment AI Applications And Workloads Based On Cost – “AI is a diverse technology with many applications that will entail many different deployment options that will be constantly changing as edge AI and cloud AI continue to evolve. Tech leaders should segment AI applications and workloads based on the cost (which includes IT or CSP’s energy cost) and determine where they are best run on-device, across edge infrastructure and cloud. – Leonard Lee, neXt Curve”
13. Consider Distributed Computing – “Distributed computing is certainly one of the key emerging technologies companies should consider to improve efficiency in their AI and cloud computing resource consumption. AI, especially generative AI and advanced AI research, can be incredibly computing-intensive. By applying advanced distributed computing techniques, businesses can orchestrate AI tasks to appropriate resources and reduce costs. – Humberto Farias, Concepta”
14. Look Into Federated Learning To Reduce Centralized Storage Needs – “One emerging technology to improve AI and cloud computing efficiency is federated learning. It enables models to be trained across multiple decentralized devices or servers, reducing the need for centralized data storage and massive computational power. This approach not only enhances data security but also optimizes resource usage by leveraging the computing power of edge devices. – Jiahao Sun, FLock.io”
15. Consider LLMOps To Optimize LLM Use – “Large language model operations (LLMOps) is an emerging category that can help provide for more efficient and optimized use of LLMs. Running these models can be expensive, which can make it challenging to achieve sufficient ROI as well as scale. LLMOps are essentially a framework that focuses on efficient resource allocation, tracking models, evaluating responses and improving inference. – Muddu Sudhakar, Aisera”
16. Educate Employees On The Nuances Of Proper Prompts – “Education on prompt engineering best practices is the most important way to improve companies’ efficient use of AI. AI is rapidly evolving, but in today’s environment, chatbot interfaces and the prompts that are used to get valuable output from today’s LLMs are critical to deriving value. Most employees treat them like search engines, do not understand the nuances of prompts and need training. – Michael Keithley, United Talent Agency”
17. Focus On The Fundamentals – “Zeroing in on the fundamentals of your business can be a powerful way to optimize resource consumption. By revisiting basic principles like clearly defining project goals, streamlining data pipelines and right-sizing cloud resources, you can ensure your AI and cloud investments are truly delivering value, facilitating efficiency gains and allowing teams to do more with less. – Todd Fisher, CallTrackingMetrics”
18. Leverage Computing Power From Endpoint Devices – “Not everything needs to be done in the cloud. There are a lot of computing resources sitting idle at every organization, every single day. Leveraging the computing power from endpoint devices brings back distributed computing at essentially no cost. – Elise Carmichael, Lakeside Software”

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These days, many businesses are moving their systems to the cloud from on-premise solutions. The reason for this is because of the many benefits cloud computing has to offer. Key offerings of being on the cloud include databases, infrastructure, platforms, software and storage capabilities that scale to seamlessly meet different operational requirements. One such benefit for being on the cloud is the utilization of artificial intelligence (AI) and business process automation. Will Quinn , Director of Global WMS Strategy at Infor, shares an article on Informatique News explaining the circumstances of deploying AI on the cloud versus on-premises.  Quinn notes that AI deployment methods brings to light multiple issues and makes it possible to evaluate the advantages and disadvantages while highlighting the central role of security in defining the optimal approach for companies. whose activity relies on AI. Quinn state that the Cloud represents the ideal solution in most cases for 10 reasons below, as well as the disadvantages of AI being deployed on-premises:

Benefits of AI and Cloud 

1. Cost-Effective Scaling: Cloud services facilitate cost-effective scalability of machine learning models without significant upfront investment, promoting flexibility.
2. Reduced initial investment: AI deployed in the Cloud frees companies, particularly those with limited resources, from investing in expensive hardware.
3. Great ease of deployment: the speed of deployment offered by the Cloud streamlines configuration processes, which promotes innovation and accelerates the launch of new projects.
4. Improved security: Cloud service providers are investing in rigorous security protocols to offer their customers cutting-edge encryption and authentication mechanisms.
5. Accessibility and collaboration: AI deployed in the Cloud facilitates access and encourages seamless collaboration between multiple users, thereby increasing project efficiency.
6. Compliance: adopting Cloud services that comply with current standards ensures compliance with data protection regulations.
7. Always up-to-date information: Regular updates and patches from cloud service providers reduce vulnerabilities and minimize the risk of data breaches.
8. Distributed Backup: Because data is stored in the Cloud across multiple locations, the risk of data loss due to physical disasters or hardware failures is minimized.
9. Expertise and monitoring: Cloud service providers employ security experts specializing in cyber monitoring and continuous threat response.
10. Scalability and interoperability: Integrating AI in the Cloud into existing systems seamlessly ensures seamless operation and scalability.

Disadvantages of AI deployed on-premises

• Higher initial investment  : implementing AI on-site requires significant investments in hardware, software and qualified personnel.
• Limited scalability  : Scaling on-premises infrastructure poses some challenges, especially if there is a sudden increase in computing needs.
• Maintenance and upkeep  : the responsibility for maintaining and upgrading equipment induces additional operational costs.
• Technological obsolescence  : Rapid advances in AI equipment risk making on-premises configurations obsolete more quickly than solutions deployed in the Cloud.
• Resource dependence  : Robust security requires the use of skilled operators, which puts a strain on company resources.
• Physical security concerns  : On-premises setups are vulnerable to physical threats such as theft attempts or natural disasters.

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Infor Nexus^TM, the single-instance intelligent supply chain network platform providing unparalleled visibility and collaboration, recently announced a new product to help companies map their multi-tier supply chains and provide evidence to support compliance with regulations such as the German Supply Chain Due Diligence Act and Uyghur Forced Labor Prevention Act (UFLPA) and the French AGEC Law and to document substantiation for product claims.  Per the press release, this new application called Map and Trace, was co-developed with industry leader Burton Snowboards to help companies “connect the dots” between suppliers, their suppliers, and the transactional records supporting the chain of custody. Further, the process of mapping the suppliers and collecting the documentation is highly manual and first requires participation from the tier 1 supplier. Moreover, Map and Trace is the first application that will help Infor Nexus customers achieve greater transparency and traceability across the product lifecycle. Up next, Infor Nexus is expecting to launch a broader traceability solution, providing a digital product identifier and helping comply with regulations such as the EU Digital Product Passport.

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