The future of enterprise AI integrates with and supports developing, tracking, and updating strategic plans. It can require organizational change to support new capabilities, including reskilling staff. Finding strategists to contribute to this effort can take time since they may worry about being displaced by AI. But there are ways to overcome this challenge.
Artificial Intelligence as a Service (AIaaS)
Artificial intelligence (AI) can improve customer satisfaction, creativity, and decision-making while drastically altering business operations. However, implementing an enterprise AI strategy is challenging and requires significant time and resources. AI as a service (AIaaS) addresses these challenges by outsourcing the management of AI-related tasks to third-party vendors. AIaaS solutions offer various benefits to business users, including cost-effectiveness, rapid deployment, and increased productivity. They can also help reduce operational risk and enhance security. AIaaS solutions can be used for various purposes, including predictive analytics, data visualization, and automation.
AIaaS is an essential component of the evolving AI infrastructure ecosystem. North America dominated the global AIaaS market in 2022, and the region is expected to hold a substantial share by 2032. However, several concerns regarding the security and transparency of AIaaS need to be addressed. These issues include privacy, liability, and transparency. Additionally, there are issues with vendor lock-in and the need for interoperability. In-depth research and preparation are needed. As a result, many companies still need to be more cautious about implementing AIaaS.
Machine Learning Optimization Platforms (MLOps)
Many organizations have large amounts of data but need more infrastructure, systems, and processes to use them effectively. It’s true for those using machine learning to drive business value. With MLOps, the ML training and deployment cycle is operationalized and managed so models can be deployed at scale to deliver valuable insight and products. A robust MLOps platform includes features like a streamlined development process, collaboration tools, version control capabilities, and robust monitoring to ensure efficient model creation and deployment. It enables enterprises to harness the full potential of their custom MLOps platforms for competitive advantage. While MLOps is similar to DevOps in that it focuses on automation, the scope of MLOps is much broader and encompasses ML model lifecycles, including data preparation, experiment tracking, modeling, and production monitoring. It ensures optimal model performance and enables autonomous model operations. In addition, a comprehensive MLOps platform provides mechanisms for model explainability and interpretability. It is critical for building trust in the decision-making process. It also enhances transparency and accountability in AI applications.
With data now generated at a speedy rate, firms need to have the ability to collect and analyze this information quickly and accurately. This information enables organizations to create competitive advantages and increase their overall value. Early conceptualizations of Big Data revolved around three central characteristics: volume, velocity, and variety. Volume is a term used to describe the vast amount of currently available data. This trend has only gotten faster as the Internet of Things (IoT) grows; by 2020, it is predicted to reach 40 zettabytes or more.
Variety relates to the plethora of sources from which companies can access big data and the range of formats it can come in, from structured numeric values to textual documents. Companies need to be able to determine which data represents signals versus noise and the varying levels of accuracy and relevance. The veracity of big data is also an essential factor to consider, as the integrity and reliability of this information can impact the success of a company’s strategies.
It may be one of the less exciting aspects of AI/ML adoption, but fine-tuning custom ML models will remain essential to successfully harnessing this new technology. It’s how a pre-built model becomes optimized for the needs of a specific industry, domain, or set of problems. It’s locking in data differentiators, aligning with a company’s culture and values, and achieving transformative results. Developing this fine-tuning capability requires significant hardware, robust storage solutions, and high-speed connectivity. Fortunately, open-source tools are mature enough to enable engineers to build and manage custom ML models.
Moreover, open data licenses are helping to commoditize training datasets. It helps democratize AI/ML development, accelerating breakthroughs that proprietary barriers would have impeded. It’s an approach that echoes the open-source model used by Wikipedia, Creative Commons, and countless other initiatives, and it will be critical for overcoming the artificial intelligence arms race. It’s the only way we’ll prevent AI from becoming an exclusive privilege of the very few.
When deploying AI, a clear and well-defined strategy is essential. Those who have done so see improved products, cost savings, and increased decision-making and business operations efficiency. AI will transform several business processes and enable new opportunities by automating tasks, delivering insights, and creating personalized content for the user. It will also impact organizational structures. For example, a dedicated business unit could be established to identify AI use cases and set roadmaps. It would also standardize practices and facilitate communication, accelerating AI adoption.
In addition, a centralized platform to manage the data and optimize the model is essential for scalable AI models. Automation is critical to a successful AI strategy, so it is essential to consider platforms that support DataOps and allow for automated data orchestration. By doing so, enterprises can avoid the time and expense of manual data management and ensure that their AI is working effectively. They can also ensure they’re getting the best value from their investment. By identifying and indexing the value realized on the outcomes, stakeholders can better intellectualize AI’s potential and shift the culture toward an AI-inspired, transformative mindset.