Simith Nambiar, Practice Lead, Emerging Tech, APJ, Rackspace Technology, tells us how businesses can overcome the challenges they experience with their Artificial Intelligence/Machine Learning efforts.
As businesses continue to leverage cloud-based compute technologies, attention is on the explosion of new data, AI and Machine Learning (AI/ML). Through the powerful combination of new data and AI/ML technologies, organizations can deliver superior customer-centric experiences, allowing them to understand their business environment like never before, resulting in the ability to drive new levels of efficiency.
In Singapore, the government has continued to invest in ambitious projects in key sectors to accelerate AI/ML adoption. For instance, through the National AI Program in Finance, financial institutions will soon leverage an AI platform to assess the environmental impact, identify emerging risks and enable financial institutions to make green investments. In the public sector, frontline government agencies will leverage AI capabilities such as natural language processing (NLP) to understand and process feedback and serve citizens better.
However, despite the growing spending towards artificial technology and Machine Learning initiatives, achieving AI/ML-driven successes is tough. Insights from a recent Rackspace Technology-sponsored study reveal that only 18% of respondents report mature AI/ML capabilities. Moreover, a majority of local respondents (75%) are still exploring or are struggling to operationalize AI/ML models.
More than one-third (32%) of respondents report Artificial Intelligence R&D initiatives that have been tested and abandoned or failed. The leading causes for these failures included poorly conceived strategy (43%), lack of data quality (36%), lack of production-ready data (36%), and lack of expertise within the organization (34%).
The failures underscore the complexities of building and running a productive AI and Machine Learning program. Upon closer scrutiny, businesses are struggling with their AI/ML efforts for several reasons, which include:
● Failure to get the right data to the right app or point-of-analysis in real-time – A company’s Machine Learning training is only as good as the data that is fed into the AI/ML frameworks and intelligent applications. If the data is bad, old or incomplete, the training will be poor and the answers and results generated will be equal to the quality of the data.
● Lack of organizational collaboration – Designing the right Machine Learning training and AI algorithms requires a holistic understanding of the data and processes being automated across organizational boundaries. Lack of collaboration often yields a poor implementation, lower-quality data and rejection of the applications/automation projects by key parts of the organization.
● IT and business process immaturity – IT and business processes should be well-formed to ensure the quality of data and seamless AI/ML execution. Also, AI/ML is best served with rapid
iterations and improvements in the data and algorithms – something that happens most effectively in a DevOps culture.
● Lack of expertise in mathematics, algorithm design or data science and engineering – Since AI and Machine Learning are built on high-quality, timely data and well-formed algorithms-representing the best in processes and models of the real-world – skills are critical. Finding the talent is tough in today’s market.
To overcome these challenges, organizations can take the following steps:
Step 1: Build the foundation
Start by preparing data and applications to migrate to the right multi-cloud and data architecture environments. This includes getting to know and understanding the business’ current environment and requirements and defining a roadmap.
Companies also need to ensure that data architecture supports the new application deployments appropriately and that ingress/egress fees can be minimized while also maximizing performance and availability. This is also the stage when database transformations and data warehouse migrations are implemented.
Step 2: Modernize the data architecture
Defining the modern data architecture, strategy and roadmap drive the transition into this phase. Focusing on modernizing the data architecture will help in defining, designing and building the data fabric. This phase includes pipelines and integration, data lakes and warehouses, and the analytics platform.
Step 3: Set the stage for more innovation
AI/ML prepares the organization for high-quality automation and predictive intelligence – driving innovation to the next level. At this stage, designing, training and deploying the models, and operationalizing Machine Learning (MLOps) will enable the business to deliver greater value to the modern cloud and data architecture built in Steps 1 and 2.
Step 4: Build intelligent applications
Finally, to start delivering strategic value and capability and realize the full value of this new cloud-based data fabric, intelligent applications that incorporate chatbot services, natural language processing, machine vision, recommendation engines, predictive maintenance and even actions can be deployed. With insights from Internet of Things (IoT) data, it is all possible now and forms a new foundation for the business.
Many organizations are still determining whether they will build internal AI/ML support or outsource it to a trusted partner. Given the high risk of implementation failure, the study also indicated that organizations (66%) prefer working with an experienced provider to navigate the complexities of AI and Machine Learning development.
In addition, according to Gartner, a dearth of IT skills among staff, wage inflation and a war for talent will likely push Chief Information Officers (CIOs) to rely more on consultancies and managed service providers (MSPs) to pursue digital strategies.
As business attempts to bridge the gap between their digital ambitions and internal resources and capabilities, many firms will increasingly rely on external consultants to meet their business goals.
With the help of data, organizations can take their resources further, delivering intelligent applications, services and results, enabling the business to make smarter decisions, improve collaboration, deliver new revenue streams and business models, and transform customer experiences.
