Organizations recognize that they need a systematic approach to “operationalizing” AI in order to drive AI success.
The pace of digital transformation has massively accelerated with artificial intelligence (AI) technologies and is already transforming every aspect of business. With massive improvements in storage systems, processing speeds, and analytic techniques, we’ve reached an inflection point where AI and cloud technologies are enabling tremendous sophistication in analysis and decision making.
But the reality is, despite the hype around AI, the majority of companies are still either kicking their heels or simply failing to get AI strategies off the ground.
But why do so many AI projects fail?
The road from data to successful data-driven, AI- and ML-powered projects is no straight line. There are many variables that go into building effective artificial intelligence, which makes it difficult to prescribe set steps that will work well every time for every company. It is known that launching pilots is deceptively easy but deploying them into production is notoriously challenging. This is the reason why despite the early adoption of AI by many organizations, few have managed to reap consistent business value on their AI investments.
See also: Putting More Intelligence into Business Intelligence
Reasons for AI failure – The Known and Unknowns
AI is still seen as risky business — an expensive tool that’s difficult to measure and hard to maintain. Organizations often start AI projects based on competitive pressures, fearing they will be left behind if they are not investing in AI. Not understanding what AI can be best used for is a top reason for failure. Other challenges can be attributed to reasons such as lack of a cohesive artificial intelligence strategy, no collaboration between business and IT stakeholders, poor organizational alignments, slow and complex implementations, and lack of continued C-suite commitments.
But the most overlooked challenges when it comes to operationalizing AI are related to data, processes, and people.
- Data: The predominant approach to developing AI systems has been to collect large amounts of data and train complex algorithms on it to produce results. But in the real world, data itself is not ideal and is prone to severe data quality issues. Data silos are a reality, and there will always be multiple levels of dependencies when it comes to organizing or accessing data. Many AI projects deliver erroneous outcomes due to biases in data, algorithms, or the teams responsible for managing them. Clean, machine learning-ready data is a prerequisite for AI and analytics projects to succeed.
- Processes: While companies develop grand AI and analytics road maps, many fail to build a robust data strategy and set up data governance systems. This results in a flimsy foundation that is more likely to fall apart once the AI projects move to production. The fact that an AI model is executed doesn’t mean it is being managed. It is essential to have discipline and management capabilities to ensure that AI models can be legally traced, monitored, and secured and that analytical assets can be deployed within operational processes in a repeatable, manageable, secure, and traceable manner.
- People: With increased availability and accessibility of data, everyone in the organization, not just the data scientists and data engineers, must understand the data and its nuances. This is where most AI projects lose the battle, as AI models give no clear explanation as to why or how they arrived at the results. Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decision-making on how they deny loan applications or classify potentially fraudulent activities. Operationalization of ML models needs a robust explainability framework and a clear path of interpretability for people across the data literacy spectrum.
Going forward, businesses are likely to embrace a more fit-for-purpose AI that is deeply embedded into organizational culture and operating models.
Driving Success by Operationalizing Artificial Intelligence
Organizations are quickly recognizing that they need a systematic approach to “operationalizing” AI in order to drive AI success. Here are the key strategies to make your artificial intelligence dream a successful reality:
1. Re-design your AI pipeline for agility
If you look at different stages of the AI pipeline – data wrangling, data management, model training, model management and deployment, and business applications, it requires efficient streamlining, effective communication, and swift collaboration with everyone: the data engineers, the data stewards, BI specialists, all the way up to DevOps and engineering resources. This time-consuming process can easily be automated with MLops techniques that bridge the gap between different teams and help operationalize AI solutions at speed and at scale.
MLOps is the singular connective tissue that links the distinct pieces of the pipeline to deliver the right value through business applications. The advent of MLOps has seen new-age businesses moving their data centers into the cloud. This trend has shown that companies that are looking for agility and cost efficiency can easily switch to a fully-managed platform for their infrastructure management needs. Through continuous integration, delivery, and pipeline automation, MLOps can enable teams to iterate quickly. This means shorter time-to-market and more successful AI deployments.
2. Re-scale your Infrastructure stack for efficiency and transparency
Going fast for fast’s sake is only going to get you the proverbial speeding ticket, or worse. The key lies in underpinning robust computing and modeling power with reliable infrastructural integrity and trust and transparency in AI models to make AI implementation a true success.
- Make operationalizing AI algorithms fast and iterative with fewer resources. Use cognitive APIs, containers, and serverless computing to help simply AI deployment.
- Shift from a monolithic stack to a microservices-based one which gives greater visibility and control of data processes.
- Apply data virtualization to improve (unified) access to data warehouses, data lakes, or other internal or external data sources.
- Select a platform that supports distributed D&A ecosystem and acknowledges the different lineage and curation of assets.
- Understand end-to-end data flows to ensure transparency of data lineage.
- Start including explainability in higher-order analytics (diagnostic, predictive, or prescriptive analytics) to improve the adoption and usage of AI solutions in the business.
3. Re-align your AI expectations
By setting achievable expectations, organizations can have an honest debate about what AI-powered success looks. This way, by being realistic about AI’s potential but also its limitations, and by developing an AI strategy and processes to accurately manage expectations, organizations can create positive AI-powered experiences for stakeholders and build trust in an AI/ML-powered and data-driven algorithmic approach to business. The companies that learn, adapt, and mobilize quickly will be the frontrunners in the space; they’ll be more equipped to reach AI production and, ultimately, the holy grail of profitably.
Steering towards AI success
Gartner has predicted that by 2024, 75% of organizations will shift from piloting to operationalizing AI. This change in momentum will be driven by greater accessibility to data and the development of highly flexible models to adapt to specific business needs. The “old-fashioned” cultural, process, and organizational challenges might be hard to change, but some comfort can be taken in knowing that the routes have been charted; the challenge is in steering the ship and avoiding the rocks.
