Introduction
Artificial Intelligence (AI) has had a formidable presence in many major industries over the past few years. The adoption of AI in businesses across multiple industries like manufacturing, hospitality, marketing, airlines, e-commerce, retail, etc. has been increasing steadily. As of 2021, the global AI market was worth 327.5 billion dollars. However, businesses are at different stages of adopting AI into their workflows. Many top companies have already begun integrating AI solutions and technologies into their core processes, still many small businesses and enterprises are at the beginning stage of experimentation and development.
In this post-pandemic era, many businesses have now started actively adopting AI and automation. According to PwC, in 2021, nearly 52% of enterprises have fast-tracked their AI adoption plans, with 85% claiming that it has become a core technology for them. There is a clear difference in the approach taken by the new companies embarking into AI: they’re not getting into it blindly. AI-ML is a much more familiar tech now and instead of it just being the new trendy hot topic it has become vital to many organizations.
The problem with AI adoption
To achieve complete value from any AI-related solution, the business should be able to utilize, consume and adopt the solution wholly, with all its components. Compared to most other technology transformations or adoptions, the journey to AI adoption would be a drawn out and messier deal. It will also be impacted by the kind of data, industry, use case, tech, and skill of the personnel involved.
AI Adoption is a continuous process, because the AI model, along with the data fed into it, needs to be continuously monitored across various stages of development, deployment, and ongoing adjustments and maintenance. When data changes with time, the AI function changes with it, as new patterns and hidden relationships are imbued into the data. Therefore, continuous monitoring and adjustments are essential.
As businesses begin to understand and appreciate the value provided by AI, the most important challenges so far have been with deployment, but due to recent hardware and software advancements, a lot of solutions have come out to tackle the deployment issues. Also, we now have access to success stories and guidelines from organizations who are leaders in AI adoption, to learn and adopt the best practices.
Various stages of AI adoption
Traditionally, there have been only three stages to AI adoption, starting with use-case discovery and strategizing, to development of the model/solution, and finally productionizing and deployment. Companies have already heavily invested in these stages but are still unable to produce the full value and complete adoption of the AI solution. This is because most businesses are missing the fourth stage, and perhaps the most critical: monitoring and operations.
Operationalizing an AI solution after it has been deployed to production is the final stage towards adoption. This includes continuous monitoring of data, model, performance, system, compliance, etc. with the help of dashboards and observability suites. At a more advanced level, data and model explanations in real-time, feedback mechanisms to the model, remediation of data, auto-retraining, and more, are also included in this stage.
The AI operations team would also be responsible to make decisions based on this real-time monitoring. For example, if there is a drift in data from the one on which the model was initially trained on, then retraining of models would be crucial. This is where assumptions of a single model throughout the production stage, and static unchanging data in real-time are broken, because real-world data is dynamic and can be unpredictable. This puts a focus on improving the robustness and stability of AI systems, which is another crucial part of the operation/monitoring stage.
The last mile
The operational stage would be essential to AI in general, in the coming few years. The companies that have already completed those first 3 stages towards adopting AI, and have deployed their solutions to production, must now start focusing and investing solely in the operation and maintenance of these solutions. Only with continued monitoring, testing, and real-time feedback, would the solution deliver continuous value.
There will be an increase in demand for the tools and products, and more importantly people who have the right knowledge, that would help in operationalizing AI. Looping in an entire data science team every time there is a drift in the model or data, is not sustainable in the long run, nor is it scalable. After all, the goal of adopting AI solutions is to automate the process, rather than increase complexity and human dependencies.
We’re heading to a world where it shouldn’t take a year to productionize and adopt AI and ensuring continuous delivery should be a norm. The solution? Invest in proper infrastructure that is operations and business adoption centric, and at the same time take proactive measures to build or procure an operation suite that can handle the last mile of adopting AI in a sustainable way.
