6 key factors that simplify AI success roadmap for 2021 and beyond

We have crossed the halfway mark for the year 2021 and the world is still trying to catch up with the crisis. The situation has compelled us to look at everything from a new perspective, whether it is health, safety, ways to socialize, ways to do business and so much more. And the domino effect has changed the very nature of businesses, the way they function, operate, and most importantly, what value they provide. If the need for AI technologies wasn’t glaring enough already, now it has become an absolute necessity.

Businesses might have worked on action plans to compensate for the losses incurred due to the pandemic and accordingly propel business growth for the future. But how many are attuned to the existing scenario? An event of such epic proportions affects predictive models and projections, which means that businesses need a robust AI strategy to accommodate dynamic factors that can take into account such scenarios and adapt to the changes.

1. Trusted AI Is On The Rise

Responsible enterprise AI adoption of business processes has become the need of the hour. AI has matured enough to address well-known and typical concerns such as governance, control of automated systems, data privacy, biased decision making, etc.

Trusted AI is poised to be a game-changer and will ensure more ethical, reliable, and consistent AI solutions. It is imperative for organizations to imbibe and value ethical practices such as data security, bias evaluation, justified decisions, and user-first solutions. The old adage from Spider-Man ‘with great power comes great responsibility’ applies to AI technologies unlike ever before.

2. MLOps And AI Success Go Hand In Hand

MLOps, as an important set of practices, have taken off as part of the AI landscape, receiving significant attention from the business world. While DevOps encompasses tools, workflows, and automation to omit any complexity in software development, it is not sufficient to cater to the needs of machine learning models. The core reason is that ML is not just code, but it also covers data. To put it simply, machine learning requires writing algorithms and code on training data to produce the perfect ML model.

AI service providers will need to focus on MLOps methodology for effective AI adoptions. An ideal AI journey powered by MLOPs begins with data extraction and analysis, data preparation followed by model training, model validation, and model serving as well as model monitoring phases. MLOps is among the new innovations in the AI landscape, having a good scope of research on tools and practices involved. MLOps will get further momentum in 2021 by giving a boost to AI-centric developments.

3. Make-Buy Decisions Take Center Stage

With the proliferation of digital products and services, the market has seen highly specific tools and technologies develop into powerhouses, boosting technology adoption at a much higher rate than before. Every organization is different, and so are their AI adoption needs. During the initial phases of the AI adoption journey, businesses come across the pertinent questions, “Do we make or buy AI solutions?” “What about our existing infrastructure?”

The increase in AI platform availability in 2021, along with their ability to integrate with other systems, companies finally have the means to focus on their core objectives. They do not need to focus on making everything, instead they can buy or subscribe to several AI-based offerings, all aimed at specific outcomes. They can choose the vendors that they want without extensive lock-ins.

4. User-First Approach

Up until AI and its subsets started to make their way into mainstream applications, algorithms were made for systems and the data used to train these models were specific, resided in one place, not having anything to do with regular people . Now, due to the explosion of data and the ways and means to collect it, models need to be designed with a user-first approach, including best practices such as observability, justified decision-making, and monitoring practices.

In 2021, AI implementation is supposed to go beyond the ML black box approach with higher accuracy, explicability, and visibility. With an emphasis on user-centric aspects like expectations, user control aspects, needs, future requirements, and more, keeping AI solutions transparent and earning user trust is of utmost importance to make humane solutions. This will minimize unexplainable situations such as improper use of personal data and biased decision-making.

5. Model Drift Monitoring with Robust Processes

With artificial intelligence, a model will predict correctly if its training data precisely reflects the real world. Drift represents the change in an entity with reference to a baseline. This can happen when real-time production data shows significant differences from the baseline or training data-set. They are further classified as concept drift, prediction drift, label drift and feature drift.

The year 2020 saw one of the biggest drifts in AI models due to the global Covid-19 pandemic. The outbreak resulted in dramatic changes with several parameters such as shopping behavior, power consumption, vehicle collisions, eating patterns, and much more, leading to concept drifts for ML models. Take an example. Earlier one-way airway ticket booking could be seen as an indicator of fraud by an ML system. But in the Covid situation, one-way air ticket booking is seen as a routine practice.

6. Addressing Post-Pandemic Needs

2020 was the year of the world’s biggest health crisis and uncertainty. It taught us many new things such as a minimalistic approach, survival struggle, preparedness, and fulfilling needs with minimum resources and costs. In 2021 and beyond, AI solutions need to address the post-pandemic needs and priorities of businesses for sustainable development.

Whether things will go back to normal or whether we need to adapt to the new normal, the reliance on technology and the use of AI systems is only going to increase. Hence, the AI roadmap for success in 2021 and beyond, needs to keep these factors in mind.

4-Step Roadmap For Successful AI

Now, let’s take a look at a proven roadmap for AI success.

AI Success Roadmap

1. Strategy

The most important thing is to concentrate on the main business objectives of your company. This means that the AI strategy has to focus on the impact of AI instead of spending too much time on which technologies to use.

2. Build AI

Simply hiring the right people is not sufficient. AI needs to be built from the ground up, meaning that all the processes and practices need to keep AI at the center. It is important to build AI pipelines that deliver outcomes on a consistent basis.

3. Consume AI

Building AI applications is of no use unless they are being consumed by the right people. This requires connecting the end-user applications to your AI pipelines because this is where the impact will be realized and you will see the desired ROI.

4. Scale

Going into production, reaching this stage in your AI adoption is all about continuous relevance. Having provisioned the data, putting the models in place and retraining them over time with new data, you embark on a journey to achieve significantly higher business outcomes.

To Sum It All Up

The year 2020 taught us an important lesson — that we have to stay battle-ready to cope with adversities. Every organization should build a sustainable AI transformation plan for the next couple of years. AI success roadmap 2021 and beyond is incomplete without massive automation efforts in business processes as we experienced drawbacks of a manual process and its failures in 2020.

We tried to provide inputs on making your AI transformation a big success in 2021 and beyond. While AI is not magic, it requires systematization, AI adoption roadmap, thoughtful consulting, and a people-centric approach to achieve desired results.

If you have questions about AI or want to connect with us, drop us a message at hello@attri.ai

Originally published at https://attri.ai.