Importance of AI Strategy for Brands in the Post COVID world

Global shocks and crisis have historically resulted in novel disruptions and innovations - altering business and technology landscape. SARS outbreak in 2003 and Global financial crisis in 2008 had resulted in the growth of e-commerce, the rise of companies like Alibaba and as well as American Express and Starbucks, towards the growth of digital operating model and ushering in digital revolution that we witness today. COVID-19 is no different as it has acted as a catalyst for digital transformation, adoption of large-scale AI models and platforms, automation and analytics, robotics, IoT devices and remote connectivity.

Customer experience in the next normal will be dependent on the ability of businesses to deliver omnichannel, device agnostic and hyper personalized solutions to customers. The ability of brands to predict demand patterns, market trends, understand and monitor customer behavior and purchase patterns will be highly connected to their ability to leverage data analytics, AI and machine learning combined with human judgment for making informed decisions and prompt delivery of products and services.

According to Fortune Business Insights 2020, the global artificial intelligence market is expected grow up to $267 billion by 2027.

Tech-savvy customers, increasing digital demands, growth in digital purchases, need for speed to market and elevated customer expectations have made it crucial for brands to plan, devise and roll their AI strategy in the post COVID world scenario to both remain competitive and provide superior customer experience. From designing smarter products to revolutionary solutions and optimization of business processes, AI has the potential to transform everything. This is the reason every business today needs to leverage, adopt and deploy AI solutions in some form or the other.

What should form the core principles of a sound AI strategy for any brand that is looking to create its own niche in the post COVID world? Organizations tend to overestimate the value proposition offered by AI and underestimate the complexity of taking the project from prototype to production. This often creates hindrance for data and AI team to manage the expectations of leadership and tend to fall for over sophistication, leading to project failures. This calls for crafting a robust AI strategy that is realistic and directed towards achieving business goals. Let’s discuss some of the main elements of a robust AI strategy that every business needs today.

Alignment with Business strategy and KPIs

Getting the most of any AI strategy would require business leaders to align their AI strategy with strategic business needs and goals in line with core business KPIs. The first step in every AI strategy is to review the present business needs, market trends and evolving customer expectations. Post COVID world scenario would increasingly demand automation of services, faster time to market, fluid and seamless customer experience, remote connectivity and digital transformation. Keeping the business strategy aligned, organizations can roll out their AI strategy and apply artificial intelligence to crucial business needs and requirements. This would mean that business leaders would realign and rethink their business priorities in line with present and future needs and then identify how AI can help them achieve the same in delivering their strategic goals.

A survey by NewVantage in 2020 revealed that 9 out of 10 leading businesses have an ongoing investment in AI.

Determining Strategic AI priorities

Post alignment of your business strategy, you can start to identify the right processes or candidates for implementing your AI strategy. Your top business priorities, strategic business goals and the problems that you would like to solve in immediate or long term can then be initiated through adoption of AI in those areas. You can start by phase wise implementation of AI with first set of use cases that are impactful, deliverable and measurable. The initial use cases could involve improving and streamlining your business process and functions and to make them more intelligent. You can start automating repetitive tasks and mundane activities which will help you free up resources for more productive and mission critical tasks. In the successive phases, you can start automating your manufacturing processes and discover opportunities for bigger AI adoption priorities by asking relevant questions pertaining to the need for optimization and deployment of AI.

Calibrating Data strategy

Data plays the primary role in every AI strategy and implementation. There could be no implementation of AI without the help of data. Thus, creating a data strategy is vital for every AI strategy. Data forms the basic premise that feeds the AI algorithms for making smart decisions. You would thus need to gather all data pertaining to the process you need to automate- data relating to all information that is relevant for improving the business process. Also, you would not just need high volumes of data but also data that is relevant. Finally, your data strategy would need to incorporate the strategic ways to collect relevant data and ensure data relevancy and privacy.

According to a Gartner estimate in 2018, it was predicted that a whopping 85% of Big Data projects are likely to fail.

According to Gartner, by 2022, all personnel working on AI projects will be required to demonstrate competency in and understanding of responsible AI practices.

Ethical and Legal Groundwork

AI may sound highly intelligent and sophisticated approach to your business process but it often involves ethical and legal complications owing to both its application, usage and its acceptance in general. Privacy concerns and regulations have become strongly stringent in today’s scenario with even tech-giants and big companies facing legal summons and international law suits. Hence, legal implications, privacy regulations and regional and international laws must be taken into cognizance while drafting any AI strategy. Any bias or discrimination has to be accounted for and customer’s and stakeholder consent at all stages has to be taken care of. This is why tech giants like Microsoft, Amazon, Google, IBM and Facebook have formed a group (Partnership on AI) to research and advocate on the ethical use of AI.

Yet, according to latest AI statistics by Salesforce in 2019, it was shown that 62 per cent of consumers were willing to use AI and share data for an elevated experience.


As you embark on your journey towards a robust, more solid, ethical, data and outcome driven AI strategy, you will will have to focus on a culture of learning and continuous improvement. Time and again business leaders would need to ensure that AI strategy is all about not letting your efforts to be lost into hype and fancy but to be aligned to core business goals. And finally, a robust data strategy is imperative for the success of your AI implementation goals, giving you competitive advantage, boosted business outcomes and an accelerated time to value.

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