Despite 2020 being a difficult year for many, businesses across the world are accelerating their AI adoption and implementation. IBM’s Global AI Adoption Index 2021 validates the same as it shows amplified adoption of AI driven by the impacts of the pandemic, changing business needs, and the ease of accessibility and deployment that comes with AI.
Another survey by PWC, AI Predictions 2021, shows that 86% of the participating US companies agree that AI is becoming the “mainstream technology” in 2021. Why not? The benefits of AI range from improved decision-making, customer experience to revenue growth or higher value work.
But, not every business is able to maximize their returns on AI. According to IBM’s Global Index, the top three barriers to AI are lack of skills and knowledge (39%), growing data complexity and data silos (32%), and lack of platforms and tools to develop effective AI models (28%).
Moreover, organizations often lack clarity over the core objectives they aim to achieve through AI. If you are an IT leader who is planning to push forward the AI agenda, we are here to help you get it right. Below are the 10 questions we believe every business leader must ask themselves before jumping on the AI bandwagon.
1. What challenges do we plan to solve with AI?
Defining the challenges is the first step towards building a successful future driven by AI. Many organizations pursue AI for AI’s sake without knowing what they aim to achieve with this technology. Many make the mistake of mapping the opportunities of AI with their organizational goals while it should be the other way round, resulting in a tunnel vision.
For small enterprises, some use cases are so simple that they do not require building and maintaining AI models. A simple procedural code would be sufficient to solve the problem. Therefore, it is crucial to figure out the exact needs and determine exactly how AI can solve them.
2. How much revenue do we aim to achieve with AI?
There is a growing adoption of machine learning capabilities to improve business processes, for e.g., chatbots are being used to screen incoming customer support requests. While that’s useful, the true potential of AI or ML remains untapped unless it is integrated with the core value proposition of the organization.
3. How do we picture ourselves in the marketplace?
You must also view it from the angle of risks and rewards. In the coming years, the world will witness business leaders rising to the occasion by selling their own AI solutions and services in the marketplace. Early adopters may not nurture the ambition of developing AI algorithms but they might partner with leading AI drivers to drive revenue and efficiency. Where you want to see yourself in this picture can determine the AI roadmap for your organization in the long run.
4. How do we envision AI improving our customer experience or engagement?
While conversational AI is taking the world by storm and becoming an integral part of the digital transformation strategies, the wide scope of AI in improving customer experience goes far beyond the realms of chatbots. Tech leaders are combining AI and ML to gather and analyze social, historical, and behavioral data facilitating better understanding of the customer segments. AI is also being implemented for real-time decision-making as well as predictive analysis.
According to Capgemini’s AI and Ethical Conundrum, 54% of customers are using AI on a daily basis to interact with businesses and chatbots are not the only way. Digital assistants, biometric scanners, facial recognition are some of the customer engagement use cases that are increasingly becoming popular and being considered as trustworthy.
5. Do we have the necessary data and capabilities to develop and feed the AI model?
Just a couple of years ago, the word AI was used to create a hype in the market. But, today, a business must be realistic in its approach to adopting AI. Without having the right IT capabilities or data warehousing, an AI strategy is doomed to fail as the quality of the AI solution is directly proportional to the quality of data fed to the system.
So, ask yourself these following question before you embark on your AI journey-
- a) Do we have enough data to feed an AI system?
- b) Are the data sources reliable?
- c) Do we have a robust data architecture?
- d) Is the data digitized?
- e) Is the existing data management system suitable to support our AI initiatives?
6. Are there any ethical risks that we should keep in mind?
The automated decision-making enabled by AI brings with it a number of risks related to systemic bias. In recent years, several brands have already faced the wrath of consumers when certain systemic bias went wrong. Hence, every AI adopter must carefully consider the decisions AI will make and how that will impact people’s lives. Accordingly, human judgement should be included. Initiatives are already underway to build a global guidance on AI ethics.
7. What’d be the consequences if the strategy or model fails?
It’s true that AI is about sophisticated and highly intelligent algorithms and statistical correlations but that does not eliminate the chance of error. In fact, AI can give wrong results based on the quality and quantity of data it’s been fed. Hence, if you have processes with high variability and low accuracy rate then consider the risks, both financial and operational, if there’s an error.
8. How to find the right talent to create, manage and monitor AI solutions?
More than technology, AI is about people. That’s why it’s equally critical to hire the right talents who will not only build models but create solutions to fulfill the exact business needs. In order to keep pace with the rapidly evolving market, early adopters have made securing the next-gen AI talents including data scientists and data engineers, a top priority.
But external hiring alone cannot be a long-term solution. Reason why organizations are increasingly focusing on building AI talent in-house by looking for underutilized talent beyond the IT team, upskilling your in-house team with available public content, bridging the skills gap with domain expertise, and allowing your in-house teams to experiment and apply their knowledge in practice.
9. Do we need an AI Center of Excellence?
As AI adoption is still in its formative age, it is a wise idea to develop a Center of Excellence focused on AI to allow subject matter experts to report directly, provide focus and dedication, and create a unified approach to practices and patterns.
10. How to integrate AI with the overall strategy?
Instead of making AI a stand-alone part of the overall business strategy, it is important to make it an integrated solution to ensure maximum productivity and outcomes. To determine that, identify the areas where AI can possibly collide with existing business processes, the problems that can arise, and how to mitigate that.
Are you planning to implement AI in your business?
