11 Questions Business Leaders Need to Ask Before Preparing AI Strategy
Undoubtedly, Artificial Intelligence (AI) can offer organizations a substantial competitive advantage if used in the right place and in the right circumstances. There is also considerable pressure on organizations to go the AI route for fear of losing an edge to competitors. This pressure can easily be felt by business leaders who need to craft and implement enterprise AI strategy. In a recently conducted survey (from Oct’17-Nov’17), majority of business leaders indicated Machine Learning (ML) and AI as their companies’ most significant data initiative for next year. 88 percent of respondents indicated that their company already has, or has plans to, implement AI and ML technologies within their organization. But, it is still not clear whether AI would bring productivity benefits or not, or whether they will have any impact on an organization’s revenues. So, what should organizations be thinking about? What questions company leaders should be asking before they push forward? Here are 11 key questions answers of which they need to find out: 1. Do you really need AI to solve this problem? Some automation and analytics use cases are simple enough that they can be solved with much simpler procedural code rather than building and maintaining an AI model. Enterprises need to figure out what they are trying to do and decide if AI is worth the investment. 2. How will AI improve your Customer Engagement? Businesses should leverage AI to deliver the right message at right time for right customer to significantly improve the customer engagement. By identifying low hanging fruit, high impact opportunities they can transform their brand for optimal customer engagement and make immediate and tangible difference in customer relevance. If cost reduction is an important driver, think about AI-powered chatbots to reduce mundane customer service tasks. 3. What is the organisation’s business case? If AI is deployed simply as a experiment without identifying and solving a specific business problem, then it would turn out to be “short-lived-no business value proposition” as leadership will not see any return on investment and people will simply stop using it and the entire technology will be dismissed as “not working for us.” 4. Do you have the necessary data? This is really a significant and important factor to be taken care of. Using AI involves being able to train a model on data. So, companies planning to use AI in coming future would really need to start thinking about data collection now without which AI will not be as effective. Enterprises need to understand the fact that AI is only as good as your data and goals allow it to be. Without framing a robust key performance indicator (KPI) and performance targets you would find yourself lost in corpus of data and wouldn’t understand the right way to optimize your actions to achieve desired results. 5. Do You Trust The Data Sources AI Will Use? One of the key questions that organizations need to answer is whether their data and date sources are suitable for AI. They should view data as a strategic business advantage and give attention about the data they’re collecting, how they’re storing it and how they can use it to create a personalised experience for their customers. 6. Is Your Data Architecture Suitable? While data is important, it is not enough. Organizations need to build a robust data strategy and ensure they have the right and effective data architecture in place. 7. Can Existing Data Management Systems Support AI? Can the existing data management systems hold up under the new load of artificial intelligence. AI systems use data as fuel and to have an effective AI model, this data shouldn’t be incomplete, inaccurate, or biased. That said, don’t wait for the data to be perfect — current AI is perfectly capable of determining what data works and what is too unreliable to use. 8. What Are the Consequences of Getting It Wrong? Sometimes, AI is all about statistics and finding the right correlation. In such cases, similar to humans, AI might produce wrong results depending on the data quality. So, business leaders need to think whether they want to implement AI in a process with a lot of variability that may have a lower accuracy rate and could have major consequences after getting it wrong. 9. What Are the Risks? AI comes in two distinct flavors — transparent and opaque, and both have very different uses, applications, and impacts for businesses and users in general. In some instances, businesses will need to employ a transparent form of AI that will explain the logic and exactly how they reach certain algorithmic-based decisions. 10. How Will This Impact Workers in Your Organization? AI’s rapid business adoption is expected to replace part of function of an employee’s role which might develop a negative perception and resistance for this change. Sustained AI adoption would require business leaders to involve employees and their line managers right from the start. Effective learning programs would help them take this change in a positive manner. 11. Will AI Integrate With Your Current Stack? AI solution should be integrated as part of a broader process not as standalone technology solution. AI, process and people should work together to make the business ecosystem more efficient and enhance the productivity, results and revenue.
