Business executives face challenges in the data and AI landscape primarily due to the complexity and rapid evolution of technology, the vast amount of data available, and the need to align technological capabilities with strategic business objectives. At WalkingTree we do a lot of applied AI and provide customers with end-to-end data solutions, while on the Qritrim side, we do a lot deeper work, wherein we are enabling Artificial Domain Intelligence using our platform, Qi. After every implementation, our introspection has led us to one thing - Key Business Questions (KBQs) that we asked or missed.
KBQs are the most critical questions a business needs to answer in order to make informed decisions, achieve goals, and drive success. They act as a roadmap for analysis, strategy, and action. While many characteristics define the quality of KBQs, I find the RAISE approach as the most important one:
- Relevant: the questions must be directly linked to the business goals and objectives. They guide data acquisition, storage, and analysis efforts, ensuring that the data ecosystem within the organization is not just robust but also purpose-driven. This focused approach to data management prevents the common pitfalls of data hoarding and underutilization.
- Actionable: the questions should lead to concrete actions and solutions as a natural response. The power of data and AI lies in their ability to generate actionable insights. KBQs frame the analytical tasks in a way that the insights generated are actionable and directly applicable to decision-making processes. This actionability transforms data and AI from theoretical jargon into practical instruments of business strategy.
- Impactful: the KBQs should guide the enterprise in the strategic direction, resource allocation, and decision-making. It must have (or lead to) a significant impact on the business. Given that AI models are becoming increasingly accessible, the challenge is no longer just about building or deploying models but ensuring their impact is visible to businesses in a reasonable time.
- Specific: the questions must clearly outline the information or insights needed to make a decision or solve a problem. With the explosion of data in recent years, the lack of specificity will overwhelm the decision-makers. KBQs must guide organizations in filtering and prioritizing data that is most relevant to their strategic questions, thereby reducing the noise and focusing on valuable insights.
- Ethical: every progress with data and AI must ensure that it respects ethics and privacy. The KBQs must align with the company's core values and ethical standards to ensure that the business remains true to its principles and maintains its reputation.
While there is no doubt that quality questions lead to quality answers. The RAISE approach ensures that it is timely, contextual, value-oriented and effective.
Understanding KBQs importance using examples
The best way to visualize something is by using a few relevant examples. For this purpose, let's consider a wealth management (Financial Services) company serving high-net-worth individuals (HNIs) and aiming to maintain a personal touch while augmenting portfolio managers with AI. Let's just consider a couple of areas - namely personalized investment solutions and client engagement and relationship management. The following KBQs will play a crucial role in giving direction to the AI strategy:
Personalized Investment Solutions
- How can AI be used to accurately predict individual investor preferences and risk tolerance based on their past investment behaviour and market trends?
- What data-driven strategies can improve the accuracy and personalization of investment recommendations for diverse investor profiles?
- How can we leverage AI to continually adapt and optimize investment portfolios in real time while aligning with clients' changing financial goals and market conditions?
Client Engagement and Relationship Management
- What AI-driven insights can help identify opportunities for deepening relationships with existing HNIs and tailoring our communication and service offerings to their unique needs?
- How can we use data analytics to predict and prevent client churn by identifying dissatisfaction or unmet needs early in the client journey?
- In what ways can AI enhance the effectiveness of portfolio managers in providing bespoke advice and nurturing client trust and loyalty?
If we pick the first question "How can AI be used to accurately predict individual investor preferences and risk tolerance based on their past investment behaviour and market trends?" - what it drives is the following
- This question directly aligns with the wealth management company's goal of providing personalized investment advice, ensuring focused and effective data utilization.
- Insights from AI analysis enable portfolio managers to make informed, customized decisions on asset allocation and risk management for each investor.
- Accurately predicting investor preferences and risk tolerance enhances client satisfaction, retention, and investment performance.
- The question targets specific data points, guiding the company to concentrate on relevant information for building effective AI models. While the company may have an interest in several types of data, in order to answer these specific questions, perhaps we need data like Investor's Historical Transaction Data, Financial Profiles, Psychometric Assessments, Interaction and Communication Data, Social Media and Online Behavior and Responses to Market Event. In addition, we may need Broader Market and Economic Data, Industry and Sector Data, Geopolitical Events and Regulatory and Compliance Information to make a better decision.
Overall, this KBQ drives a more focused, effective, and responsible AI strategy for the wealth management company, leading to better client outcomes and business success. The associated team knows what they are supposed to achieve and from here on they can continue building the right experience for the end customers.
A few times, I have seen businesses asking questions like we have data about certain transactions, what can you do with that data for us? Internally, we conduct the exercise of what all Key Business Questions can involve the use of this data and to answer the given KBQ what else we need.
I like the idea of starting with these questions. The team will be able to find the answers if we have the right questions for them. While this is helpful in most scenarios, this is a great tool to get the AI and Data Strategy right for an organization.
