CIOInsider India Magazine
Human in the Loop: Balancing AI and Human Insight for Effective Decision-making
Nitesh Jain, President and Chief Operating Officer, C5i has over 27 years of expertise in spearheading various technology-driven changes. He has built his career by having helped numerous top manufacturers, banks, and retailers in implementing initiatives driven by strategic analytics.
Organizations around the world are adopting AI-enabled solutions to drive operational efficiency and enhance business performance across functions. A Gartner study has found that 37 percent of organizations have implemented AI in some form, with an additional 42 percent exploring its potential. These numbers point to the growing recognition of the value that AI adds to the highly competitive and evolving business landscape.
In India, a NASSCOM report has stated that AI has the potential to bring in $957 billion to the economy by 2035. India being pegged as a global AI hub by the government has further accelerated this trend. However, the integration of AI into business purposes poses a few challenges. A survey conducted by MIT Sloan Management Review and Boston Consulting Group revealed that 65 percent of companies are not yet seeing value from their AI investments.
We are still not at that stage where AI can take human-like decisions without any supervision for a plethora of use cases. For a foreseeable future; we believe a more nuanced approach to AI implementation – one that leverages the strengths of both machines and humans.
Implementing the Human in the Loop (HITL) Model
Organizations need to balance the capabilities of AI-driven solutions with the risks it poses to ensure effective outcomes. A Human in the Loop model combines the power and pattern recognition ability of AI with human perspective, creativity, and contextual understanding. Keeping humans involved in the decision-making process allows organizations to mitigate risks associated with AI bias, ensure ethical considerations are addressed, and capitalize on the unique insights that only human experience can provide.
Customer service with AI-powered chatbots is a case in point. While automated systems have improved response time and handling capacity, chatbots are not able to deal with complex queries and irate customers. A Human in the Loop (HITL) model can seamlessly transition from AI-driven interactions to human agents when necessary, ensuring customer satisfaction while maintaining efficiency.
From an operational standpoint, this approach offers several key advantages:
Enhanced accuracy: While AI excels at processing vast amounts of data and identifying patterns, it can make errors or miss nuances that a human would catch. Human supervision can significantly reduce error rates that would otherwise go unnoticed and perpetuated, improving the overall quality of decisions.
Adaptability: Since business environments are dynamic and unforeseen situations can arise at any time, humans are required to adapt to new scenarios and make judgment calls based on incomplete or ambiguous information – a capability that AI systems are still developing.
Ethical considerations: As AI systems become more complex, ethical decision-making also becomes increasingly challenging. The HITL approach can help ensure that AI-driven decisions align with organizational values and societal norms.
Continuous improvement: Human feedback helps refine and improve AI models over time, creating a virtuous cycle of enhancement.
Human creativity, empathy, and ethical judgment will remain crucial in many decision-making processes.
A successful HITL strategy requires organizations to consider several factors to define clear roles for both AI systems and human operators to ensure that each complements the other’s strengths. This may involve redesigning workflows to optimize the interaction between humans and AI.
Training, for instance, is critical for an HITL implementation to be successful. Employees need to be equipped with the skills—interpreting the outputs and providing meaningful input—to effectively work alongside AI systems. This often requires employees to change their mindset, encouraging them to view AI as a collaborative tool rather than a replacement for human expertise.
Data quality and management are also crucial since AI systems rely on high-quality, relevant data to produce accurate insights. Human operators can ensure data integrity, validating AI outputs, and providing contextual information that may not be captured in structured datasets.
Use Cases of the HITL Approach
AI systems can quickly analyze vast amounts of transaction data. In the BFSI sector, this capability is harnessed to flag potential fraudulent activities, but false positives—which are fairly common—can lead to customer dissatisfaction. Human analysts can mitigate this issue with their expertise and experience to make the final judgment on AI alerts.
In the healthcare sector, AI algorithms can analyze medical scans to detect potential abnormalities faster than humans, but the final diagnosis still rests on healthcare professionals. They take the patient’s medical history into account, along with other contextual factors before prescribing the line of treatment.
The manufacturing sector employs AI-powered predictive maintenance systems to analyze sensor data to predict equipment failures. Human technicians provide crucial input on factors that may not be captured by sensors, such as visual inspections or unusual operating conditions.
HITL also holds promise in the field of agriculture. AI systems can give recommendations based on its study of satellite imagery, weather data, soil sensors, crop selection and farming practices. However, a local farmer’s generational knowledge of their specific micro-climates and soil conditions can also prove valuable in adapting these recommendations to individual farms.
Assess, Identify, Implement
Embracing the synergy between human perspective and artificial intelligence can help organizations be at the forefront of innovation, driving improved decision-making, operational efficiency, and customer satisfaction in an AI-driven world. Considering the benefits, it is imperative for business leaders to develop HITL strategies that align with their organizational goals and values. This involves not only investing in AI technologies, but also in developing the human skills necessary to work effectively alongside these systems. However, organizations should conduct assessments of their current processes to identify areas where HITL can add the most value before embarking on an implementation plan. The findings and solutions should address technical, operational, and cultural aspects of this transition.
One thing is certain; as AI evolves, the role of humans in the loop will likely shift. But it is unlikely to disappear entirely. Human creativity, empathy, and ethical judgment will remain crucial in many decision-making processes.
