Generative AI is new to us as we have always used AI in an auto-pilot mode where we just consumed it as a black box – a sort of a genie that provided insights, analytics, predictions, corrections, etc., based on previous interactions.
That being the case, wouldn’t it be great to have GenAI as a collaborator or a co-pilot? Read on to understand how we can partner with it.
Adding Value with GenAI for Business Growth
Generative AI is a magic wand from the world of Artificial Intelligence that has taken us by storm, thanks to the years of research that has gone behind it. The reason it is garnering much attention in recent times is that it operates as a co-pilot with us, helping to generate various types of content, including text, imagery, audio, programming code, and synthetic data.
Organizations need a clear and compelling generative AI strategy to become an industry leader in the next 5 years. Where does one start the Generative AI journey and the transition?
Impacting Different Industry Segments
The forecast for global spending on AI systems is expected to reach $110 billion by 2024 and as it continues its rampant growth in multiple dimensions, a particular subdomain called generative AI is revolutionizing many industry segments. It can create human-like content that is apparently holding everyone’s attention.
A study by McKinsey, reveals that generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries. It says that the precise impact is dependent on various factors such as the mix and importance of different functions as well as the scale of a particular industry’s revenue.
As this technology is multidimensional in its outlook and adaptation, it is undoubtedly making a huge impact across these major industry segments.
Retail: By processing and taking clues from vast amounts of customer data, generative AI can craft hyper-personalized product descriptions, reviews, marketing content, fashion designs, and recommendations, thereby vastly improving the shopping experience.
Retailers can also make use of the other AI segments like virtual and augmented reality to adjust store layouts according to customer preference, enabling virtual “try on”, etc. Generative AI can also help them with dynamic pricing based on various trends and data points.
Insurance: Risk management is a major area in insurance where generative AI can make a huge impact with its predictive insights and can transform this whole process. This is achieved by simulating a large amount of customer risk-taking behaviors from the past and generating new policies and coverage, tailored to their specific need. This helps in coming up with much more accurate risk profiles, better pricing, and thus ensuring profitability and customer satisfaction.
E-commerce: This being retail’s cousin, stands to benefit from everything like the retail industry but more with a digital footprint. This includes generating landing pages, catalog generation, product images, new product recommendations, offers and promotions, reward management, credit schemes, etc. – all these are hyper-personalized to the user.
Healthcare: Generative AI is taking the form of a personalized healthcare chatbot that provides vital clues on the overall health of a patient. Patients and caregivers can receive recommendations on the best healthcare facilities, health insurance details, etc. based on their specific needs.
Fintech: In an environment that is saturated by multiple apps including super apps, the differentiating factor is the amount of personalization that organizations can provide to their user – be it to find a low-interest loan, suggest the most appropriate insurance, or help them plan their investment as per the market conditions.
Expediting your AI journey
Organizations can be at any phase of consolidation, and the new crop of GenAI can significantly accelerate their competitive edge, even if they lack deep AI or data science expertise. Although customization still requires expertise, they can adopt a generative model for a specific task with low quantities of data, either through API or prompt engineering.
Evaluate the strategic vision: Leaders should recognize and believe in the transformative and game-changing potential of generative AI. It is imperative to understand its implications for that specific industry and align its capabilities with the organization’s business goals.
Upskill/Reskill the employees: The power of GenAI lies in the collective capabilities of the workforce of the organization and hence tapping into the resource pools’ knowledge, skills, and creativity to drive meaningful change.
Unlock your data goldmine: AI is all about data, particularly crunching out intelligence from past data. This data may be stored in different modes – files, databases, web, etc. and needs to be consolidated and streamlined for further processing through data cleansing, structuring, augmentation, etc. An important aspect to consider is ensuring the security of the data, masking PII (Personally Identifiable Information), and other crucial data as this may trigger issues with compliance and audit.
Generative AI technology can also potentially produce a series of new business risks like misinformation, plagiarism, copyright infringements, and harmful content, therefore, it’s important to explore the ethics and security by design moving forward.
Ethics and Security by Design
It involves proactive planning and intentional implementation and assessment at every stage of the development lifecycle. You can do it by following the principles below.
Stakeholder Engagement – Before starting the AI project, engage with diverse stakeholders, including ethicists, user representatives, and domain experts. Their insights can help identify potential ethical or security concerns related to the application’s usage.
Ethical Framework Development – Draft a set of ethical guidelines tailored to your AI application’s goals and potential impacts. This can be based on existing ethical standards but should be tailored to the specific AI application and its nuances.
Data Privacy and Integrity
- Use data that has been obtained ethically and with explicit consent from the owners.
- Anonymize sensitive data and use encryption to ensure data confidentiality.
- Ensure data quality and integrity by ensuring there is no biased or incorrect data that may lead to unethical outcomes.
Transparent Algorithms – Opt for models that offer transparency and can be explained. While complex models like deep neural networks can be more accurate, they often act as black boxes. Tools and techniques like SHAP, LIME, or attention mechanisms can aid in making these models more interpretable.
Bias Testing and Mitigation – Continuously test your AI models for biases in all dimensions. You can consider using tools like AI Fairness 360 that can be integrated into your framework through design. If biases are detected, refine your model or dataset to mitigate them.
Robust and Secure Models – Implement techniques to make your AI models robust against adversarial attacks by using standard security protocols to safeguard against unauthorized data access or model tampering.
Continuous Monitoring – Once deployed, the AI models should be continuously monitored for ethical and security lapses. Automated tests can detect if the model starts behaving unexpectedly or if newer biases emerge.
Feedback Loop – Have a mechanism in your application to allow end-users to provide feedback on AI decisions and outcomes, especially on bias reporting.
Transparency with Users – Communicate to users about how your application uses AI, the type of data consumed, and its decision-making process. This ensures users are aware and can make informed decisions about using the AI-enabled application.
Review and Audit – Periodically review the AI application with an emphasis on ethics and security. This can be done internally or through third-party auditors. It ensures that the application remains compliant with evolving ethical and security standards.
Emergency Protocols – Have a protocol in place for shutting down or rolling back the AI part of the application in case of severe ethical or security breaches. Your application should be as detachable as possible with the AI infusion.
Ethical and Security Training – Provide training to AI developers and users about the ethical and security aspects of the application. This ensures that everyone involved understands their role in maintaining the application’s ethical and security standards.
Through this disciplined approach, organizations can adopt generative AI both strategically and responsibly.