I spent a memorable evening at the AWS Generative AI forum in Taj Santa Cruz in Mumbai, learning and sharing with a packed audience of 300 business leaders from finance, manufacturing, technology, and more industries in India. The participants included distinguished experts: Deepak Dastrala, Partner & CTO at Intellect Al, Sreedhar Gade, VP of Engineering at Freshworks, and Rangarajan Vasudevan, Co-Founder & CDO at Lentra. A robust set of questions in a friendly, conversational style by our moderator Kiran Jagannath, Head of Financial Services and IT Enabled Services at AWS India, made it one of the best panels I have been a part of in a while.
While there is a significant interest in experimenting and implementing GenAI within enterprises, the audience was curious about how to navigate the new world of GenAI in their own companies. During the conference and at the dinner afterward, I was asked numerous interesting questions, of which, the top ones are listed below
Journey with GenAI: The Selection of Use Cases
Most companies are grappling with where to start in terms of their GenAI initiative. While it will depend on enterprises’ own priorities, technical sophistication, and perceived business values, the common framework that is useful for making a choice is to ask two following questions:
- Does this task occur in enough frequency?
This is important so that there is adequate training data available for training the models
- Are the outputs validatable through a human curation process?
This enables an enterprise to ensure that the GenAI outputs are consistent with what humans would expect.
In short, if the task requires effort to execute, but is relatively easier to validate, it might be a good candidate
Key Steps in Experimentation and Proof of Concepts (POCs): The Watchouts?
POCs, shortlisted by the above criteria, should include the following additional guardrails in the early days:
- Choosing an area where the business benefits are high or business outcomes are constrained by the current processes.
For example, a Direct-to-Consumer (D2C) brand running substantial marketing on social platforms such as Instagram, may today be constrained by a manual content generation process and can only do 2–5 posts a week. A GenAI system that enables them to create 100+ posts automatically matches up to the optimum marketing goals that would have been otherwise impossible.
- Avoiding direct interaction with customers where advice on a product or service is given
For example, while GenAI systems are capable of underwriting loans, offering such services through a chatbot to a bank’s customer, is a strict ‘no-no’, at least until the GenAI systems and customers have matured enough.
On the other hand, if the same customer queries about the suite of home loan products from the same bank mentioned in the above scenario, it is a good use case to pursue. However, even this shall be subject to business benefits, because it does not involve giving advice that can have a monetary impact.
What are the Key Risks of GenAI? What are the Principles of Governance Frameworks to Manage these Risks?
One can possibly write a book on risks posed by GenAI, with everything from Existential (human extinction after the takeover by uncontrolled GenAI) and societal (job losses) to Enterprise (operational, financial, reputational, etc.). My advice is to be aware of the first two but also recognize that the ones that an enterprise can focus on, are the risks that GenAI creates for its own operations.
Within that context, I recommend building a simple risk framework that is easier to comprehend, operationalize, and learn, over a period of 3–6 months. It can also be aligned based on how to apply it within the context of the early experiments. An overly complex risk framework that tries to anticipate all the possible permutations and combinations of the risks that can arise, would be overtly complex, unwieldy, and will likely not be governable as the whole field of GenAI itself is evolving rapidly.
How does an Enterprise communicate about its GenAI initiative?
GenAI, like any early technologies, has generated curiosity, and a sense of awe. While the earlier technological breakthroughs had limited public reactions (semiconductor in the 1980s) or curiosity (internet in the 2000s), this is the first technology that competes and is capable of producing, perceivably, human-level outputs that compete directly with modern jobs that define us and are closely related to the identities we hold socially.
The key pillars of any interaction with customers should be:
- Transparency: Let your customers know which processes are being run by GenAI, for example. If LLMs power your ChatBot or Recommendations, say so. For example, “this communication is powered by a Generative AI bot” is a good start.
- Choice: In the early days when the technology was still maturing, providing a clear choice (such as whether to be served by a GenAI algorithm), would provide customers an opportunity to decide if they want to “opt-out”, easing the fear. Extending the example above, “this communication is powered by a Generative AI bot. Click here to talk to a live agent instead”.
- Trustworthy Communication: A combination of the above, plus, open communications with the customers, including that at the company’s websites, and customer communications (email, SMS, etc.), helps build a rigid long-term trust in the brand’s credibility.
My best guess is that GenAI, which is currently evoking awe, shock, and fear, would be like the technologies that were integrated into enterprises during the past 20 years namely, internet, eCommerce, and cloud. There would be an adoption cycle where enterprises and customers will get used to the new technology and its nuances. However, as every new technology cycle does, there will be winners and losers. Being an early adopter, choosing experiments carefully, realizing business benefits through process changes, and taking the customers on the journey, in a transparent and trustworthy manner would be the key to ensuring an enterprise’s success.