Generative AI is bound to change how we interact with various software we use in day-to-day life. AI is already being used to provide differentiated brand experiences but with Generative AI (Gen AI) this will become more personalised finetuning all aspects of digital interactions with the customers.
At times, the software may have to go beyond the brand reach into areas outside its purview if it wants to offer solutions to customers. this may require offering complete solutions to the customer even if it requires collaborating with outside partners and developing the infrastructure to connect everything into one comprehensive solution.
Gen AI is capable of generating voice, text, videos, speech, music and even software codes. This capability along with a clear understanding of customer needs helps in delivering the necessary solution to the customer quickly and efficiently.
This model does away with the traditional style of choosing between multiple options in the software, narrowing down filtering the available choices and shortlisting the most favoured option. Today with GenAI programs, the user can simply type in, in a conversational tone, what he requires and the program gives multiple options to the user. The more specific or focused the query the better the quality of the answer provided. this changes dramatically how we interact with the software and is more easily accessible to everyone. This helps reduce the time to interact with the applications. It enables companies to build their brand value proposition.
A broad journey with broader boundaries.
To be effective in using GenAI requires focusing on the broadest view of one’s brand and the data available, of the various possibilities that one can imagine as well as the downsides they may involve.
Bring data together:
This would require collecting all possible data on the customer from across the organisation, where it may reside in different silos. These may reside in dissimilar systems and it is for the IT experts to integrate the various data into a cohesive whole. AI teams would have to invest time to clean the data and ensure that the data governance norms in place are adhered to and identify key features that define the data.
Combining one’s owned data with public data besides other AI tools data would help AI programs understand the data and give a better predictive answer to questions from the user. it is important to ensure the accuracy of third-party data before integrating it into owned data.
The rules established in AI systems are critical.
AI applications should have guidelines to ensure that they respond with an appropriate answer to queries from the customer. It should also be capable of responding to queries that are beyond its means or inappropriate.
In GenAI this becomes even more critical, and the best approach would be to start small and offer specific solutions and learn the best way to answer for other cases that are not so well defined. this would need human intervention to understand the nature of the queries and respond to them and build a sufficient bank of responses for the AI program to build general conclusions.
Deliver end-to-end solution.
Customers would usually ask for specific solutions and seek the most cost-effective way to get the work done. Companies can build data and the strategy for execution to help customers.
The power of interconnected systems helps companies extend their reach much beyond their brand and product offerings. When companies offer services based on a simple command, these reflect the real goal of the customer and the solution they need and not just one component of their need that your company may satisfy.
Differentiate via your ecosystem.
To meet the broader requirements of the customers, companies may have to enter into new partner relationships. It is important to ensure that the partners you choose to be associated with are trustworthy and capable of providing timely access to information and a clear-cut agreement on the commercials.
Prioritise safety, privacy and transparency.
The way the data is managed and the results that the customers get becomes part of your brand. There could be extreme cases where wrong results may occur and this should be managed quickly in a transparent way. Companies should also ensure that no customer data is shared with other partners without customers’ express consent.
The risks of data leakage increase as the GenAI programs take root. It is necessary to build safeguards on how the product managers are managing the systems. Testing data for bias as well as knowing the origin of data and checking for copyright and privacy risks is an integral part of this exercise. It is also important to limit where the information will be shared and at the same time be ready to face legal challenges that are bound to come up.
The risks are bound to multiply and the cost of managing risks could become huge. However, the capabilities of GenAi are massive and the accuracy is bound to improve as the usage increases. The speed and personalisation opportunities could make it worth the cost. This will also put pressure on all participants to manage efficiently.
Generative AI Will Change Your Business. Here’s How to Adapt.
by David C. Edelman and Mark Abraham
HBR 2023/04
R2G 