By Jitendra Putcha, EVP & Global Head – Data, Analytics and AI at LTIMindtree

There is no denying the mind-blowing power and potential of generative AI (GenAI), especially for businesses. The technology can serve as a trusted virtual assistant, handling data analysis, administrative tasks, research, and more. Even if technology teams haven’t yet adopted GenAI, they’ve likely already thought about how it could transform their workflows and capabilities. According to recent research from LTIMindtree, most GenAI pre-adopters intend to use the technology to improve efficiency (78%) and enhance customer experience (75%). GenAI has empowered us to reimagine what’s possible with technology.

But just as GenAI introduces new capabilities, it also introduces new risks: security breaches, regulatory challenges, lack of transparency, and the spread of misinformation. Early adopters of GenAI may have learned of these risks the hard way. If business leaders are looking to implement GenAI and expand its use cases, they must ensure they do so carefully.

Here are five steps that technology leaders can take to embrace mindful AI, leading to business transformation and superior client service.

1. Establish AI principles and framework

Begin by defining a set of core ethical principles that will guide your AI initiatives. These principles should align with your corporate values and address key ethical concerns such as fairness, accountability, transparency, and privacy. Building and upholding these principles will require sponsorship and commitment from your executive leadership team along with support from legal and security teams. The principles and framework should be tailored to your industry and the specific applications of AI within your enterprise, along with any regulatory compliance requirements that may apply. This framework will be the jumping-off point for all AI usage, and it’s crucial for it to have bespoke alignment with your specific organization.

2. Choose suitable technical interventions

At the heart of the any AI system is well-annotated data. Technology teams must evaluate their data sources for quality, bias, and representativeness when fine-tuning or training their models. Anti-bias measures must also be made a priority. The integrity of any GenAI tool is only as good as the large language model (LLM) it’s trained on. If the underlying data is biased in any way, GenAI will perpetuate that bias in its output. To eliminate bias, technology teams must ensure the LLM data is raw; any analysis or interpretation could alter its integrity. As technologists train their GenAI models, they should monitor feature importance, visualize decision boundaries, and test against fairness metrics.

If organizations are using third-party solutions, they should weigh their options carefully. Be sure to pay close attention to data diversity, transparency, and interpretability. For instance:

• Interpretable models: Opt for architectures like decision trees, linear models, or rule-based systems.
• Feature attribution: Use methods like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to understand feature importance.

3. Foster an ethical AI culture

It’s crucial for all GenAI users (even potential users) and stakeholders to receive hands-on training through both formal sessions and day-to-day guidance. This instruction must be offered on an ongoing basis and updated continuously to keep pace with the ever-evolving technology.

One key focus area for training must be AI hallucinations, a term used to describe GenAI’s output of incorrect information. Unless users are aware that these hallucinations sometimes occur, they may be inclined to implicitly trust every piece of information GenAI produces.

Another important avenue for mindful AI training involves educating teams about all the ways GenAI can add value to their day-to-day workflows. Whether for marketing, sales, customer service, or more, GenAI can offer so many capabilities that users might not know where to start. LTIMindtree’s recent research found that 80% of U.S.based GenAI users cite selecting use-cases as a critical challenge of the technology. GenAI training sessions can help point users in the right direction by offering inspiration for how to wield the powerful technology.

4. Invest in proper talent and expertise

Having the right talent in place is crucial for any organization considering a GenAI implementation. Skilled experts help facilitate change management, maximize the utility of GenAI, and instill trust in the advanced technology. This has proven to be the case with organizations who have already adopted GenAI-over half (57%) of early GenAI adopters said having access to skilled personnel was a critical success factor, according to LTIMindree’s recent research.

As teams are getting up to speed with GenAI, technology leaders should be sure they have a talented change management team in place. These experts help pave the way toward GenAI success, smoothing speed bumps as they go. They serve as the go-to internal resources who can help answer questions, provide use-case recommendations, and troubleshoot as needed. Technology leaders might consider hiring experts who understand concepts like explainable AI. Other roles like data scientists, ML engineers, and domain experts can collaborate to implement and interpret mindful AI effectively.

Prompt engineers are also vital for GenAI success, as they have a deep understanding of how the technology works. They help teams frame queries with the precision necessary to get the desired output. If the technology isn’t working as expected, prompt engineers are the experts to consult.

5. Routinely audit and update AI

Because GenAI-like all advanced technology-is constantly changing, it’s imperative that businesses keep a pulse on potential vulnerabilities and safeguards.

Technology leaders must regularly dedicate time to learn about recent GenAI incidents, whether through topical newsletters, relevant media outlets, or their own research. By seeking knowledge on how GenAI can fail or be exploited-as well as how to counter these weaknesses-leaders can effectively fortify their own organization’s tools.

It’s important that GenAI’s underlying LLM is regularly updated as well. Users will access the database with every query they make, and information must be regularly refreshed and fact-checked. Whether for internal or external use, GenAI’s data output must be as accurate and updated as possible.

With AI, mindfulness matters

GenAI has been touted as “AI for everyone,” and it will prove to be a monumental feat of human ingenuity and innovation. The technology has already shown its efficacy across countless use cases. The more advanced technology becomes, the more carefully and thoughtfully it must be implemented. The leaders need to take a mindful approach so their teams can take full advantage of all the technology has to offer.