Top 10 Enterprise Gen AI Dilemmas Confronting the C-suite

The publicity for Gen AI has settled as organizations have begun to lay the groundwork for seamless adoption.

According to the research company Gartner’s May 2024 report, 45% of 2,500 professionals said that the popularity of ChatGPT forced them to invest a lot in AI, 19% said their business is in production mode, and 70% are already using Gen AI.

But there are critical decisions to be made, dilemmas to be resolved, and responses to be given to queries. This blog lists the top 10 Enterprise Gen AI dilemmas facing C-suite leaders.

Open or Closed Gen AI models

The world came to know about Gen AI only through licensed models such as Open AI’s GPT or Google’s Bard (now Gemini).

But the Geminis and ChatGPTs indeed helped end-users understand Gen AI’s immense possibilities for the larger public.

But soon came the emergence of open-source models such as Meta’s Llama. These models are built on a shared architecture, reveal the underlying codebases, and are pretty much transparent about the data being trained.

So which way Enterprises would sway when it comes to embracing these foundation models? Open-sourced or licensed – which would benefit enterprises in the long run.

Various aspects related to performance, accuracy, ethics, explain ability, and IP protection would enable stakeholders to decide whether to go with closed or open-sourced models.

LLM or LMs

Large language models are a group of foundation models trained on extensive volumes of data making them capable of generating natural language and various forms of content to do a variety of tasks.

They are infinite energy spenders, which indicates only the multi-national or largest companies can invest in training and maintaining these models with billions of parameters.

Could it be that these enterprises are better off adopting smaller language models that are comparatively energy-efficient, cost-effective, and even privacy-friendly?

Among these smaller models, domain-specific LM is fast getting traction because of the obvious advantages – you can run your small model on modest hardware yet build powerful customized models trained on proprietary data.

Industries such as healthcare, legal, and finance could immensely benefit from these domain-specific smaller language models.

Explainable AI or Black Box AI

Have you ever asked ChatGPT as to how it arrived at a decision? While the results of ChatGPT can be analyzed for their accuracy and relevancy, the inner activities of how particular responses were generated were never clear.

This could be a challenge for enterprises when they work with LLMs as these models given their vast architecture with several layers of neurons, make it hard to trace the decision path.

For any input, the model may not explain how it arrived at a specific result. This is called a Black box AI.

Again, on this front, smaller language models (SLMs) are better poised to allow human users to comprehend and trust the results created by their algorithms.

This justifies the capability provided by smaller language models that let developers ensure that the system is working as planned.

SLMs may go even further to help enterprises meet regulatory standards.

Don’t overlook the Enterprise layer

The enterprise layer is crucial to building successful Gen AI applications. Remember one can’t take the-box foundation models such as Open AI’s GPT or Anthopic’s Claude, and instantly create Gen AI applications for their enterprise.

They have to manually build an enterprise orchestration layer that enables the infusion of Gen AI into their normal applications.

Would organizations invest considerable resources into building this layer? This is where Enterprises must work with effective partners to create a customized business layer that truly connects enterprise applications with Gen AI models.

ROI dilemma

Enterprise leaders emphasize that Gen AI offerings must demonstrate a clear business value proposition. But for a technology that is in its nascent stages, yet with tremendous transformative potential – how can enterprises measure value?

Should organizations overlook ROI in the initial stages and instead focus on how Gen AI infuses innovation and helps them in strategic positioning and differentiation?

Or should enterprises be shrewd enough to prioritize investing in areas that can bring easy wins, and gradually get into complex projects?

Garbage in Garbage out

The adage cannot be truer in the age of generative AI where the reliability of Gen AI output majorly depends on the quality of data being sent as input.

How can organizations ensure the highest data quality standards when training Gen AI models? How seamless/difficult would it be for organizations to ensure that their custom AI abides and operates based on their internal data?

This situation is further compounded when we realize that on average, organizations only capture 56% of the data they create.

Even more alluding is that about 77% of the data collected is either obsolete or trivial.

So, Organizations, if at all, they wish for seamless large-scale Gen AI deployments, must thoroughly capture, classify, and clean data before feeding into their AI systems.

Trust issues

Enterprises are concerned about the accuracy of Gen AI outputs. No doubt, generative AI is changing the scope of work, but still, many people think that it can result in unemployment or the loss of jobs.

Together, this cumulative mistrust could pose a barrier to Gen AI enterprise adoption.

But fortunately, these concerns have not stopped organizations from embracing Gen AI in their pilots or small-scale experiments.

But if these pilots are to be scaled, then organizations have to build trust in numerous dimensions across worker empathy, transparency, input/output quality, etc.

Sophisticated Virtual Agents

Can chatbots go all the way to completing the task apart from making sensible conversations? The ChatGPTs and Geminis have shown us how their question-and-answer systems can give rich contextualized answers.

Can it plan a trip or even book a ticket? The dawn of sophisticated virtual agents is not far and that would transform customer experience in ways never seen before.

Ambiguous regulations

The ambiguity in the regulatory environment continues to prevail.

In Dec 2023, a provisional agreement on the Artificial Intelligence Act was reached, prohibiting indiscriminate scraping of images to create facial recognition databases, biometric categorization systems (with potential for discriminatory bias), “social scoring” systems, and the use of AI for social or economic manipulation.

Because of the impending election, there is still time for America to decide on AI governance and security, but that could only do little to stop the stellar developments continuing to happen in the country.

Yet organizations cannot afford to be complacent when it comes to implementing guardrails for trust and security.

Shadow AI

Shadow AI typically arises when a seemingly enthusiastic employee, in the desire to learn or complete an action through an AI application, feeds sensitive information to a public-facing model and thus exposes business secrets.

Conclusion:

C-suite has to understand how businesses can give the tools required to combine genAI easily into their workflows.

Also, how they can nurture cross-functional partnerships, break down silos, and allow varied expertise to merge around GenAI techniques.

Generative AI can convert the operations of businesses with significant collisions in organizational domains within the value chain.

This technology is developing at rocket speed while C-level executives are still going through its risks and business values.

It might notably affect the workforce, and the effect on local communities and particular groups could be extremely negative.