Embrace Ethical and Governance Standards To Reap the Rewards of Gen AI

Businesses can unlock the full potential of Generative AI safely and sustainably if they follow five basic steps to mitigate risks and foster trust, according to SoftServe’s Director of AI and Data Science, Maya Dillon.

Speaking at a client workshop with experts from our partners at Google Cloud and NVIDIA, she said firms that want to reap the rewards Gen AI offers must have strong AI and data governance as a foundation. They must also have a core strategy to place ethical AI as a strategic imperative. This means putting in place these five key pillars before embarking on a Gen AI journey:

Prioritize data ethics
To accelerate innovation, make solutions more resilient, and build trust.

Appoint an AI leader
To ensure consistency, accountability, and progress.

Empower the workforce
To create efficiency, loyalty, and differentiated solutions.

Conduct regular AI audits
To build commercial rigor, compliance, and transparency.

Partner with ethical AI providers
To deliver aligned value, innovation, and maximum impact.

Reputational risks

Traditional AI excels at solving well-defined problems through prediction, classification, and automation, while Gen AI stands out in creative tasks that require generating new, original content. Both play crucial roles, but Gen AI brings more complexity, innovation, and unpredictability to the AI landscape. Therefore, if these foundations are not embraced, Dillon said firms risk not only missing out on the greater efficiency and other benefits Gen AI offers but also face potential financial and reputational damage.

Areas that must be given particular attention include mitigations to ensure fairness and remove bias, transparency, and explainability to build trust, resilience, and guardrails to safeguard data privacy. Dillon said these steps should be accompanied by policies to manage intellectual property risks and governance frameworks to cover data quality, lineage, and regulatory governance frameworks.

Mastering AI

SoftServe Enterprise Solutions Principal Paul Fryer took this a step further, saying there are simple ways firms can approach this challenge.

Industry feedback confirms there has been a tremendous amount of activity and investment in AI and Gen AI strategies, but that an “AI expectation gap” is appearing. Fryer said this is because many initiatives remain stuck in proof of concept (PoC) stages as firms focus on process improvement with an approach that says, “doing something is better than nothing.”

Multi-stage models

NVIDIA is already delivering vast improvements on the hardware and software framework side. But at a holistic level, we are seeing AI roll out everywhere, with advanced models running directly on the edge, in human wearable devices, with dedicated hardware backends, and in the cloud. Advanced models are on every device at every stage where data flows, with a big shift towards a growing use of multi-stage models.

The starting point is the creation of a model to manage a process that leverages other problem-specific models. For example, a finance pipeline can use a high-level model to understand the reconciliation process and required steps in the finance tool. Then, leverage a specific model to understand an invoice and another to understand the quote.

Next-gen hardware

NVIDIA has also recently announced significant improvements in next-generation hardware, where performance is simply breaking Moore’s law. We are now seeing models double in size and requiring 4x the compute each year, with the proliferation of AI “co-processors” and APUs in CPUs to offload AI tasks. The current AMD Zen5 dedicates 10% of the CPU core to AI tasks.

Flexibility and PoC

All this means that if your tasks can work on a cloud platform, it’s easy, cheap, and fully managed. While, if you need to run computer vision on the edge, you can run simple models on accelerated CPUs or offload to high-performance GPUs. This flexibility is unlocking use cases in production, so let’s look at some workloads in production.

To move forward, it is important to understand the PoC, and where you want to be post-PoC, to identify any potential gaps in the path to production. The question should not be whether you use AI in your business, but where to use it and how to integrate it into your processes.

Integrate, build, or buy?

If the process you are looking at is not unique to your business, start by exploring the market. Can you buy the skills or capabilities you require, or activate them through an enhancement subscription such as Salesforce Einstein?

If the challenge you want to improve is core to your business, but the problem is common, such as image tagging, adding metadata, summarization, and document comparison, then you might look at Gemini models that are available off the shelf. With the addition of a few API calls you might be able to make significant enhancements to your team’s performance.

Finally, if the problem is specific to you, then it’s probably correct to consider fine-tuning an existing LLM with advanced prompt engineering. The key here is to evaluate the scale of the problem you are addressing.

This will show how specific that problem is to your business and how unique the challenge is that you are trying to solve. Once established, it’s crucial to understand the PoC and post-PoC objectives to identify gaps on the path to production.

Climbing the AI mountain

In many ways, the path to AI production is like climbing a mountain. Prototyping is the easy part. Spinning up a notebook, creating a first agent, or rag application based on some samples is also straightforward. Everyone can do that.

The toughest challenge lies in the final step — getting to production. Without it, customers won't see the value in Gen AI, and your potential revenue gains will take a hit. Why is this part hard? After working on this topic with customers for the past two years, we found some common obstacles they encountered when trying to reach production:

A call to responsible action

Gen AI has the power to reshape industries, redefine innovation, and revolutionize the way we work and live. However, this power brings with it a profound responsibility — to build and scale AI systems that are ethical, transparent, and aligned with both organizational goals and societal values.

As we’ve explored in this workshop, successful AI adoption hinges on balancing ambition with accountability. Whether it’s integrating robust data governance, embedding fairness and transparency, or leveraging partnerships with leaders like Google Cloud, NVIDIA, and SoftServe, the path forward requires intentional action.

Now is the time for organizations to take the lead by implementing best practices, empowering their teams, and fostering a culture of ethical AI development. By doing so, we can collectively ensure that Gen AI not only drives business impact but also contributes to a more equitable, sustainable future.