We got our first glimpse of generative AI in 2022. Less than two years since, the technology seems to have taken over the world, even becoming an enthused topic of discussion at board meetings and family dinners alike. Today, companies are actively seizing the opportunities it offers – a fact evident by the millions of Microsoft Copilot licenses and generative media platform subscriptions purchased by companies around the world. In fact, according to McKinsey’s ‘State of AI in Early 2024’ survey¹, “Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year.”

But for all its hits, generative AI has also had its fair share of misses. From wildly inaccurate summaries of search results to support chatbots hallucinating critical information, some of the world’s most prominent companies have struggled to ensure the smooth integration of generative AI into their services.

As a result, for businesses that want to uncover generative AI’s potential, effective adoption isn’t a long journey but a very complicated maze – one where implementations can feel like dead ends and too many paths can cause volatility. The question is, what do you need to navigate it with confidence?

Mapping out the hedges

At Virtusa, we believe that generative AI adoption requires an approach that’s as methodical as it is experimentative. Because when you can’t look over the hedges, the next best solution lies in knowing what corners to turn so you can make your way through the maze with maximum efficiency.

The complexities of implementation

As enterprises embark on their generative AI adoption journeys, skill gaps, and strategic complexities could surface as some of the most pressing concerns. Generative AI isn’t a one-size-fits-all solution for companies. Tailoring it to your specific problems can mandate specialized knowledge, and enterprises may find their IT teams lacking the expertise to navigate implementation intricacies like responsible design and legacy modernization.

At Virtusa, we’ve been helping enterprises with generative AI adoption for over a year, with a focus on ensuring our solutions are more than just proofs-of-concept. Our collaboration with a prominent not-for-profit organization stands as a testament to this fact: Virtusa Helio helped them implement a generative AI project management assistant across their global operations, garnering 40,000 users from multiple geographies and linguistic backgrounds within just four weeks of its launch. Such involved projects have equipped us with insights that can help enterprises chart holistic implementation strategies right from the start.

The volatility of a changing ecosystem

Generative AI may be brand new, but it's evolving at an alarmingly fast rate. Every day brings new players, solutions, and models – but only some show the promise of becoming foundational pillars. Over just the past year, we’ve seen tech giants pull back on their generative AI platforms due to issues with responsible content generation, while smaller players have cropped up that offer a well-considered approach to generating everything from meeting summaries to slick presentations. Separating these hits from the misses can be challenging enough, but even just keeping track of new and niche entrants can prove overwhelming.

Virtusa Helio acts as a trusted advisor, helping enterprises sift through the noise and identify the most relevant and reliable solutions for their specific needs. In a recent project, Virtusa helped an information management company zero in on Google’s vision-based generative AI models for 30x faster information scanning. Working together with our partners, we leveraged our Generative AI Center of Excellence to help the client move from experimentation to execution swiftly and effectively.

The challenges of responsible adoption

As with any powerful technology, generative AI raises several ethical concerns. Companies that don’t do their due diligence stand to be caught in an inferno of ethical issues, security risks, and privacy violations. An important consideration here is transparency. Companies that do not design their systems responsibly risk falling prey to the “black box” problem that can impact interpretability and, ultimately, consumer trust.

That’s why Virtusa Helio focuses heavily on responsible adoption to ensure bias mitigation and interpretability, especially in applications affecting individuals or society, ensuring trust and accountability. Our Helio Operate accelerator also uses AI-powered tools for continuous monitoring of threats to protect AI investments from adversarial attacks.

The cost of navigating the maze

Generative AI is expensive and companies can rack up massive costs while going from experimentation to implementation. The most basic cost drivers include acquiring the right talent and exploring multiple models and platforms – both of which can place large demands on time and financial resources alike. Infrastructure costs can begin to pile quickly, thanks to a never-ending need for more powerful servers. All of which eventually contribute to large development budgets. And that’s before we even breach the possibility of running models with your computational power.

Talent acquisition: Securing the specialized skills needed to develop, manage, and maintain generative AI models can be a significant expense. A survey from hiring platform Indeed² found that people with generative AI skills can command salaries up to 47% higher than their counterparts, with average salaries of over $174,000.

Model experimentation: With generative AI still in its infancy, finding the right model and approach often requires experimentation with different tools and platforms. This experimentation can be time-consuming and resource-intensive, adding to the overall cost of adoption.

Computational power: Training and running generative AI models can be computationally expensive. Companies that choose to host their models may need to invest heavily in high-performance computing infrastructure. Cloud-based solutions can offer a more scalable alternative, but even these come with ongoing pay-as-you-go costs.

Data acquisition and preparation: Generative AI models thrive on high-quality data. The process of acquiring, cleaning, and preparing the necessary data sets can be a hidden cost that shouldn't be underestimated.

Virtusa Helio’s accelerators are designed to help enterprises streamline the development life cycle and optimize resource allocation to not only simplify implementations but also save costs.

Helio Innovate supports ideation with rapid prototyping in a secure environment so companies can set courses without burning through people and financial resources. Beyond experimentation and prototyping, Helio Build acts as a catalyst for greenfield application development, while Helio Enhance helps enterprises modernize and prepare existing systems to support their generative AI efforts.

A little foresight goes a long way

Ultimately, what enterprises need as they make their way through the tall hedges of the generative AI maze is predictability – the ability to see what challenges lie around the next corner. And that’s where Virtusa Helio excels. We call our AI suite of generative ai services “Helio” as a representation of this. Helios, the Greek God of the Sun, is also the God of Sight – and the inspiration for the Virtusa Helio name.

Our work with our clients over the past year has provided us with some valuable foresight into the challenges, considerations, and opportunities that lie ahead for enterprises embarking on this journey. Whether it’s solving for expensive talent or optimizing costs for experimentation and development, our experience allows us to navigate the path effectively. That’s how Helio serves as a beacon to guide businesses down every path and around every corner in the great generative AI maze.

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