The Rising Tide of AI Adoption
As we approach 2025, the conversation around the true cost of Artificial Intelligence (AI) has intensified, especially in C-level boardroom discussions. With AI firmly moving beyond hype into tangible reality, sectors like pharmaceuticals, manufacturing, airlines, transportation, logistics, and customer experience are witnessing significant successes. A Goldman Sachs report predicts that AI investment will soar to an astonishing $200 billion next year. However, such a substantial investment comes with its own set of challenges, especially for tech and consulting companies trying to determine the optimal point of entry into this dynamic arena while trying to figure out, “What’s the cost of AI?”.
We asked this question to Nandakumar Sivaraman (Nanda), Vice President (Engineering) and Data and Insights Practice Head at Bridgenext. Here are his insights on what enterprises need to consider when adopting AI and why the cost of AI goes beyond just financials.
Q: Adoption of AI is on the rise, but what are the key factors driving AI costs?
Nanda: The cost of AI isn’t a one-dimensional figure; it’s multi-faceted and depends on various factors. Some of the key contributors include:
- Scope and Vision: Many companies start their AI journey with isolated use cases, which leads to fragmented initiatives and minimal returns. It’s crucial to define the scope and ensure Artificial Intelligence (AI) solutions align with long-term business goals as well as immediate needs.
- Technology Stack: Selecting the right technology stack is essential. Since most AI solutions are cloud-first, companies need to evaluate their multi-cloud strategies, focusing on scalability and relevance. The needs of large enterprises differ from those of SMBs and getting that balance right can save costs down the line.
- Data Acquisition: AI thrives on data. In fact, data acquisition can consume up to 80% of AI-related costs due to the complexity of collecting, cleaning, and organizing data. Managing data efficiently is one of the biggest cost factors. Industry leaders from Apple to Snowflake and others stress that clean, well-managed data is critical for AI success. Yet, many companies face challenges in data management due to factors like mergers, market shifts, and competition, often leading to delays or project failure.
- Talent Acquisition: Skilled AI talent is in high demand but still relatively scarce. Organizations need to invest not only in hiring experts but also in developing internal teams to manage AI initiatives. We’re seeing Chief Data Officers evolve into Chief Data and AI Officers, which reflects AI’s growing role in enterprises.
- Development Time and Maintenance: AI projects are iterative. They typically move through stages—prototype, MVP, and full-scale implementation—and this process takes time. Maintenance is another ongoing cost, as AI solutions need to evolve with changing trends.
In addition to this, the biggest challenge and cost is associated to the cultural shift, not the technology itself. Transitioning to an AI-centric paradigm demands organizations to embrace data-driven decision-making, continuous learning, and innovation. Overcoming resistance to change is crucial, with leadership playing a key role in communicating AI’s value. Training and trust-building are essential in aligning technology with human capital, turning cultural adjustments into an invaluable investment for innovation and growth.
Q: How should companies approach AI implementation strategically?
Nanda: Strategy is key to unlocking the potential of AI. Here are a few important steps:
- Define the Scope Early: Instead of experimenting with isolated AI use cases, companies should develop a clear, long-term AI strategy. This includes defining the audience, complexity, and types of data needed to support the initiative.
- Focus on Data: From the outset, organizations need to invest in the infrastructure, processes, and data analytics & AI solutions to manage data efficiently. This not only prevents delays but also ensures the AI system delivers meaningful results.
- Invest in Continuous Learning: AI is an ongoing process, not a one-time setup. Companies must remain agile, constantly refining their AI models and adapting to new business realities. Staying flexible is crucial to long-term success.
Q: How can companies realize the return on AI investment and manage early costs?
Nanda: Realizing AI’s return on investment requires a strategic approach and patience. Initially, costs often outweigh immediate benefits, much like plotting an X-Y axis where investment spikes early while returns lag. Early-stage AI projects, especially without a clear use case, can result in cost overruns and misaligned goals.
To address this, many companies adopt a Center of Excellence (COE) framework to align resources and guide AI initiatives toward high-impact outcomes. Early AI projects may start in a “High Cost, Low Benefit” quadrant, but the COE helps prioritize efforts for long-term gains. Research shows that effective use case optimization can improve ROI by over 50% within two to three years. This mirrors the adoption of the internet and cloud 30 years ago—despite early uncertainty, the long-term benefits have been substantial for those who invested strategically.
Q: Is there a “true” cost to AI, or does it vary depending on the business?
Nanda: There is no single “true” cost to AI because it’s as much about opportunity as it is about resources. Reflecting on the digital adoption wave of the internet and cloud technologies three decades ago, organizations faced a landscape filled with skepticism and uncertainty. At that time, the decision to integrate these innovations was surrounded by ambiguity and a lack of concrete cost structures, much like today’s AI investments. Yet, visionary enterprises took the plunge, driven by the potential these technologies promised. As pioneers navigated opaque pricing models and uncertain returns, they laid the groundwork for the transformative digital economies we witness today.
Yes, there are costs tied to infrastructure, talent, and data, but AI opens new doors—whether it’s through innovation, efficiency, or creating new revenue streams.
AI helps businesses stay competitive by enhancing customer experiences and improving operational efficiency. For those who invest strategically, the returns can be significant.
Q: What’s the message for businesses hesitant to invest in AI?
Nanda: The real cost of AI is not in dollars—it’s in missed opportunities for those who hesitate. AI is not a fixed expense; it evolves with time, technology, and business needs. Some companies may need to invest heavily upfront, while others can grow their AI capabilities gradually.
At Bridgenext, we recognize that the cost of AI is not just a financial consideration. We guide businesses through every step of the AI journey, helping them understand the true value of AI and how to maximize it. Companies that hesitate today may find themselves behind the curve tomorrow, missing out on the growth and efficiency AI can deliver.
Bridgenext’s forward-thinking approach positions it as a leader in guiding companies through the complexities of AI adoption. As AI continues to evolve, organizations that take a strategic, well-rounded approach will be the ones that come out on top.
Reference
www.goldmansachs.com/insights/articles/ai-investment-forecast-to-approach-200-billion-globally-by-2025
