Cutting Through the Noise: Why Now is the Time to Invest in Next-Gen Technologies

By Krishnan Raghunathan, Head - Finance & Accounting Services, WNS

In the realm of boardroom conversations, buzzwords like Generative ArtificialIntelligence (Gen AI), data lakes, predictive analytics and automation frequently surface. Concurrently, pivotal issues like talent scarcity, rising inflation and widespread geopolitical and economic instability shape discussions within organizations. However, there's a noticeable disparity between the prevalent talk on these topics and the actual scenarios business leaders face.

So, how is the increased adoption of next-gen technologies driving investment, and what steps are leaders across industries practically taking in applying these technologies to address pressing business problems?

Step 1: Embrace Gen AI

Such is the negative noise around Gen AI and risks associated with it, according to a survey published by Boston Consulting Group (BCG), more than half of C-suite respondents (52%) said that they would actively discourage the use of Gen AI. Only 8% regard it as a source of business value going forward. The reality, however, can be very different.

For example, we can examine a large US-based manufacturing firm that looks at financial insights on a monthly basis. With current systems, by the time forecasts and analysis of any month are finalized, it is already too late for company leaders to take actions that can have significant impact to business unit performance, and ultimately change any monthly outcomes. Coupled with increased complexity in forecasting, due to things like environmental factors and the availability of experienced talent pool for business partner roles, they are a bit behind.

As a solution, a CFO could make the decision to invest significant dollars to use AI tools in its financial planning and controls environment. These tools not only result in a significantly improved forecast, but also the ability to receive this data much faster to help timely decision making that would generate significant business benefits – alongside reduced staffing requirements in these highly skilled roles.

However, while the adoption of AI by CFOs should have been as game changer, business leaders kept going back to their traditional ways of working.

So, what is the cause of this slow adoption and management failure? Leaders currently find it hard to work with models that do not provide explainability or traceability of decisions. For example, instead “RMSE” values of forecasting models, leaders would like someone to explain the most accurate model used with percentage probability of error. This only gets more difficult and complex with usage of more and different models. This is where Generative AI can play a role and allow them to interact in natural language with the AI tools and get responses, without necessarily learning the skills of data science.

Such combined use of Gen AI along with traditional AI models can be a game changer in development and usage of sophisticated, timely financial models that consider multiple variables, scenarios and market conditions. Company leaders can use Gen AI for a number of tasks, including augmenting reports with recommendations on capital markets, and using sentiment analysis to increase ROI, among others. In response to market dynamics, for example, the company's finance team can use Gen AI to create daily updates of the income statement and balance sheet. They can also delve into key metrics for insights and interpretations.

Moreover, the tool can generate notes and disclosures on the financial statements based on the variances between budgeted and actual figures.

Additionally, if fraud emerges as a growing concern for the company, the finance team could harness Large Language Models (LLMs) to analyze massive datasets and track emerging market trends to provide real-time reports on possible fraud cases and areas susceptible to revenue leakage. Gen AI can also alert financial controllers and internal auditors, enabling them to take proactive and, in many cases, preventive action.

Step 2: Exploit the Full Potential of Data Lakes

The ‘chatter’ might suggest that companies are already fully harnessing the potential of data lakes. However, the reality is that investment in this technology is still in its infancy, according to theGlobal CFO Surveyby Everest Group, supported by WNS. Meanwhile, research findings indicate that the global data lake market is estimated to reach USD 89.53 Billion by 2032, up from USD 13.70 Billion in 2022, registering a remarkable compound annual growth rate of more than 20 percent. Business leaders and CFOs should already be investing in data lakes to improve data management.

Let's take the example of a UK-based financial services firm struggling to organize, store and access data. The CFO and her team know that using this data effectively could improve the firm’s decision making, customer experience, aid marketing strategies and bolster compliance and controls. The challenge lies in converting this raw data into structured information ready for analytics, data science, Machine Learning (ML) and Gen AI.

By embracing data lakes over traditional data warehouses, which incur higher costs due to increased storage requirements, the CFO and her team gain access to centralized, flexible and scalable repositories to manage structured and unstructured data. Through dynamic data ingestion and layering, the company’s newly implemented data lakes can convert data into readily accessible accounting numbers and reports. This process is notably more efficient than traditional methods.

Step 3: Leverage Automation and Right Shoring in the Talent Battle

Contrary to ‘chatter’ suggesting normalcy in hiring and retaining post-pandemic, recent research from the Harvard Business Review indicates otherwise. Less than a fifth (19%) of new hires are considered fully successful and after 18 months nearly half (46%) are deemed to be failures. TheGlobal CFO Surveyby Everest Group, supported by WNS, reveals that talent-related issues are the primary roadblock in organizations’ transformation journeys.

Let's consider the example of a consulting firm grappling with talent acquisition. In the past, the company’s CFO adopted conventional approaches, such as salary hikes and improved benefits, which yielded little success. To overcome this challenge, the CFO introduces automated tool around process mapping, Robotic Process Automation (RPA) to perform multiple repetitive tasks, use various AI tools to support consulting team and uses right shoring to leverage global talent pool specially for tasks that do not require real time customer interface. This minimizes the need for extensive new hires, at the same time providing access to expanded talent pool and reduced reliance on manual intervention, allowing team members to focus on tasks aligned with their skills and creativity. The introduction of these technologies can improve data accuracy, skill sets available to service end customers, boosting staff morale and retention.

As company leaders and CFOs navigate the balance between shaping their longer-term technological investments and addressing immediate concerns, noise can overshadow reality. However, the illustrated scenarios emphasize the value of collaborating with seasoned operational consulting partners, technology evangelists with domain expertise and a broader perspective across all industries. This partnership can enable leaders across industries to filter out the noise, concentrate on reality and harness the potential of groundbreaking technologies to empower their teams and future-proof their businesses.

Investing in these areas will be key for company leaders and CFOs aiming to not only stay ahead of the times, but keep up with them, as Gen AI adoption has become necessary for all. As technological innovation accelerates, many company leaders, including CFOs are, quite understandably, still determining how to implement rapidly emerging technologies such as Generative AI, data lakes and automation. The benefits offered by these technologies must be weighed against the challenges stemming from talent shortages, inflation, and prevailing economic uncertainty. With technological change accelerating, business leaders and CFOs must filter out the noise and focus on what really matters.

Looking ahead, there is no doubt that embracing Gen AI and next-gen products will be essential to stay competitive and earn a place in the ever changing and continually competitive market.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.