SaaS Brief

Finance 2.0: AI-Bolstered, Data-Driven Planning

Forward thinking finance leaders are broadening the scope of their work beyond bottom-line-related management tasks. Technological innovations combined with the Covid-triggered economic downturn have caused an increased focus on the top line: increased market share, long-term corporate financial health, and competitive edge are critical and top of mind.

Frontrunner corporations shape their finance department to inject value by providing business insight and assertively predicting future business trends. Two key elements of next gen, impactful finance leadership: AI technology and data integration.

Preparing for the AI leap

Innovation begins when CFOs take the time to identify opportunities for automation in the areas that absorb significant resources. Manual processes slow down the company’s day-to-day operations and the lion’s share of the finance department’s tasks — budgeting, forecasting, reporting, operational accounting, transactions, and allocations, to name a few — can greatly benefit from switching toward automated processes, liberating time and resources to focus on business growth and long-term sustainability.

Rich Clayton, Vice President of Oracle's business analytics product group, asserts: “As business applications become more proactive, looking for and understanding patterns in the data from across the organization around the clock, companies need to have the right systems, processes, and culture to take advantage.”

The AI leap goes beyond implementing technology into your company’s processes, however. First, company data needs to be in order. Second, laser-focus on investment and outcomes is a must, and AI projects should be managed through the same lens of rigor and meticulousness required by financial reporting.

The scenario to avoid is an AI roll-out before a cohesive and clearly outlined business case is made. As published by Harvard Business Review, Deborah O’Neil, Partner at Oliver Wyman’s Digital and Financial Services practices, believes that, “...companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them.”

Breaking data silos

With a business case mapped out and AI now forming part of day-to-day automation, CFOs can focus on implementing their blueprint for structured analytics and centralized data processes. The latter enables the company to collect standardized data. A single, mainstream data language allows all departments across the company to feed operational data to a centralized platform. Organizational silos within a company often lead to data silos, so relying on data with conflicting information and differing data formats should be discouraged. Data silos are as crippling for strategic decision-making as no information at all.

As cloud technology company Oracle points out, “Automation lets companies amass data faster, and access to more data enables more informed business decisions. However, the quality of those data insights and human judgements will be reduced if the organization operates in silos.” What is more, said data needs to become consumable and visual for better interiorization purposes — understandable by anyone in the company, even in the absence of the CFO’s insights.

In-house AI or an outsourced platform?

When strategizing around AI implementation, the choice of either building or buying an AI platform comes down to more than costs. To guide the decision, finance leaders should make a point of seeking input from their CIO or CTO on partnering with a third-party AI provider versus developing a platform internally.

Evidence points corporations to combine both alternatives for the best outcome, leveraging in-house strengths and outsourcing weaknesses. Deloitte advises CFOs to “...consider if the problem is shared across other areas of the enterprise, and ensure alignment of the organization’s AI ambitions. Is the process you are solving for specific to finance (e.g., revenue forecasting)? Or is it a solution that could benefit other areas as well (e.g., invoice matching)?

Corporations that consolidate their data experimentation capacity can test new algorithms, business models, and a range of scenario simulations to assess both risks and probabilities of success before new products or services are deployed. This can optimize future performance that would have otherwise been out of reach.

Clayton insists on the value of establishing a “data lab” to test viable commercial uses for in-house corporate data. These act as a “...platform where you can bring together sets of data, conduct tests, and use machine learning to identify hidden patterns.” Equipped with the right analytics tools, including internal and external data and personnel expertise, what seem like unrelated dots can be connected to boost the expected outcome of new business strategies.

What industries say about AI-driven decisions

McKinsey’s 2019 Global Survey on leaders in data analytics suggests that data-driven decisions are becoming the go-to tool to gain a competitive edge. Forty-four percent of survey respondents reported traditional competitors were launching new data and analytics businesses in 2018, a whopping 144 percent increase compared to 2017’s 18 percent.

To guide companies further into their transition toward a data-driven, AI-bolstered business, McKinsey outlines the primary factors influencing data analytics and outcomes as follows:

  • Constructing a strategy to pursue data and analytics (21 percent)
  • Ensuring senior-management leadership of analytics (18 percent)
  • Designing effective data architecture/technology infrastructure to support analytics activities (11 percent)
  • Developing a workforce that understands how to use analytics (11 percent)
  • Getting business users to apply analytics insights consistently in day-to-day work (10 percent)

The main takeaways of the survey are:

Make data available. McKinsey stresses how critical it is to transfer corporate data from their traditional silos toward advanced-analytics-based tools, hence the importance of reconfiguring organizational processes to enable swift and seamless data sharing across departments with a bolstered tech infrastructure and a user-friendly dashboard.

Treat data as a product with real return on investment. To make the most of their corporate data, CFOs need to switch their optics on data as raw material that can be used to manufacture good executive decisions. Rather, McKinsey suggests treating data as an in-house product to be packaged and distributed to the company’s different departments.

Take an agile approach to data transformation. To obtain the best results, McKinsey advises companies to focus on treating their data culture as an incremental, evolving element. This can be tackled on two fronts: Educate new and existing hires on the use of data and analytics, and continuously communicate from the C-suite how critical the appliance of these tools is on a day-to-day basis.

A properly deployed AI- and data-driven transition will translate into finance teams that do not require looking backward for answers to questions about the future of their business.

Looking forward is the staple of advanced analytics, granting the tools and capacity to improve future forecasts with on-point predictions about either customer behavior or product and service performance.

An end-to-end global expansion solution

The same principles of a seamless and data-driven platform powered by AI for business sustainability applies when it comes to international expansion, especially cost-effective expansion. CFOs looking at global growth to broaden revenue streams and diversify know how much of a time-intensive venture it is. Market research, product testing, business intelligence research, stakeholder mapping, entity establishment, and local talent hiring are but a few of the many hurdles inherent to the consolidation of an international footprint.

To successfully streamline the process, companies can partner with an Employer of Record. Using this innovative solution, finance teams spend less time on the intricacies of in-country legal requirements, and more time adding value in other areas of the business. Globalization Partners’ advanced technology simplifies the hiring process and supports teams with in-country expertise in international labor laws, making global growth less complex for companies as they go after market share internationally.

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