{"id":10003,"date":"2021-03-05T01:53:00","date_gmt":"2021-03-05T06:53:00","guid":{"rendered":"https:\/\/www.sage.com\/en-us\/blog\/?p=10003"},"modified":"2024-09-26T07:25:49","modified_gmt":"2024-09-26T11:25:49","slug":"finance-2-0-ai-bolstered-data-driven-planning","status":"publish","type":"post","link":"https:\/\/www.sage.com\/en-us\/blog\/finance-2-0-ai-bolstered-data-driven-planning\/","title":{"rendered":"Finance 2.0: AI-bolstered, data-driven planning"},"content":{"rendered":"
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\n\t\t\t\t\t\tFinance 2.0: AI-bolstered, data-driven planning\t\t\t\t\t<\/h1>\n\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t

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\n\t\t\t\n\t\t\t\t\"\"\t\t\t\tBob Cahill<\/span>\n\t\t\t<\/a>\n\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Preparing for the AI leap<\/strong><\/h2>\n\n\n\n

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\u2019s day-to-day operations and the lion\u2019s share of the finance department\u2019s tasks \u2014 budgeting, forecasting, reporting, operational accounting, transactions, and allocations, to name a few \u2014 can greatly benefit from switching toward automated processes, liberating time and resources to focus on business growth and long-term sustainability.<\/p>\n\n\n\n

Rich Clayton,<\/a> Vice President of Oracle’s business analytics product group, asserts: \u201cAs 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.\u201d<\/p>\n\n\n\n

The AI leap goes beyond implementing technology into your company\u2019s 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.<\/p>\n\n\n\n

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<\/a>, Deborah O\u2019Neil, Partner at Oliver Wyman\u2019s Digital and Financial Services practices, believes that, \u201c…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.\u201d<\/p>\n\n\n\n

Breaking data silos<\/strong><\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

As cloud technology company Oracle<\/a> points out, \u201cAutomation 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.\u201d What is more, said data needs to become consumable and visual for better interiorization purposes \u2014 understandable by anyone in the company, even in the absence of the CFO\u2019s insights.<\/p>\n\n\n\n

In-house AI or an outsourced platform?<\/strong><\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

Evidence points corporations to combine both alternatives for the best outcome, leveraging in-house strengths and outsourcing weaknesses. Deloitte<\/a> advises CFOs to \u201c…consider if the problem is shared across other areas of the enterprise, and ensure alignment of the organization\u2019s 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)?\u201d<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Clayton insists on the value of establishing a \u201cdata lab<\/a>\u201d to test viable commercial uses for in-house corporate data. These act as a \u201c…platform where you can bring together sets of data, conduct tests, and use machine learning to identify hidden patterns.\u201d 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.<\/p>\n\n\n\n

What industries say about AI-driven decisions<\/strong><\/h2>\n\n\n\n

McKinsey\u2019s 2019 Global Survey on leaders in data analytics<\/a> 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\u2019s 18 percent.<\/p>\n\n\n\n

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:<\/p>\n\n\n\n