Traditional finance cycles are fading. Jawwad Rasheed explains why AI, automation, and self-service analytics are transforming the role of finance leaders.

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According to PwC’s 2024 Finance Effectiveness Benchmarking Study, for the first time in 15 years of tracking data, finance departments now spend over 30% of their time on business insight rather than transaction processing. This change highlights how finance functions are evolving beyond traditional controllers and consolidators to become strategic drivers of business growth and competitive advantage.

In a recent Data Faces podcast, Jawwad Rasheed shared insights on this shift and its implications for finance professionals and business leaders.

About Jawwad Rasheed

Jawwad Rasheed is the Finance & Financial Services Transformation Lead at Alteryx with extensive experience guiding organizations through technology-driven change. Previously at EY, Rasheed specializes in helping financial operations bridge the gap between traditional finance and modern data-driven approaches. In this conversation, he discusses finance’s evolution from controller to cognitive agent, overcoming data perfectionism, the rise of self-service analytics, and why continuous accounting will replace traditional finance cycles.

https://www.youtube.com/watch?v=fZIU5abj7kE

 1. Moving from Controller to Cognitive Agent

Finance functions have undergone a fundamental transformation over the past 25-30 years. Jawwad Rasheed explains this evolution: “Historically, finance has been the controller, consolidator, compliance-led function—one that manages cost and keeps books and records in order,” says Rasheed. “But increasingly, finance professionals must become cognitive agents, with the capabilities, resources, and technology to analyze complex data sets and improve analytical capabilities for business and strategic decision-making.”

The role that’s really grown is the role of the finance business partner…to drive revenue growth, not just look at the cost and control elements, which is equally important, but really be at the forefront of how can we grow the enterprise in the business. — Jawwad Rasheed

A cognitive agent in finance goes beyond reporting what happened to identifying why it happened and what might happen next. Instead of simply flagging budget variances, they provide context on market conditions, competitive factors, and potential strategic responses.

This shift responds to intensifying competitive pressures making it harder for businesses to find an edge. The emergence of the finance business partner role exemplifies this change—finance representatives who work alongside business units to drive revenue growth rather than merely monitoring costs and controls.

The PwC study reinforces this trend, showing top-performing finance teams have reduced the proportion of time spent on transaction processing to 52%, compared to 59% for median companies, allowing more focus on strategic activities.

2. Self-Service Analytics: Redefining the Finance-IT Relationship

The traditional model of finance requesting data from IT has changed. Finance professionals now increasingly manage their own data pipelines, enabled by low-code platforms, business intelligence tools, and self-service analytics solutions.

What we’ve seen is the traditional model where a finance professional would raise a request to IT to define pipelines, curate the data, prepare the data… That model has been disrupted by the rise of hyper automation, by internal automation solutions, where those capabilities are now sitting within finance. — Jawwad Rasheed

This shift creates a new organizational dynamic. Finance analysts are becoming data analysts in their own right, handling more intricate data pipelines and complex requests that previously required specialized IT support. Meanwhile, data scientists can focus on even more sophisticated analysis.

The transition does require careful governance. Organizations must balance providing finance teams the freedom to access and manipulate data with appropriate controls. According to Rasheed, “You need to work in a more agile manner” rather than relying on traditional waterfall approaches to managing data requests.

PwC’s benchmarking shows this trend clearly—top quartile companies have reduced manual performance of automatable tasks from 35% in 2014 to just 19% in 2023, significantly outpacing median performers.

3. Leading Finance Transformation: Overcoming Common Misconceptions

Finance leaders embarking on analytics transformation often encounter several stumbling blocks that can derail their initiatives. Jawwad Rasheed identifies several key misconceptions:

There is this old adage that we need to get everything right in order before we can do analytics. If anything, having the right initiatives to create a cultural shift where people understand the importance of analytics… that’s what will surface the issues with the data. — Jawwad Rasheed

Three critical misconceptions stand out:

First, the belief that data must be perfect before implementing analytics. Rasheed argues that waiting for perfect data is counterproductive—imperfections will always exist, and analytics itself often helps identify data quality issues. A global manufacturing firm discovered this when they began analyzing profitability by product line despite imperfect cost allocation data. The analysis revealed insights that both improved the business and highlighted which data points needed refinement.

Second, assuming that deeper granularity in analytics automatically leads to better insights. The value of analytics depends on alignment with strategic priorities. “It very much depends on what the organization believes are its most important set of metrics and KPIs,” notes Rasheed.

Third, viewing transformation as a one-time fix rather than an ongoing evolution. Effective analytics requires a continuous improvement process that adapts as the business changes. The PwC study confirms this reality—even top-performing companies continue to find new automation opportunities, with management reporting (40% manual work remains) and customer billing (31%) offering significant room for improvement.

Creating meaningful change requires a cultural shift where organizations incentivize teams to surface new insights and identify opportunities, not just report what’s expected.

4. Bridging the Last Mile: Connecting Insights to Action

Many finance organizations struggle to convert their analytics capabilities into actionable business decisions. The key, according to Rasheed, lies in approaching the problem from the decision backward, not from the data forward.

The better course of action is to think right to left, saying, ‘What are the 10 decisions that I need to make? What are the 10 questions I’m looking to answer?’ Knowing what those are will help define those KPIs and metrics and then help confirm, ‘Do I have the data or the data gaps against those particular requirements?’ — Jawwad Rasheed

This approach starts by identifying the critical decisions business leaders need to make, then determining what information would enable those decisions, and finally assessing what data is needed to generate that information. 

Rasheed describes working with a global bank to implement this strategy. They first defined a series of metrics and visualizations for high-priority insights (the “80/20” approach), then built the capability to drill deeper into specific areas as needed. This approach allowed the bank to reduce financial close times by 40% while simultaneously increasing the depth of regional performance insights.

Organizations that bridge this gap successfully create a framework connecting corporate strategy to specific metrics, cascading through the organization as a driver tree of interrelated KPIs. For example, a company focused on increasing shareholder value might connect revenue growth, cost-income ratio, and P&L volatility as primary KPIs, with each breaking down into more specific operational metrics.

The PwC study reinforces the importance of this approach, showing that top-quartile companies generate 80% of their management reports from data repositories, compared to just 50% for median companies—indicating an integrated approach to data management that supports decision-making.

5. The AI-Enabled Future of Finance

Jawwad Rasheed envisions a future where traditional finance cycles become increasingly obsolete. Instead of periodic reporting, finance functions will provide continuous visibility into business performance.

> “There will come a point where things like your actuals and your forecasts can be produced instantly on demand, and therefore traditional cycles don’t become relevant… The distinction between operational and analytical data begins to disappear.” — Jawwad Rasheed

This evolution toward “continuous accounting” fundamentally changes the skills needed in finance. Rather than manually assembling data, finance professionals will become:

1. Model owners and specialists: Finance analysts will need deep understanding of the AI/ML models generating insights, with enough domain expertise to validate outputs and identify potential errors
2. Human interfaces between machines and decision-makers: Translating complex model outputs into business recommendations
3. Domain-specific model developers: Creating and fine-tuning specialized models for finance tasks

Looking ahead, Rasheed sees these changes happening in stages: over the next 1-2 years, increasing automation of transaction processing; within 3-5 years, widespread adoption of predictive models for forecasting; and beyond 5 years, the emergence of truly continuous accounting systems.

The PwC report supports this direction, showing finance technology spending has increased from 9% of total finance budget in 2017 to 11% in 2023 for median companies (and 16% for top quartile companies). Additionally, automation of finance controls has reached 78% among top-performing organizations.

This transition creates an urgent skills gap. According to Rasheed, “The talent that you need in finance doesn’t become the people that crunch and assemble all of that to produce some output. It becomes almost like the human interface between machines and the owner of models, that may be responsible to produce some level of outcome.”

6. Technology Priorities for Forward-Looking Finance Functions

While emerging technologies dominate discussions, Rasheed cautions that many finance functions have yet to fully leverage existing capabilities. Several technology priorities stand out:

We haven’t done enough of what we said we were going to do in the next 10 years. There’s a number of big ticket items out there that haven’t really come into full play and fruition that again, we need to explore and make practical use of. — Jawwad Rasheed

Key technology priorities include:

1. Cloud-based ERP solutions with microservices extensions: Core systems that provide flexibility and enable real-time data access. Implementation challenges include integration with legacy systems and ensuring consistent data governance across platforms.
2. Distributed ledger technologies: Blockchain and similar technologies can enhance transaction certainty through decentralized verification, particularly valuable for financial services companies with high-volume transaction processing needs. The main hurdle remains developing practical applications beyond cryptocurrencies.
3. Hyper-automation of standardized processes: Even with current technology, many organizations have only “scratched the surface” in automating rule-based processes. The challenge often lies in process standardization rather than technology limitations.

The PwC study shows this technology gap clearly. While top-performing organizations have automated 78% of their finance controls, median companies have reached only 40% automation, indicating significant untapped potential even with existing tools.

For finance professionals navigating this evolving landscape, Rasheed recommends developing hybrid skills that blend finance expertise with technology understanding: “Don’t lose the need for that finance specialism… but also think about how you become that middle person, almost the human middleware to technology.”

Becoming the Human Middleware

The finance function’s evolution from scorekeepers to strategic partners represents one of the most significant organizational shifts in modern business. Finance professionals who thrive in this new environment will:

1. Think beyond cost reduction to focus on team composition and strategic partnership
2. Select technologies appropriate to their organization’s maturity level rather than pursuing every new innovation
3. Act as change agents who help connect insights to action across the enterprise

For finance leaders preparing for this future, investing in both technical literacy and business understanding for their teams today will pay dividends tomorrow. Training programs that combine traditional finance skills with analytics capabilities should be a priority for any forward-looking finance function.

As Jawwad Rasheed concludes, “Be completely aware of the changes around you. It’s wonderful that we can accelerate the way that we get those foundational skills these days, but understand the role that you have to be more of an agent of change and an ambassador for finance services, rather than sitting in a functional language in finance.”

Transcript Highlights

This edited transcript has been condensed for clarity and readability.

David Sweenor (00:05): Welcome back to Data Faces, the podcast that explores human stories behind data analytics and AI. I’m your host, David Sweenor. Today I’m joined by Jawwad Rasheed, Finance & Financial Services Transformation Lead at Alteryx.

Jawwad Rasheed (00:59): My background has been predominantly in financial services. In recent years, there’s been a focus on driving change with technology at the forefront of how people and resources can best come together to move finance forward.

David Sweenor (02:08): Finance has historically had so much data. Why does finance seem primed to be data-driven, and how are modern analytics and AI changing this dynamic?

Jawwad Rasheed (02:35): Let’s go back 25-30 years. Finance historically has been the controller, consolidator, compliance-led function—managing costs, keeping books and records in order. The big change has been finance becoming a cognitive agent with the right capabilities, resources, and technology to analyze complex data sets and improve analytical capability for business decision-making.

David Sweenor (07:04): Does self-service analytics introduce more risk to a company?

Jawwad Rasheed (07:04): It’s a balancing act between giving finance professionals freedom to access information and maintaining governance controls. Unless you have some concept of being more self-serve, you’ll be left behind if you rely on a waterfall approach to managing requests.

David Sweenor (09:15): What are the biggest misconceptions finance leaders have about implementing analytics?

Jawwad Rasheed (09:15): A common misconception is thinking “we need to get the data in a very good state before we think about analytics.” There will never be a perfect state—the imperfect state will have to exist, and that shouldn’t be a blocker. Another misconception is thinking deeper analytics automatically lead to better understanding. It depends on what metrics and KPIs are most important to your organization.

David Sweenor (14:12): How do you bridge the gap between analytics and turning insights into action?

Jawwad Rasheed (14:12): The better approach is thinking right-to-left: What are the 10 decisions I need to make? What are the questions I’m looking to answer? Knowing those helps define KPIs and metrics, and then confirm if you have the data needed or identify gaps.

David Sweenor (19:10): How do you see AI impacting the role of finance over the coming years?

Jawwad Rasheed (19:10): We’re moving toward a state where there’s no concept of closing your books—you’re just always live, and at any time you can get a finger on the pulse of the organization. The talent needed in finance becomes less about crunching numbers and more about being the human interface between machines and the owner of models producing outcomes.

David Sweenor (29:58): What other technologies will have a significant impact on finance analytics?

Jawwad Rasheed (29:58): We’ll see more touchless transactions, more competition with major players providing ERP solutions moving to cloud-based automation, and more microservices sitting on top of those. We haven’t seen enough practical applications of distributed ledger technology yet. Most organizations have only scratched the surface with hyper-automation.

Jawwad Rasheed (32:45): For finance professionals, don’t lose your finance specialism, but think about becoming the human middleware to technology. Be aware of the changes around you and be an agent of change and ambassador for finance services, rather than just sitting in a functional siloed role.

Transcribed and edited from the original conversation on The Data Faces podcast