Building an Analytics and AI Strategy? Don’t Overlook These 5 Key Components

In my experience with helping internal audit teams develop and launch their data analytics initiatives, most fail because they lack a long-term strategic plan for their analytics program. 

Strategic analytics planning is essential for internal audit functions aiming to deliver value and drive meaningful organizational change. By developing a strategy that includes key performance indicators (KPIs), regular reassessment, performing gap analyses, a long-term vision, and securing executive buy-in, organizations can ensure their analytics initiatives are effective and aligned with the broader goals of the department.

Read on to learn five things every analytics and AI strategy should include — and for a deeper dive into the subject, register for my upcoming free CPE webinar with AuditBoard, “How to Build Your Analytics and AI Strategy,” on July 11, 2024. 

1. Key Performance Indicators (KPIs)

KPIs are important for measuring success, but these often lack specificity and a clear path to achieve a goal. Vague KPIs can lead to misleading assessments, where organizations give themselves undue credit. A good KPI should be specific, measurable, and tied to a time frame and overall goal. For example, “We will run our outlier analytics on 100% of audits with transactional data by the end of the year” is a well-defined KPI. The KPI includes a measurable goal, a specific scope, a time frame, and a measurement method.

When developing KPIs, I recommend considering the ramifications of not meeting the goal. If missing a KPI has no significant negative impact, it may be a useless metric. KPIs should align with the overall vision for data, analytics, and AI programs. For example, if the vision is to make audits more efficient, the KPIs should reflect improvements in audit efficiency.

2. Quarterly Review and Assessment of Strategy

It is often overlooked that strategies should be reviewed and updated regularly. Quarterly assessments allow teams to adjust their plans based on past performance and anticipate future disruptors. For example, if a key team member leaves, plans should be in place to fill the position and continue analytics initiatives without interruption.

These reviews should also account for disruptors, such as new technology implementations or changes in company priorities. Adopting high-level agile concepts, such as regularly evaluating what is working and what needs to change, helps organizations stay on track. Many departments are dealing with the impact of the widespread use of AI tools. This new technology has allowed people to create anything from art to lines of computer code. In fact, some teams are using AI to assist with their data analytics program. If this disruptor is not part of your team’s conversation, it should be very soon.

3. Adopting a Longer (3+ Year) Strategy

Many organizations fail to consider a long-term strategy beyond the immediate future. A robust 3+ year strategy should encompass people, processes, and technology, with detailed plans for the first year and less specificity for subsequent years to allow for flexibility and changes as circumstances change. A governance component should be included as part of the plan as well. Accountability measures, such as the KPIs mentioned before, should be in place to ensure that the strategy is being executed as planned. A clear roadmap with quarterly milestones adds credibility and authority to the strategy when looking for continuous buy-in from stakeholders.

4. Gap Analysis

A gap analysis helps organizations understand their current position and the steps needed to achieve their desired state. How can you know where you want to be if you don’t know what’s possible? Organizations can use a maturity model like the one pictured below to assess what is possible and create a strategy to bridge the gap.

5. Executive Buy-In

Securing executive buy-in is another key to the success of long-term analytics strategies. Obtaining the necessary resources is challenging without agreement from key executives, such as the CFO or Audit Committee. Building good relationships with data officers and aligning the analytics strategy with organizational goals can facilitate this buy-in. When the audit analytics strategy supports the organization’s overall strategy, executives are more likely to support it. Demonstrating how analytics initiatives can help achieve broader organizational objectives can appeal to executives’ self-interest and ensure their commitment.

Advance Planning Pays Off

Strategic analytics planning is key to transforming internal audit functions into value-adding components of an organization. By developing specific KPIs, conducting regular strategy assessments, planning for the long term, performing gap analyses, and securing executive buy-in, organizations can harness the power of data and AI to drive change and gain a competitive advantage.

Internal audit teams should strive to be seen as innovators and valuable partners within their organizations by leveraging data and analytics to make impactful contributions. By following the strategies outlined, audit functions can motivate their teams, garner stakeholder appreciation, and actually add value to their organizations. For a deep dive into the subject, join me for my upcoming free CPE webinar with AuditBoard, “How to Build Your Analytics and AI Strategy” on July 11, 2024.