How to build the business case for AI initiatives

Crafting an effective business proposal for AI initiatives

A well-developed AI business case should use concrete data and underscore potential cost savings, revenue growth and efficiency improvements. An effective proposal identifies key aspects of the AI initiative, including its objectives, scope, benefits, costs, value, risk and implementation plan.

Executive summary

The executive summary serves as a high-level overview of the AI initiative, including its purpose, scope and expected benefits. It should succinctly convey why the initiative is essential and what it aims to achieve.

Problem statement

The problem statement captures the business problem or opportunity that the AI initiative seeks to address. Its goal is to help stakeholders understand the context and relevance of the project.

Objectives and goals

This section includes specific, measurable objectives. These should align with the organization's overall strategic goals, including short-term and long-term targets.

Scope and deliverables

This portion defines what is in and out of scope for the AI initiative. It should set key deliverables and milestones as well as provide a clear timeline for project completion.

Benefits and value proposition

Next, the proposal identifies and quantifies the expected benefits of the AI initiative, using data to support claims. Examples of benefits include increased revenue, cost savings, improved efficiency, enhanced customer experience and competitive advantage.

Cost analysis

The cost analysis begins with a detailed breakdown of the costs associated with the AI initiative, including initial investments, ongoing operational costs and potential hidden expenses. It should also compare costs versus benefits to demonstrate the initiative's financial viability.

Risk assessment and mitigation

This section identifies the potential risks associated with the AI initiative and outlines mitigation strategies. Risks to address include technical, operational, financial and regulatory considerations.

Implementation plan

A detailed implementation plan sets forth the project timeline, resource allocation, key tasks and responsibilities. This section should also cover the project management approach and governance structure.

Stakeholder analysis

This section lists the key stakeholders involved in or affected by the AI initiative, including internal and external parties. It should describe their interests, expectations and anticipated engagement throughout the project.

Performance metrics

This section defines the key performance indicators that will measure the AI initiative's success. It should also document the process for monitoring and evaluating these metrics to ensure the project stays on track and delivers the expected benefits.

Conclusion and recommendations

The proposal concludes by summarizing the key points of the business case, with a focus on reiterating the benefits and value proposition. Finally, it should provide clear recommendations for proceeding with the AI initiative, such as suggested initial steps.

Financial and risk considerations

Financial and risk considerations are top of mind for every CFO, particularly when it comes to emerging and potentially risky technologies like AI. Consequently, preparing for their inevitable questions is essential.

Any AI initiative requires an initial investment. The CFO must allocate a budget for upfront costs such as the following:

• Developing or purchasing AI technologies and services.
• Investing in the necessary cloud infrastructure -- in particular, building out compute and storage to meet the AI initiative's demands.
• Skilling up current developers and hiring new AI talent.

The CFO must also assess the AI initiative's expected ROI by estimating the financial benefits, such as increased revenue, cost savings and efficiency improvements, and comparing them to the total costs involved. Proper financial planning requires understanding the payback period, which is the time it takes for the AI investment to generate enough benefits to cover its costs.

Beyond the initial investment, AI initiatives incur ongoing operational costs. These include maintenance, updates, AI model training and potentially high-energy consumption due to increased computational demands. CFOs must account for these recurring expenses in the company's budget.

Adhering to data protection laws and industry regulations can add to the cost of AI initiatives. CFOs must factor in expenses related to compliance, such as legal consultations, audits and modifications to AI systems to meet regulatory standards.

AI investments can affect various financial metrics and ratios, including capital expenditures, operational expenses and profitability margins. CFOs must analyze how these changes will impact the company's financial health and communicate these effects to stakeholders, such as the board of directors for some companies.

An organization's FinOps maturity also plays a role in managing the financial risks the AI initiative might bring. The more mature the organization's practices and reporting, the better the team can communicate cost projections to the CFO. Whoever is managing cloud costs should have a seat on the AI initiative team so they can work on final projections and business models for how AI will affect cloud spending.

Identifying and mitigating risks is a critical component of any AI initiative. Potential risks include data security concerns, integration challenges and technological uncertainties. A comprehensive risk management plan should outline these risks and present strategies for mitigating them. Strategies could involve adopting robust cybersecurity measures, conducting pilot tests and planning for scalable infrastructure.

Collaboration with CFOs during the budget cycle

Engage with the CFO from Day 1 of AI initiative planning to ensure their insights and concerns make it into planning. Depending on organization size, a designee from the CFO's team might work directly on the project instead of the CFO themselves.

"AI for the sake of AI" is a recipe for failure in an AI initiative. Instead, align the initiative with the company's broader strategic objectives. CFOs are more likely to support projects that contribute to the company's mission, vision and long-term strategy. Demonstrating this alignment can secure the necessary budget and resources. One of the best ways to showcase alignment is to release proofs of concept that clearly show how the AI initiative will contribute to the company.

While early AI initiatives will garner attention from all levels of an organization, take the extra step to maintain regular and direct communication with the CFO throughout the project lifecycle. Proper communications should include progress updates, changes to the initial project plan and financial performance. This type of continuous dialogue ensures the CFO remains engaged and supportive, allowing for timely adjustments to keep the AI initiative on track.

Will Kelly is a freelance writer and content strategist who has written about cloud, DevOps, AI and enterprise mobility.