With AI adoption now becoming table stakes to supporting and fueling modern enterprise operations, CFOs and CIOs require a unified vision and implementation framework to capitalize on enormous opportunities while minimizing associated risks.
Leaders must contend with and ultimately prioritize the tens of thousands of AI tools on the market and the fact that their competitors and even their own employees are likely already ahead of them on the adoption curve. Those tools can generally be bucketed into three streams: 1) broad-use generative AI systems like ChatGPT, Gemini, and Copilot, 2) existing enterprise systems containing AI-forward functionality, like UiPath, Alteryx, OneStream, and Dataiku, and 3) niche, functional-specific AI platforms purpose-built for accounting tasks, fraud detection, and more.
So how can organizations move forward strategically?
Enterprise AI Implementation: An Existential Necessity
Choosing the right AI tools and technologies is a major financial decision that will, in time, easily stretch into six and seven figures at full scale and adoption. The CFO must take the lead to justify the scope of this investment and allocate budget. But without the technical insights and knowledge of the CIO/CTO, it’s virtually impossible for the CFO to make informed decisions about what to invest in, the anticipated ROI, and the long-term implications for the organization.
Finance and IT’s partnership is vital to balancing technical feasibility, financial viability, and successful execution of enterprise AI.
The knock-on effects must also be accounted for, as they too will incur a cost, including:
- Upskilling or retraining certain staff to work alongside newly implemented AI tools.
- Proactively hiring new leadership to manage AI infrastructure and continuous AI investments.
- Re-engineering business processes to capitalize on AI while migrating existing staff over to value-add activities.
- Addressing potential employee churn, resistance, and change management issues.
- Enhancing cybersecurity, data privacy, risk mitigation, and company policy measures to combat undue risk posed by new technology.
- Cloud computing and data management can become more complex, costly, and time-consuming due to the quality and quantity of training data required and the processing power needed for maximum impact.
- Large pre-emptive AI investments run up against an opportunity cost – what other critical investments weren’t made due to prioritizing AI? Are there now underfunded areas of the business that will result in its own set of challenges?
Really, the entire global economy is at an inflection point. For organizations to set the pace of change (or at least adapt to it), the decisions made on AI today must be wrapped in due diligence, tight executive partnership, and a clear vision for the future.
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Implementation Challenges and How to Overcome Them
Effective collaboration between Finance and IT can determine the success or failure of AI implementation, leading to a seismic impact on an organization’s competitiveness, efficiency, and profitability.
Expected challenges on the horizon include:
- Change management: Implementing AI disrupts existing workflows and requires a well-coordinated strategy. Address employee concerns through proactive communication, training programs, and upskilling initiatives.
- Cost and ROI concerns: Finance can build a compelling business case for AI investments while IT can lead on resource utilization, technology architecture, and ongoing adoption/maintenance. Together, these functions can create ROI models capable of estimating and demonstrating return.
- Integration hurdles: The technology and process architecture of the business may receive a shock due to the speed and necessity of adding new systems, sunsetting obsolete ones, and rewriting operational playbooks. Building tangible use cases and proving ROI in the near term can also be difficult without a defined implementation framework, further obstructing greater adoption of AI tools and the successful rollout across the organization.
- Ethical considerations: Both Finance and IT must be mindful of potential AI biases and ensure ethical data practices. Transparency and responsible AI development are key to maintaining a harmonious relationship with stakeholders.
- Risk factors: While AI technologies can enhance risk management in some capacities, they can also introduce new risk vectors to which leaders don’t have an immediate answer. Any AI implementation should include proactive solutioning for cyber, privacy, data, technology, financial, and operational risks as well as the establishment of a risk-aware culture among staff.
The Collaborative Path to Success
A winning partnership between the CFO and the CIO/CTO on AI starts with:
- Joint vision-setting: Open dialogue, clear understanding of mutual goals, and regular interaction will facilitate the appreciation of technical and financial perspectives. Together, they can develop a cohesive AI implementation plan that covers initial cost outlay, integration hurdles, change management, long-term benefits, risk mitigation, and more.
- Shared language: Bridge the technical jargon gap. Finance needs to grasp the capabilities and limitations of various AI technologies, while IT must understand the nuances of financial processes and regulations.
- Collaborative selection of technologies that meet business needs: Piloting AI tools within specific functions for certain tasks can help prove value and build momentum for greater adoption. But which tasks come first? And which technologies make the most sense for a pilot program? Leaders must ensure they’re aiming AI at the most in-need areas of the business and the technologies ultimately selected are scalable beyond the pilot.
- Cross-functional AI steering committee: Representatives from different departments can ensure the right balance between financial management and technological oversight, while accelerating decision-making and ingesting the right inputs, like data, opinions, ideas, allocated dollars, etc.
- Establishing and tracking KPIs: How is success and progress measured? This is often tracked as labor hours saved, handoffs reduced, redundant software retired, users adopted, etc. These KPIs should be closely monitored, visualized, and reported on to demonstrate AI’s ROI potential.
- Cross-functional teams: Remove silos by forming teams with both Finance and IT experts to jointly assess potential AI applications, evaluate vendors, and develop implementation plans. Using shared workflows, platforms, language, and goals, a cross-functional team is an incubator for new ways of collaboration the rest of the business can learn from. And it will be critical to ensuring Finance and IT are aiming at the same objective during the AI implementation journey.
What Should I Do Right Now?
Executives directionally know where they need to head but are less clear on where to specifically begin.
- Focus on business needs: Prioritize AI applications that address specific pain points or unlock significant value for the organization. There’s no advantage in waiting.
- Data is the key: AI thrives on data. Before investing in tools, assess your data quality, availability, and accessibility. Poor data quality will undermine any comprehensive AI initiative in the long term. You don’t get to successful enterprise AI adoption before getting your data in order – there are many steps that can’t be missed.
- But don’t let data delay you: Organizations can still identify use cases, qualify target states, and build familiarity with AI tools for individual tasks like document preparation, research, and brainstorming without having well-structured data or strong data hygiene. In the process of successfully piloting tools like ChatGPT or Google Gemini for ordinary tasks, AI becomes engrained in day-to-day workflows. These are valuable near-term lessons that can influence larger AI initiatives before huge investments are made.
- Start small and scale: Begin with pilot projects in low-risk areas to test the waters and refine your approach. Gradual implementation allows for iterative learning and adjustment, preventing a jarring experience or less-than-desirable AI performance.
- Executive buy-in: Secure the support of key decision-makers in Finance and IT, of course, but also from Risk, HR, Operations, and other key players. Their shared commitment sets the stage for a successful implementation.
- Establish governance: Develop a clear AI governance framework outlining roles, responsibilities, and decision-making processes. This ensures everyone is dancing to the same beat.
- Continuous learning: AI is a constantly evolving field. Foster a culture of continuous learning and collaboration to stay ahead of the curve and refine your AI strategy.
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The alliance between the Office of the CFO and the Office of the CIO/CTO is fundamental for any organization aspiring to leverage AI successfully.
Ready to define and mobilize AI success? CrossCountry Consulting offers strategic advisory and frameworks for identifying GenAI’s integration and alignment potential with your current or future data strategy. Contact CrossCountry Consulting to get started today.