Companies are rushing to invest in AI initiatives. AI has great potential and can deliver many benefits, from driving innovation and streamlining processes to cutting costs and boosting revenue. The technology is so impactful experts believe it will add approximately $15.7 trillion to the global economy.
However, approximately 90% of AI projects do not deliver the desired ROI. While this is not uncommon for new technology, the key to successful projects lies in selecting the right initiatives that are strategically aligned with company strategy and can provide tangible benefits to existing goals.
To build a business case for any AI project, companies should follow an established blueprint that determines the level of alignment and gives teams the information needed to ensure AI investments are worth making.
The organizations with the most successful AI initiatives determined how to adapt the technology to existing business processes. While focusing on business value is crucial, success requires alignment with the company’s strategy, goals and objectives.
For example, when COVID-19 shook the world, insurers were confronted with pressing questions from their stakeholders, such as, “How much money will we lose?”
Quantifying potential losses and preparing for future uncertainties was paramount for Lombard, a South African insurer. Risk assessment is a core process for any insurance company, so for Lombard, this process was a logical place to start using AI. They partnered with Sand Technologies to develop real-time dashboards that transformed their risk assessment process. Gaining complete visibility into all risk areas enabled Lombard to perform full-spectrum risk evaluations and make more informed decisions, leading to highly strategic product pricing and smarter investment strategies.
AI’s computing power means companies can track their business performance faster and at a more granular level to learn the best aspects of performance. In addition, it allows companies to prioritize value over hype and avoid investing in initiatives that are a financial burden and waste resources.
However, companies must focus on initiatives that address specific problems and deliver measurable value. For example, a global telecommunications leader recognized that legacy systems couldn’t satisfy the demands of a rapidly evolving world. They needed to scale without sacrificing quality or efficiency. So, they built AI-driven models using company data to simulate real-world challenges and opportunities. This solution ensures every decision is data-driven, context-aware, and enriched by decades of industry knowledge. As a result, the telecom was able to prioritize large-scale fiber rollouts, capturing an astounding $4B in value and identifying 4 million new customers.
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First, companies should identify a specific use case and assess the AI initiative’s business value. This business case should at least answer the following questions:
AI isn’t magic. It is a powerful new technology that can help companies achieve their goals more efficiently. This technology requires investments in skills and equipment. When new investments are needed, mapping the benefits to an existing company strategy or goal is essential. Misalignment in company strategy can waste resources, cause missed opportunities and potentially lead to reputational risks.
AI will change market structures, lowering entry barriers for some companies and increasing the economic scale of others. In companies where AI improves productivity, the impact will lead to greater market concentration and competitive advantage due to significant savings. These savings help companies justify and afford the investment in AI technologies.
Technical feasibility means evaluating an AI project against a company’s existing technology, resources and skills. AI requires specific hardware, software, infrastructure and technical know-how, some of which will be new for the tech stack and the staff. Evaluate the requirements against your company’s capabilities, identify the key components required and plan for integrating technology and acquiring new skills.
AI requires vast amounts of data in standard formats. Assess the company’s data availability, fragmentation level and cleaning requirements necessary for usable insights. Calculate the amount of storage required and determine the best location (on-premises or in the cloud).
Management teams must assess various issues to determine the level of project complexity. Some factors to consider are technological innovation, the number of integrations, data privacy and security required, data access control, project and team size, level of technological innovation, the number of technologies involved, integrations and required technical skills.
No AI project can succeed without support from the C-suite. The capital expenditures and acquisition of new skills needed for AI projects require buy-in and commitment from the highest levels. Stakeholders must know that a new project will provide ROI and achieve stated goals.
Perhaps more than any other type of technology project, AI initiatives necessitate a great deal of collaboration. While most of the involvement will fall on individual stakeholders like internal and external users, developers and data architects, other participants will also play critical roles, particularly in following rules and regulations around data usage and privacy rights. The C-Suite must ensure a high level of collaboration across the organization.
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