1. Determine the company’s posture for the adoption of generative AI
As use of generative AI becomes increasingly widespread, we have seen CIOs and CTOs respond by blocking employee access to publicly available applications to limit risk. In doing so, these companies risk missing out on opportunities for innovation, with some employees even perceiving these moves as limiting their ability to build important new skills.
Instead, CIOs and CTOs should work with risk leaders to balance the real need for risk mitigation with the importance of building generative AI skills in the business. This requires establishing the company’s posture regarding generative AI by building consensus around the levels of risk with which the business is comfortable and how generative AI fits into the business’s overall strategy. This step allows the business to quickly determine company-wide policies and guidelines.
Once policies are clearly defined, leaders should communicate them to the business, with the CIO and CTO providing the organization with appropriate access and user-friendly guidelines. Some companies have rolled out firmwide communications about generative AI, provided broad access to generative AI for specific user groups, created pop-ups that warn users any time they input internal data into a model, and built a guidelines page that appears each time users access a publicly available generative AI service.
2. Identify use cases that build value through improved productivity, growth, and new business models
CIOs and CTOs should be the antidote to the “death by use case” frenzy that we already see in many companies. They can be most helpful by working with the CEO, CFO, and other business leaders to think through how generative AI challenges existing business models, opens doors to new ones, and creates new sources of value. With a deep understanding of the technical possibilities, the CIO and CTO should identify the most valuable opportunities and issues across the company that can benefit from generative AI—and those that can’t. In some cases, generative AI is not the best option.
McKinsey research, for example, shows generative AI can lift productivity for certain marketing use cases (for example, by analyzing unstructured and abstract data for customer preference) by roughly 10 percent and customer support (for example, through intelligent bots) by up to 40 percent. The CIO and CTO can be particularly helpful in developing a perspective on how best to cluster use cases either by domain (such as customer journey or business process) or use case type (such as creative content creation or virtual agents) so that generative AI will have the most value. Identifying opportunities won’t be the most strategic task—there are many generative AI use cases out there—but, given initial limitations of talent and capabilities, the CIO and CTO will need to provide feasibility and resource estimates to help the business sequence generative AI priorities.
Providing this level of counsel requires tech leaders to work with the business to develop a FinAI capability to estimate the true costs and returns on generative AI initiatives. Cost calculations can be particularly complex because the unit economics must account for multiple model and vendor costs, model interactions (where a query might require input from multiple models, each with its own fee), ongoing usage fees, and human oversight costs.
3. Reimagine the technology function
Generative AI has the potential to completely remake how the tech function works. CIOs and CTOs need to make a comprehensive review of the potential impact of generative AI on all areas of tech, but it’s important to take action quickly to build experience and expertise. There are three areas where they can focus their initial energies:
- Software development: McKinsey research shows generative AI coding support can help software engineers develop code 35 to 45 percent faster, refactor code 20 to 30 percent faster, and perform code documentation 45 to 50 percent faster. Generative AI can also automate the testing process and simulate edge cases, allowing teams to develop more-resilient software prior to release, and accelerate the onboarding of new developers (for example, by asking generative AI questions about a code base). Capturing these benefits will require extensive training (see more in action 8) and automation of integration and deployment pipelines through DevSecOps practices to manage the surge in code volume.
- Technical debt: Technical debt can account for 20 to 40 percent of technology budgets and significantly slow the pace of development. CIOs and CTOs should review their tech-debt balance sheets to determine how generative AI capabilities such as code refactoring, code translation, and automated test-case generation can accelerate the reduction of technical debt.
- IT operations (ITOps): CIOs and CTOs will need to review their ITOps productivity efforts to determine how generative AI can accelerate processes. Generative AI’s capabilities are particularly helpful in automating such tasks as password resets, status requests, or basic diagnostics through self-serve agents; accelerating triage and resolution through improved routing; surfacing useful context, such as topic or priority, and generating suggested responses; improving observability through analysis of vast streams of logs to identify events that truly require attention; and developing documentation, such as standard operating procedures, incident postmortems, or performance reports.
4. Take advantage of existing services or adapt open-source generative AI models
A variation of the classic “rent, buy, or build” decision exists when it comes to strategies for developing generative AI capabilities. The basic rule holds true: a company should invest in a generative AI capability where it can create a proprietary advantage for the business and access existing services for those that are more like commodities.
The CIO and CTO can think through the implications of these options as three archetypes:
- Taker—uses publicly available models through a chat interface or an API, with little or no customization. Good examples include off-the-shelf solutions to generate code (such as GitHub Copilot) or to assist designers with image generation and editing (such as Adobe Firefly). This is the simplest archetype in terms of both engineering and infrastructure needs and is generally the fastest to get up and running. These models are essentially commodities that rely on feeding data in the form of prompts to the public model.
- Shaper—integrates models with internal data and systems to generate more customized results. One example is a model that supports sales deals by connecting generative AI tools to customer relationship management (CRM) and financial systems to incorporate customers’ prior sales and engagement history. Another is fine-tuning the model with internal company documents and chat history to act as an assistant to a customer support agent. For companies that are looking to scale generative AI capabilities, develop more proprietary capabilities, or meet higher security or compliance needs, the Shaper archetype is appropriate.
There are two common approaches for integrating data with generative AI models in this archetype. One is to “bring the model to the data,” where the model is hosted on the organization’s infrastructure, either on-premises or in the cloud environment. Cohere, for example, deploys foundation models on clients’ cloud infrastructure, reducing the need for data transfers. The other approach is to “bring data to the model,” where an organization can aggregate its data and deploy a copy of the large model on cloud infrastructure. Both approaches achieve the goal of providing access to the foundation models, and choosing between them will come down to the organization’s workload footprint. - Maker—builds a foundation model to address a discrete business case. Building a foundation model is expensive and complex, requiring huge volumes of data, deep expertise, and massive compute power. This option requires a substantial one-off investment—tens or even hundreds of millions of dollars—to build the model and train it. The cost depends on various factors, such as training infrastructure, model architecture choice, number of model parameters, data size, and expert resources.
Each archetype has its own costs that tech leaders will need to consider (Exhibit 1). While new developments, such as efficient model training approaches and lower graphics processing unit (GPU) compute costs over time, are driving costs down, the inherent complexity of the Maker archetype means that few organizations will adopt it in the short term. Instead, most will turn to some combination of Taker, to quickly access a commodity service, and Shaper, to build a proprietary capability on top of foundation models.
