Question #3: Data
Is the data required for the intended purposes of the project available? Is that data unique?
A project using unique data generated internally as part of the business model—for example, customer interaction data—has the chance of becoming extremely valuable and strategic. This is especially true if the data or algorithms can be monetized (i.e., made available to third parties for a fee). In such cases, building the AI product or service in-house makes sense to retain intellectual property rights.
However, if the data for the project is widely available in the market—for example, data from Twitter™ or other social media—then the company should shift its AI strategy and consider buying AI solutions externally. Top-notch services exist today that collect social media, mobile device or other data and offer analysis on top. Considering there are many options and prices are relatively low, companies can use the solution that is “good enough” rather than try to gather their own data and slow down the AI implementation process. Our research shows that limited internal data availability and quality are the most common reasons why companies lag in AI deployment.
Question #4: Strategic impact
Is the project strategic?
If it is determined that a project will deliver a competitive advantage or have transformative impact, it is clearly of strategic importance. If the objectives are narrower—for example, keeping pace with rivals or meeting customer expectations—the impact of the project is more likely to be tactical. An additional consideration is whether the project is relevant only to the unit or function implementing it, or if it will also be useful for other parts of the business. A solution or tool that can be easily adapted and re-used by multiple divisions, functions or units may be deemed strategic, even if the team developing it is pursuing tactical business objectives.
An AI project deemed to be of strategic significance may very well merit a decision to build internally, depending on the existence of internal capabilities (see Question #5). A project of tactical importance should almost always entail a buy or partner approach, given the lower costs and risks involved in pursuing these.
Three AI operating models
The question of strategic versus tactical impact of an AI solution may be addressed differently depending on the operating model each company adopts to execute AI projects. Companies tend to employ one of three models:
- Distributed: Highly distributed ways of working are the sign of mature technology, tools and usage patterns. Here, data scientists have considerable resources at their disposal and can be productive working in loosely connected groups with established ways of reusing and providing knowledge and expertise to the wider business.
- Centralized: At the other extreme are highly centralized models, in which all data science and AI projects are executed centrally by a specialized team. Local groups that want to use the technology consult the center for all aspects of development. While efficient, this model can become a bottleneck of value and growth for companies, especially large ones in which command and control at this level might be difficult to achieve.
- Hub-and-spoke: This is a great model of working when you are setting yourself up for a journey to maturity. A central hub helps drive standards, methods and tools, and it guides the prioritization of work as the organization matures to support the technology. It consists of people who belong to the hub (data scientists, engineers of different types and data specialists) and specialists who belong to the business units.
Question #5: Capabilities
Do we have the required technical capabilities in-house?
If an AI project is deemed to be strategic, the last major question to ask is whether the organization has the internal capabilities to deliver it. If the answer is yes, developing internally will usually make sense. There could, however, be a case for using a vendor solution or partnering if speed is of the essence and mobilizing existing internal resources will take too long. In such situations acquiring a start-up with the right solution and ready-made capabilities could also be a good option.
If the capabilities are not on hand, the question then becomes whether to develop them internally, buy an off-the-shelf solution or use a specialist partner to help develop the project. Given that top AI talent is highly sought after—many organizations have trouble attracting and affording the right people—buying or partnering becomes an attractive option for many firms.
A hybrid approach is also possible, in which the company can draw on partners and/or buy off-the-shelf components (to ensure better results in shorter time with lower risk) while simultaneously developing a capability that can take on more of the heavy lifting on future projects.
In all cases, though, companies should always make sure that they are not reinventing something that’s commoditized and available at reasonable cost in the marketplace.