Beena Ammanath – Global Deloitte AI Institute Leader, Founder of Humans For AI and Author “Trustworthy AI.”
In this era of humans working with machines, being an effective leader with artificial intelligence (AI) takes a range of skills and activities.
AI is often imagined to be novel, breakthrough applications that provide a nearly insurmountable competitive edge in the marketplace. To be sure, “moonshot” AI endeavors can lead to powerful new tools, but the broader value of AI is found in numerous deployments that augment multiple parts of the business. Not every one of these (or even most of them) are novel deployments. Things like robotic process automation, computer vision, natural language processing and other types of AI have matured to a point where many of them are either available as off-the-shelf applications or offered as a service.
The AI acquisition question for business leaders ultimately comes down to: buy, build or partner?
As a starting point, understand that AI is not just one thing. It is the product of a constellation of hardware, software, data, other enabling technologies and high-skilled human talent. The availability of these component elements dictates what makes the most strategic sense for the business in terms of AI acquisition. Leadership is needed when answering the buy-build-partner question because the decision impacts multiple areas of the business, and there might not be harmony between how stakeholders envision the organization’s AI programs. A technologist might have a bias toward building in-house. A marketing team might seek an easy-to-deploy off-the-self platform. The CFO might calculate the best choice for the bottom line is AI-as-a-Service.
It takes leadership to balance these inclinations. All AI stakeholders need to operate within a cohesive strategy and governance structure, and decisions concerning that are made in the C-suite. As you contemplate how to build a powerful AI ecosystem, consider the conditions under which the enterprise might best buy, build or partner.
Buy
Purchasing packaged solutions is an expedient way to begin building the organization’s AI ecosystem. AI platforms related to back-office functions and automated customer engagement are reaching true maturity, and there might not be a need to reinvent those wheels. Consider the organization’s desired outcomes, identify where AI could contribute value and survey the marketplace for existing solutions.
This might be particularly beneficial to businesses that are early in their AI journey and might not yet have robust in-house capabilities and talent. Meanwhile, enterprises at all levels of AI maturity might look to co-developing AI applications, using kit-like platforms that can be further tailored to the business.
Keep in mind that any technology (including AI) that is available for purchase is unlikely to deliver differentiating capabilities because these tools are available to any organization.
Build
Transforming a business to use AI has ramifications beyond just the application. Over time, technologists are hired, the workforce is upskilled, technology investments and replacements are made and, ultimately, the organization is ready to take on bolder, more innovative use cases. It follows that organizations with more years of AI experience might be more likely to develop AI solutions in-house.
In this instance, collaboration between business units and data science is essential. The line of business users are in the best position to identify needs and opportunities for improvement and transformation. The data scientists, meanwhile, are best positioned to think through what is feasible with existing technology and the kinds of models that could be developed. Together, supported with the right technologies, processes, governance and talent, they can create applications that deliver differentiating capabilities in the marketplace.
Of course, not every AI pilot will prove out and make it to production. What is more, ongoing innovation and management bring real operational costs. When deciding whether to build, weigh the expected outcomes against the real costs of creating the tool that delivers those outcomes.
Partner
Many businesses are innovating and exploring AI uses cases, and working with these organizations can accelerate access to AI capabilities and applications. There are different ways to tap into the ecosystem of organizations developing AI.
A partnership might take the form of collaboration with a services vendor or product company that can build out a desired use case or offer AI-as-a-Service. This might be attractive in part because the continued innovation, ongoing management and scale-on-demand can be managed by a third party. Partnerships can also include making a business investment in a promising startup or acquiring a startup outright to give you access to the human capital and intellectual property. Or, an enterprise might also find eager partners in universities and academic institutions where AI is researched and developed.
When exploring potential partnerships, consider the enterprise’s AI strategy and goals, where the marketplace aligns with the vision and how collaboration can fuel a return on investment.
Interestingly, a recent survey by Deloitte, where I’m an executive director, found that many organizations today take an all-of-the-above approach to AI acquisition. The method of acquiring AI does not appear to be linear at this point, wherein organizations early in their journey buy and more AI-mature organizations build. Instead, because every business grapples with its own needs, goals and resources, the strategies they adopt necessarily need to be tailored to the enterprise.
Indeed, there is no single correct approach to building a powerful AI ecosystem. There are only the best decisions for the business, and, in that, leadership has a vital role to play.
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