Businesses across the world have moved on from whether or not to implement artificial intelligence (AI) solutions in their operations to which ones to implement first. Statistics corroborate the trend – a 2018 research by Gartner projects that the business value of AI globally reached $1.2 trillion by the end of the year – registering a 70% increase in a single year – would eventually triple that value by 2022. An International Data Corp study also makes similar projections – global spending on AI will hit $35.8 billion in 2019, and then, double by the year 2022.
As business leaders take giant strides toward embracing AI, we’ve now moved on to the question of how best to adopt it to our advantage. This leads us to the question of whether businesses should self-build or co-develop their AI initiatives.
To make sure that AI initiatives do not collapse even before they take off, organizations must start by defining what they seek to achieve by incorporating AI solutions. This can help organizations choose a model that optimizes their AI initiatives in true earnest. To help you choose between the two popular approaches – self-build and co-develop – let’s first understand what each of these entails.
Self-building means setting up in-house resources. However, self-building AI solutions is far more complicated than building software applications. It requires an extensive technology stack – a combination of tools, platforms, APIs in an integrated environment to develop end-to-end solutions at different stages of the AI lifecycle. In this model, AI initiatives are likely to fall short of their optimum potential either due to the inability to leverage full advantage of this technology or limited focus on integrated solutions leading to duplicacy or incompatibility of efforts.
The expertise needed, mainly across AI tech stack, to create effective and scalable solutions, isn’t readily available in most organizations. In such cases, businesses prefer partnering with an organization that already possesses the relevant AI capabilities. Traditionally, organizations have a vast knowledge of the business domain, processes, and ensuing challenges. The system integrators require these precise inputs to build the right integrated solution. So, the best-case scenario for most businesses is to bring together these teams to co-develop their AI solutions. In the case of complex requirements, adopting a phase-wise approach is recommended.
When you consider these two models, the answer is simple – self-build only if AI is integral to your core business practice. For every other outcome, co-developing is a more practical alternative.
Why co-develop over self-build? An AI initiative, by character, ought to be demand-driven. With that premise in mind, here’s why choosing co-developing over self-building is the smarter choice.
The many challenges of self-building
A lot of businesses take the AI self-plunge, looking at it as an extension of having created their in-house software applications – database, ERPs compared to market proved products. However, the process of self-building AI solutions is a complex one.
Even with open source tools, self-building AI can be a million-dollar investment. If you add in the cost of training machine learning algorithms and hiring experts, the self-build model comes at an astronomical price point. Why should one invest all to create something already available with the vendors specializing in AI tools?
Besides, AI initiatives crafted by in-house teams often come with limited focus and capabilities, which means hampered scalability, thus, preventing businesses from taking optimal advantage of this revolutionary technology.
Co-developing has a competitive edge
Co-developing also comes with a host of other benefits. For instance, vendors are wholly responsible for handling the integration of AI solutions into your existing IT environment as well as training the workforce to handle these tools. Besides, they also have specialized algorithms to offer for specific business processes.
You can also collaborate with the vendor to create a clear AI strategy and solutions roadmap, in line with business goals and objectives, to leverage these solutions optimally. Deploying POC, POV, MVP plans with schedules and resources follow an agile methodology. It gives businesses the option to build and test the solutions and accept these if and only if they meet the predetermined criteria.
Co-develop or Self-build: A checklist for the right fit
No business wants to invest in a model that isn’t sustainable in the long run and can potentially damage competitiveness and financial robustness. This risk can be mitigated right at the onset if stakeholders assess their capability vis-a-vis the end goals. Apart from factoring in the economics of self-building versus co-developing, here’s a checklist that can offer you a solid ground to base your decision.
• Do you have an AI roadmap ready to review?
• Is the AI initiative a one-off project or part of a long-term roadmap?
• Have you identified low-hanging fruits in this roadmap?
• Do you have clearly defined objectives, use cases, and RoI strategy in place?
• Does your AI roadmap cover an end-to-end integrated business scenario?
• Have you identified skill requirements based on scope and deliverables?
• Do you have adequate quality data to support the AI initiatives?
The bottom line
The self-build or co-develop debate is often best settled on a case-by-case scenario. However, if you look at the general market trends, self-build is a riskier proposition with a lot of room to lose out on capital and resources. On the other hand, the co-develop model, supported by the right vendor, will improve the chances of success and also build the innovation quotient across the organization.