Why In-House Artificial Intelligence Projects Fail
Companies all over the world, from giant corporations to start-ups, are keen to cash in on the vast value of Artificial Intelligence. With an intent to capture as much of that value as possible, many spend millions on in-house AI solution development rather than outsourcing to address their most critical business challenges. In a world where there’s a feasible DIY solution for almost everything, Artificial Intelligence is most often the outlier. The complexity and cost of AI solution development demands experience to reduce financial risk and ensure speed to benefit, especially in highly competitive sectors.
Think of your business as a human body, and your business challenges as illnesses of varying severity. Some challenges, like some illnesses, are treatable with over the counter medications, while others require a visit to the doctor, prescriptions, long-term treatment or intensive care. If you had an illness that required extensive medical attention, you wouldn’t hesitate to seek out the best medical team for treatment. Should you treat your business any differently?
In deciding whether to go in-house or outsource, it is important to consider how a strategic AI implementation will impact your business. If it’s done right, it will reduce costs, increase revenue and enhance competitive advantage. If it’s done wrong, to what extent is your business at risk?
Financial opportunity from AI abounds across sectors (see the figure below), and there is both margin opportunity and market share on the table for the businesses that harness AI to tackle their strategic challenges first. In other words, getting your AI implementation done fast and right matters, and you must weigh your decision to go in-house or outsource accordingly.
With competitive advantage on the line and the clock ticking, corporations place their bets on whether to navigate the AI journey alone or with partners. Instinctively, they are hesitant to collaborate, tantalized by the prospect of minimizing solution costs, while building their own innovative capacity and owning the resulting IP outright.
Logically, they then look to market outcomes and learn why in-house AI solution development efforts fail more often than not, even in the Fortune100 and at tech companies.
Without experience developing and delivering AI solutions, many corporations fail understand the costs, resources, processes, stakeholders, and even the objectives involved from the onset. As a result, in-house projects often lack a clear and viable design and delivery strategy, roadmap and KPIs, dramatically reducing the speed to benefit if not inhibiting benefit delivery altogether. Program costs and timelines become a driving force for failure. With little transparency into which aspects of the solution will drive the most value, there is no clear way to prioritize spending. Costs either spiral out of control, or corners are consecutively cut in design, development, testing and delivery, resulting in piecemeal solutions that impair data quality, promote bias and diminish solution accuracy, functionality, utility and outcomes.
More often than not, successful AI adopters partner with proven providers on a combination of off-the-shelf solution tailoring, ground-up solution design and solution delivery. How do they decide to partner rather than go it alone?
First, they recognize the competitive imperative for AI—the opportunity cost of following rather than leading in their market—along with the direct costs of failure, and their lack of in-house knowledge and experience with AI solution design and delivery. Second, they find a provider with a successful track record in similar or analogous environments. Third, they develop trust with that provider by laying the groundwork for a happy marriage in contracting. Then they see it through. From the leadership level-down, they commit to the partnership and collaborate from end-to-end to ensure project success.
Strategic AI implementations are broad in scope, capturing data and impacting activities across corporate ecosystems. This complexity is readily apparent in industry, where AI not only provides data-driven direction for decision-making at every step of the value chain and in every organizational department, but directly informs control and automation strategies in production, testing, packaging, distribution and even purchasing.
In recognition of the immense value and complexity of AI in industry, and the competitive need for speed in adoption, the World Economic Forum, in collaboration with McKinsey & Company, has published a toolkit of “practical recommendations” for industrials to accelerate their AI journey at scale. Appearing in The Next Economic Growth Engine Scaling Fourth Industrial Revolution Technologies in Production, this toolkit advocates the adoption of proven AI solutions and related technologies through a partnership and acquisition approach rather than in-house development.
With more and more data available for exploit across industries, the opportunities for its monetization through AI are greater and increasingly complex. So too is the risk of getting your implementation wrong.
Can your business afford the DIY approach?
