When it comes to AI adoption, ask “if” and “what”, but also “how”.

Last week, I joined a meeting to discuss a new survey to collect data on AI adoption and use in businesses across various countries, with the aim of informing policymaking. The survey developers are asking some really interesting questions around whether businesses are using AI, what type of AI they were using (e.g., AI for core vs secondary business activity), and why they invest in AI (i.e., drivers and barriers). To these, I suggested that the team should also add questions about how the businesses are approaching AI deployment. Namely, I suggested that the survey ought to capture whether businesses were developing AI in house or were relying on third parties. 

I suggested this because, based on my own empirical work with Ben McKeegan and Dorothy Yen, how businesses deploy AI is highly consequential for them. We reported our extended findings in the paper “Power Negotiation on the Tango Dancefloor: The Adoption of AI in B2B Marketing”.

It is customary for businesses to use third parties in the early stages of IT adoption. For instance, in the paper “Troubled Waters: The Transformation of Marketing in a Digital World”, my co-authors and I examined how firms used digital marketing agencies and other third parties to remedy a skills-gap around big data. As one of our interviewees said, at the time, businesses reach out to marketing agencies, consultants and other intermediaries, to enter the digital space because: “There aren’t enough people and businesses that understand how to use data.”

As with establishing a web presence or search engine optimisation in the 1990s, or social media and big data in the 2000s, firms are now having to look beyond their organisations to secure the resources needed to deploy AI in their business. As McKeegan, Yen and myself write in the “Power Negotiation on the Tango Dancefloor: The Adoption of AI in B2B Marketing” paper:

“Focal firms enter the AI space in pursuit of financial rewards and improved brand image. However, this requires technical expertise and technological prowess that many focal firms lack, presently. Faced with either entering a very costly and risky in-house process, or engaging the services of third party-suppliers, many focal firms opt for the latter (…) This allows focal firms to engage the technical expertise and technological power of the suppliers in order to deliver a complete solution to their customers.” (p45)

However, unlike previous digital technologies, where many firms go on to develop the relevant skills and are able to reduce their dependency on those external parties, if they wish, in the AI solutions’ space, dependency on third party-suppliers may actually increase over time. This occurs for two reasons. First, third party-suppliers are secretive about the algorithms used (and, in some cases, they are unable to explain it or replicate its workings, at all):

“You’ve subcontracted some key decision making to somebody that never tells you why or how they made the decision. It’s dependency on a black box IP that you don’t own, and that somebody else controls. If it stops working, you are not able to go in and fix it.” (Page 45)

Not only is the organisation unable to replicate the work of the third-party internally, becoming dependent on the supplier, but they also face rising costs: 

“Focal firms become dependent on the proprietary and secretive algorithms developed by the AI suppliers. By algorithmic gatekeeping, suppliers retain control of the use of the AI solution, maintaining the firms’ dependence and prolonging contracts”. (page 43).

Second, for the AI solutions to work, firms need to share their datasets with the third-party suppliers. Because acquiring bigger and better datasets enables the development of more sophisticated algorithms, firms are giving away a valuable internal resource that fuels the improvement of the third-party’s product and further increases their market power:

“The transfer of information from focal firms to suppliers is particularly relevant in the case of AI technology, as data are essential for the development and improvement of algorithms (…) On the one hand, the third-party supplier’s solution is appealing because it was developed and validated by accessing large databases. On the other hand, to use the solution, focal firms need to share their data with the third-party supplier, further strengthening the latter’s algorithms and, therefore, its technological superiority over other network actors. (…) Datasets that keep on being augmented with focal firms’ data become an amplifier of power imbalances in the AI ecosystem” (Page 43-44)

Based on these findings, it is my belief that monitoring how organisations are deploying AI solutions (and, in particular, which ones are relying on third parties), can give us an early indication of future power imbalances in the AI ecosystem, not just between AI suppliers but also between AI suppliers and firms using those solutions. The first imbalance leads to market dominance within the AI supplier market, the second one to a de-facto lock-in between a firm and its supplier.

Do you have experience, or insight, of engaging third parties, in the digital arena (AI or otherwise)?