Resources

AI diligence is an assessment of your AI maturity performed by strangers, under time pressure, with something you value at stake. In Strangers at the Gate, Andrew Welch argues that buyers, insurers, regulators and auditors are all really asking how an organization runs, and that readiness cannot be manufactured under pressure. It has to be built in steady state.

Nations are, to a great degree, the product of the technologies available to them at their founding. So are organizations. The framers of the American Constitution considered communication so foundational that a postal service was among the few operational powers they explicitly spelled out. The telegraph, the railways, the jet age, and the internet each expanded what both the world’s nations and its firms could become over the two centuries that followed. Every one of those technologies gave the institutions of their eras time to absorb it.

Artificial intelligence will not. Here is a technology that improves upon itself in a matter of months, that writes the code for its own next version, like a railway track laying more railway tracks at an ever-faster pace. No nation possesses the legislative or policy-making capacity to evolve at that speed. Neither does any organization's operating model, governance, or architecture. This is precisely the problem our model exists to solve, for the leaders and practitioners doing the work.

Today’s paradox in artificial intelligence is, for many organizations, a tension between AI’s promised benefits and the hard reality that approximately 80% of AI pilots fail to scale into production or deliver measurable business value.

The Center for Trustworthy AI has worked on these issues with organizations around the world, and has prepared this paper, The Frontier Campus, to provide organizations with a practical strategic and technical blueprint that they can make their own, growing beyond what are often strategically dubious pilots to instead build true multi-agent artificial intelligence at global scale across different cloud technologies.

It has become obvious how difficult so many organizations are finding it to actually craft and execute their AI strategy, in part because of the (often) decades they’ve spent kicking their proverbial data can down the road, in part because it turns out that enterprise-grade AI really does require the adoption of ecosystem-oriented architecture to truly scale, but largely because many organizations have no idea where to start. Many lack the wherewithal to really assess where they stand on day one, and to identify areas where they must mature to get to day 100 (and beyond).

We’ve learned a great deal about maturity and readiness for - and responsibility to the ethics of - AI over the past year, as well. It’s now time to broaden the thesis, so in this second edition we offer a model through which organizations may realistically assess their current maturity to adopt and scale artificial intelligence, and then identify specific areas to invest time, talent, and funding along their journey.