5 Easy Criteria To Get Quick Returns on AI Investments
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Minimum investment, maximum return and pretty fast too.
Based on research below, we intend to adopt these findings ASAP for the Syndicate. While there are some very positive use cases, continue to cast a skeptical eye on much of the breathless business value chatter about AI today.
We have seen many major AI research breakthroughs deliver significant new technical capabilities this decade. And more are coming. But most published market surveys inaccurately predict rapid, broad-spread enterprise technology adoption. That’s wishful thinking.
These surveys are cognitively-biased exercises in deception. The authors deceive themselves. Most predictions of widespread adoption are self-serving. And wrong. We’ve been finding:
Most enterprise AI activity has not passed beyond the serious play stage. It’s confined to:
- Experimentation and technical training
- Pilot projects (that fail to achieve production use)
- Reuse of older analytical tools and methods disguised as AI breakthroughs
Virtual agents and virtual assistants account for the largest single enterprise investment area. These uses have merit. But users and sponsors are underwhelmed by the end results.
Implementers soldier on because they are delivering cost reductions that sustain management interest.
There are a few shining lights to analyze. Research from the NBER (National Bureau of Economic Research) is one such standout. But who has the skills, time and energy to read academic research? Us for starters, at the Analysts Syndicate. We’ll translate.
One of my (many) academic favorites is Erik Brynjolfsson (of MIT and NBER). Erik and his coauthors have published many valuable peer-reviewed papers on the business impacts of technology adoption.
His central AI related finding?
He can’t find any evidence of a significant economic impact of AI adoption thus far.
In Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics Brynjolfsson, Rock and Syverson (2017) say that
AI’s full effects won’t be realized until waves of complementary innovations are developed and implemented.
And it may take many decades or longer to develop those waves of innovation.
Look at other breakthrough technologies (which economists are now labelling ‘general purpose technologies’) such as:
- Steam Engines
- Heavier-than-air aircraft
- Gasoline engines
- Chips (semiconductor devices)
The uses for electricity, steam, airfoils, gasoline and chips continue to evolve. Commercial use of electricity has been with us for a century and a half. New uses for electricity and electricity-powered devices continue to emerge. Likewise for other general purpose technologies.
How much evolution is enough? (It’s easy to say in hindsight but that really only tells us if we’ve waited too long.) How will we know when there has been enough complimentary innovation to say the core breakthrough technology is now ready for large scale deployment and exploitation?
A clever answer
Brynjolfsson, Hui and Liu turned the question around. They looked for AI technologies and use cases where there didn’t seem to be any major needs for complimentary innovations. (They also wanted a use case and technology pair that wouldn’t require business process, organizational or cultural changes.) They settled on testing Automatic Machine Translation (AMT) in eCommerce.
In Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform these researchers report on the impact of applying AMT on eBay’s web commerce platform and found large, statistically significant improvements in international trade sales volumes from 17 to 20 percent.
Wake Up Call
Some new AI technologies (like AMT) can deliver benefits quickly and at minimal cost.
Seek opportunities that do not require significant business process, organization, technical or culture change.
Five Easy Criteria to Seek
- Fast time to implement. Can a viable production instance be up and running in 90 to 120 days? (Avoid massive reengineering projects of all types.)
- Low levels of internal disruption and reinvention. Let the results drive disruptive change instead of requiring disruptive change to achieve the results.
- Suppliers and service providers with the right business mode. (If a significant part of their business is time and materials consulting, you may be failing points 1 and 2 and taking on unjustified risk.)
- Relevant real world experience. (Demand verifiable references – use cases – that are already in volume production. Visit them and dig deeply.)
- Revenue enhancement (which beats cost reduction.)
You can fail all these tests and still succeed. But you can succeed more quickly, with lower cost and risk, if your project passes all these tests. Succeed quickly and then iterate.
- Apply AMT to your e-commerce initiatives to almost painlessly increase sales and expand available markets.
- Apply the Five Easy Tests before making strategic AI investment decisions.
- Read at least section 1 of Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
- Respond back to this posting (either via public comment or private communication) with (a) your own examples that conform to the 5 easy tests, and (b) additional easy tests you would apply to make the list stronger.
Disclosure: I am the author of this article and it expresses my own opinions. This is not a sponsored post. I have no vested interest in any of the firms or institutions mentioned in this post. Nor does the Analyst Syndicate.