In our recently released white paper, “Artificial Intelligence and the Future of Work,” we challenge thinking on how Artificial Intelligence (AI) will be one of largest changes to how human beings perform work. However, exactly like previous waves of technological innovation, it is prudent to resist the urge to see AI as a Swiss army knife solution that can fix anything, anywhere.
AI’s current applications are narrow, more like a scalpel. Similar to the .com and 2.0 bubbles, companies should be careful to not rush head long into purchasing AI simply for the sake of allocating funding toward something related to AI. We can spare trite examples, but we all remember investors in internet-related companies they did not understand thinking “It’s on the internet! It can never lose! It’s the future!”
Kartik Hosanagar and Apoorv Saxena of the Harvard Business School point out in a recent article, “The First Wave of Corporate AI is Doomed to Fail,” that most companies, when mixed with new technology not fully understood will likely not maximize their investments.
So how do organizations take a detailed and focused view on areas that are best suited to AI investments? We believe companies should, like they do with product development, see it as a portfolio play. Invest a little in five to seven areas that could be automated. Spend less, and test more. Once you learn which areas seem to respond best, invest more into the two to three most promising.
Sound familiar? These principles are similar to fiscally sound IT investment and development today with Agile. There are corporate structures that are extremely well suited to this type of unknown, tech heavy, risky investment: V.C. Firms. The V.C. approach of investing a little into a portfolio of potentials, then investing further as the technology shows more promise, can also be performed inside of big business.
How will embed AI investment principles in your organization?
AI’s current applications are narrow, more like a scalpel. Similar to the .com and 2.0 bubbles, companies should be careful to not rush head long into purchasing AI simply for the sake of allocating funding toward something related to AI. We can spare trite examples, but we all remember investors in internet-related companies they did not understand thinking “It’s on the internet! It can never lose! It’s the future!”
Kartik Hosanagar and Apoorv Saxena of the Harvard Business School point out in a recent article, “The First Wave of Corporate AI is Doomed to Fail,” that most companies, when mixed with new technology not fully understood will likely not maximize their investments.
So how do organizations take a detailed and focused view on areas that are best suited to AI investments? We believe companies should, like they do with product development, see it as a portfolio play. Invest a little in five to seven areas that could be automated. Spend less, and test more. Once you learn which areas seem to respond best, invest more into the two to three most promising.
Sound familiar? These principles are similar to fiscally sound IT investment and development today with Agile. There are corporate structures that are extremely well suited to this type of unknown, tech heavy, risky investment: V.C. Firms. The V.C. approach of investing a little into a portfolio of potentials, then investing further as the technology shows more promise, can also be performed inside of big business.
How will embed AI investment principles in your organization?