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AI is the future that tech companies are worried about

Tech companies in New York realize their businesses will depend on mastering emerging technologies, but they are facing a shortage of expertise in them.

That was the main finding of a new industry survey, in which 60% of large companies and 51% of startups said that 'emerging technologies'—artificial intelligence chief among them—would be 'extremely important' for the future of their businesses.

The survey which collected responses from 400 companies with at least $1 billion in revenue and 86 startups with fewer than 20 employees, did not ask why they don't feel ready.

'Access to technical talent remains the great limiter for technology companies, and that is true both in and outside New York,' said Julie Samuels, executive director of Tech:NYC, which conducted the survey with Accenture.

Briefing: Chinese AI companies raised $31.7 billion in the first half of 20181 min read

ChinaNews What happened: Chinese artificial intelligence companies raised a combined $31.7 billion in the first six months of this year, representing almost three-quarters of the worldwide total of $43.5 billion, Zhang Xueli, deputy director of China Academy of Information and Communications Technology, said at a conference in the eastern city of Suzhou.

Why Companies That Wait to Adopt AI May Never Catch Up

While some companies — most large banks, Ford and GM, Pfizer, and virtually all tech firms — are aggressively adopting artificial intelligence, many are not.

Such systems will probably add little value to your business if they are completely generic, so time is required to tailor and configure them to your business and the specific knowledge domain within it.

For example, Memorial Sloan Kettering Cancer Center has been working with IBM to use Watson to treat certain forms of cancer for over six years, and the system still isn’t ready for broad use despite availability of high-quality talent in cancer care and AI.

Unless you are employing some AI capabilities that are embedded within existing packaged application systems that your company already uses (e.g., Salesforce Einstein features within your CRM system) the fit with your business processes and IT architecture will require significant planning and time for adaptation.

If you want to affect customer engagement, for example, you will need to develop or adapt multiple AI applications and tasks that relate to different aspects of marketing, sales, and service relationships.

New AI systems typically mean new roles and skills for the humans who work alongside them, and it will typically require considerable time to retrain workers on the new process and system.

For example, investment advice companies providing “robo-advice” to their customers have often attempted to get human advisors to shift their focus to “behavioral finance,” or providing advice and “nudges” to encourage wise decisions and actions in investing.

The algorithms can be updated as the machine learns from patterns in new data, but they will need to be monitored by subject matter experts to ensure the machine is interpreting the change in business context correctly.

For instance, if an AI system is trained to create product recommendations based on customer demographics and the demographics change dramatically in new data, it may provide biased recommendations.

By the time a late adopter has done all the necessary preparation, earlier adopters will have taken considerable market share — they’ll be able to operate at substantially lower costs with better performance.

The obvious implication is that if you want to be successful with AI and think there may be a threat from AI-driven competitors or new entrants, you should start learning now about how to adapt it to your business across multiple different applications and AI methods.

Such central groups focus on framing the problems, proving out the business hypothesis, modularizing the AI assets for reusability, creating techniques to manage the data pipeline, and training across businesses.

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