The predictions of PwCs 2020 suggest that companies are more worried about being disturbed than doing the disturbance.
Collaboration is the key to make AI and IoT work
Companies must work together and ensure buy-in to be successful with transformational technology such as artificial intelligence and the Internet of Things.
The biggest barrier to implementing artificial intelligence on a scale is not about technology, but about people and business practices. In a new report, PwC discovered that companies are reducing AI ambitions.
What is the challenge? Measure ROI, get an approved budget and train current employees. In the 2020 AI Predictions report, PwC points out operational barriers and reinforces the need for sustainable deployment of managers.
Senior leaders know the wave is coming: “Ninety percent of executives surveyed believe that AI offers more opportunities than risks, and nearly half expect AI to disrupt their geographic markets, the sectors in which they operate or both.”
At the same time, only 12% of the survey’s 1,062 respondents said they intended to disrupt their own industry, demonstrating that “nearly four times as many respondents are afraid of disruption as they intend to disrupt”.
The report recommends these five broad priorities for AI projects in 2020:
- Get on board with boring AI
- Rethink further training
- Lead on risk and responsibility
- Operate AI – integrated and to scale
- Reinvent your business model
The most interesting and useful part of the report is specific tasks that belong to each priority. These are the tasks that are easy to postpone or ignore. Without taking these steps, it is much harder to get AI to work on a scale and to transform day-to-day operations and a long-term business model. Here are five AI tasks that should be on your project list.
Make an AI intake strategy
This is one of the most boring but important parts: identify where AI can have the greatest business impact and build the technical and human capabilities needed to succeed. Indicate AI efforts on paperwork that no one wants to read anyway.
SEE: Telemedicine, AI and deep learning are a revolution in healthcare (free PDF)
The report’s authors say the best way to use AI to work efficiently and increase productivity is to use the technology to extract information from tax forms, bills of lading, invoices, and other documentation. Look for tasks that are common throughout the company to create reusable AI solutions, such as a model for processing unstructured text.
Set a multilingual goal
This is part of the reconsideration of refresher work – if you only offer technical training to your non-technical employees, you are doing it wrong.
Collaboration between business units is generally crucial for transformation technologies, and up-skilling between teams is also part of that.
The report recommends making it a priority to allow different specialists to speak the language of other specialties. To encourage cross-functional collaboration, companies must set up ‘multilingual’ teams, where data engineers, data ethicists, data scientists and MLOps engineers are part of application development and business teams. “Also train members of the technology team on the business side so that everyone speaks the same language.
As 50% of executives acknowledged in the survey, team members must “give people immediate opportunities and incentives to apply what they have learned, turning knowledge into real skills that improve performance.”
Build up your AI risk confidence
PwC found that only about a third of respondents “fully addressed risks related to data, AI models, output and reporting”. The authors of the report suggest that companies support their words with promotions. The Responsible AI Toolkit from PwC lists these five dimensions of responsible AI:
- Board
- Interpretability and explanation
- Prejudice and honesty
- Robustness and safety
- Ethics and regulations
The survey found that about 50% of managers face the “explainability” challenge. The report also recommends working with risk and compliance functions to develop appropriate AI standards, controls, tests and monitoring. Companies also need a budget for AI assurance, comparable to that for cyber security or cloud security.
Make your data trusted data
Data must be “accurate, standardized, labeled, complete, biased, secure, and secure.” This step is crucial to make AI operational on a scale. The biggest data challenges are:
- Integrate data from the entire organization (45%)
- Integration of AI and analysis systems (45%)
- Integrate AI with IoT and other technical systems (43%)
The survey found that only a third of respondents said labeling data was a priority for 2020. The report recommends that, even if AI efforts are focused on a single function or process, it is essential for companies to collect secure, high-quality data from within (and outside) the organization.
Earn money with cognitive assets
This task is part of renewing the work of the business model. Companies must create unique data assets and cognitive assets: AI models that encapsulate a company’s experience and expertise in a specific domain.
To see ROI from AI projects, companies must be able to take advantage of the insights and results that these new assets offer.
These tasks are so important because “AI development is very different from software development and requires a different mindset, approach and tools.” As the development of AI models requires a “test and learn” approach, business teams must also constantly learn and refine their approach.
Of the 1,062 respondents this year, 54% hold C-suite titles, more than half work in IT and technology positions, and 36% came from companies with revenues of $ 5 billion and more. The research was conducted by PwC Research.
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