As AI becomes more ubiquitous, it’s also become more autonomous — able to act on its own without human supervision. This demonstrates progress, but it also introduces concerns around control over AI. The AI Arms Race has driven organizations everywhere to deliver the most sophisticated algorithms around, but this can come at a price, ignoring cultural and ethical values that are critical to responsible AI. Here are five predictions on what we should expect to see in AI in 2021:
- Something’s going to give around AI governance. Though regulation hasn’t reached a boiling point yet, AI governance will continue to be a hot topic in 2021. As AI becomes more pervasive, more and more stakeholders are waking up to the potential problems it introduces to the public. In response, organizations everywhere — from the most cutting-edge to the laggards — will be expected to deliver AI systems that are responsible, transparent, and unbiased. But whose responsibility is it to make sure this happens and regulates AI – the government, businesses, industry groups, or some combination?
If businesses want to regulate themselves before the government does, they will have to take steps to ensure the data that feeds their AI is fair and unbiased, and that their models are empathetic, transparent, and robust. Organizations will also need to implement a way to closely monitor their AI — with a robust simulation capability and automated oversight –, so it doesn’t go off the rails as it “learns.” So far, businesses have come up short, and unless they make meaningful strides, government regulators will crank up the heat this year.
- Most consumers will continue to be sceptical of AI. With several big consumer brands in the hot seat around questionable AI ethics, most people still don’t trust AI. For many, it’s because they don’t understand it or even realize they’re using it daily. Consumers are getting so many AI-powered services for free — Facebook, Google, TikTok, etc. — that they don’t understand what they’re personally giving up in return — namely their personal data. As long as the general public continues to be naïve, they won’t be able to anticipate the dangers AI can introduce or how to protect themselves — unless the market better educates customers or implements regulations to protect them.
Despite this, there’s some evidence that we’re turning the corner on AI’s trustworthiness. 81% of business leader respondents to Pega’s upcoming survey said they’re optimistic that AI bias will be sufficiently mitigated in five years. Businesses had better hope this turns out to be true – because as more of the public wakes up to how AI impacts their lives, and in some cases plays favourites, they will continue to ask harder questions that further erodes trust in AI, forcing businesses to have to answer to them.
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- Digital transformation (DX) finds its moment. With COVID-19 spotlighting the drastic need for digital transformation (and in many cases fast-tracking efforts) in 2020, the trend will only continue in 2021. This year, businesses were forced to take those five-year DX vision plans and execute them in only five months to respond to the new realities brought on by the pandemic. After seeing this success, leaders wonder what other DX projects are possible that they once felt were out of reach.
Given growing consumer queries, distributed workforces, and increased challenges, there will be more demand for automation in AI and close monitoring of those AI interactions. Hyper-automation will help streamline workflows in the post-COVID era. However, businesses will still need to understand what their AI is doing in order to predict it and monitor it, so they don’t disenfranchise any of their customers.
- Organizations will increasingly push AI to the edge. As computing and storage capability grows, more processing power and functionality will be accessed at the enterprise network edge. Our study found that 41% of business leaders believe extended edge use cases are highly dependent on the maturation of AI and other technologies like automation and machine learning, suggesting a complementary relationship between the two technologies. With the proliferation of the internet of things (IoT) devices and the increased adoption of 5G fueling this trend, computing power at the edge will grow, and the ability to leverage AI at the edge will grow too.
To be successful, businesses will want to ensure everything on the periphery syncs up to a central brain for a holistic customer view. Isolating AI into edge computing silos would ultimately lessen the power of AI. Only by ensuring the edge is constantly connected to a central location will they be able to push the boundaries of what’s possible.
5. ModelOps will become the “go-to” approach for AI deployment. Much like the way DevOps has given structure to the way applications are deployed, ModelOps will reach a tipping point in 2021 as a way for mainstream businesses to better develop and operationalize their AI models. This will give them a more systematic way to quickly and responsibly develop, test, and deploy AI models more efficiently via the Cloud.
Next year, more organizations will focus on effectively moving their AI models up the food chain with ModelOps while ensuring there’s enough structure for IT to put in guardrails. That process should not be so restrictive that agility and innovation are stifled – particularly with Citizen data scientists. In 2021, ModelOps will help organizations everywhere strike that right balance.
There’s no doubt that AI will be in the spotlight in 2021. And scepticism is not going away. As AI becomes more pervasive, more structure and controls around processes and policies on AI, and how it is developed and used, are sure to emerge. With so much potential to help businesses and consumers everywhere, AI can change the game, if handled responsibly.