How you can get your business ready for AI
- 90% of executives see promise in the use of artificial intelligence.
- AI set to add $15.7 trillion to global economy.
- Only 4% planning major deployment of technology in 2020.
They say you have to learn to walk before you can run. It turns out the same rule applies when it comes to the rollout of artificial intelligence.
A new report on AI suggests that companies need to get the basics of the technology right before scaling up its use. In a PwC survey, 90% of executives said AI offers more opportunities than risks, but only 4% plan to deploy it enterprise-wide in 2020, compared with 20% who said they intended to do so in 2019.
Slow and steady wins the race
By 2030, AI could add $15.7 trillion to the global economy. But its manageable implementation is a global challenge. The World Economic Forum is working with industry experts and business leaders to develop an AI toolkit that will help companies understand the power of AI to advance their business and to introduce the technology in a sustainable way.
Focusing on the fundamentals first will allow organizations to lay the groundwork for a future that brings them all the rewards of AI.
Here are five things PwC’s report suggests companies can do in 2020 to prepare.
1. Embrace the humdrum to get things done
One of the key benefits that company leaders expect from investment in AI is the streamlining of in-house processes. The automation of routine tasks, such as the extrication of information from tax forms and invoices, can help companies operate more efficiently and make significant savings.
AI can already be used to manage the threat of fraud and cybersecurity – something that 38% of executives see as a key capability of the technology. For example, AI can recognize unauthorized network entry and identify malicious behaviour in software.
2. Turn training into real-world opportunity
For companies to be ready for AI at scale, they need to do more than just offer training opportunities. Employees have to be able to use the new skills they have learned, in a way that continuously improves performance.
It’s also important to make teams ‘multilingual’, with both tech and non-tech skills integrated across the business, so that colleagues can not only collaborate on AI-related challenges, but also decide which problems AI can solve.
3. Tackle risks and act responsibly
Along with helping employees to see AI not as a threat to their jobs but as an opportunity to undertake higher-value work, companies must ensure they have the processes, tools and controls to maintain strong ethics and make AI easy to understand. In some cases, this might entail collaboration with customers, regulators, and industry peers.
As AI usage continues to grow, so do public fears about the technology in applications such as facial recognition. That means risk management is becoming more critical. Yet not all companies have centralized governance around AI, and that could increase cybersecurity threats, by making the technology harder to manage and secure.
4. AI, all the time
Developing AI models requires a ‘test and learn’ approach, in which the algorithms are continually learning and the data is being refined. That is very different from the way that software is developed, and a different set of tools are needed. Machines learn through the input of data, and more – and better quality – data is key to the rollout of AI.
Some of AI’s most valuable uses come when it works 24/7 as part of broader operational systems, such as marketing or finance. That’s why leaders in the field are employing it across multiple functions and business units, and fully integrating it with broader automation initiatives and data analytics.
5. A business model for the future
It’s worth remembering that despite AI’s growing importance, it is still just one weapon in the business armoury. Its benefit could come through its use as part of a broader automation or business strategy.
Weaving it successfully into a new business model includes a commitment to employee training and understanding return on investment. For now, that investment could be as simple as using robotic process automation to handle customer requests.
AI’s impact may be incremental at first, but its gradual integration into business operations means that game-changing disruption and innovation are not far away.