2021 Predictions: AI & Automation in Business
A lot has changed in 2020 – more than we could have ever imagined. In the world of technology, that rings true, and we can expect the acceleration of technology adoption that we saw this year continue well into 2021. The impact of the pandemic on businesses will be lasting.
This year, we’ve seen a lot of industries and disciplines turn to AI and automation in their hour of need – such as finance, HR, and supply chain. There was more urgency than ever before. Urgency to meet revenue targets, meet margins, and make efficiency gains, at a time when these sectors were under more stress than ever before. As a result, we’ve seen lots of companies quickly digitise, and start introducing AI and automation into their business to solve problems that needed urgent solutions.
We’re now years ahead of where we were in early 2020 – but this doesn’t show signs of slowing down. In fact, digitising is no longer enough. Businesses need that additional layer of intelligence that AI and intelligent automation can bring.
In 2021, I have three key predictions for how this will take shape:
Goodbye chatbot, Hello humanoid avatar
Once heralded as the next frontier in customer experience, and widely used by retailers, banks, and online marketplaces, chatbots can no longer provide the level of service the modern consumer wants. Particularly after a year spent largely in our own homes, more than ever, we’re all craving human connection. What’s more, we’ve all experienced chatbots who were no help at all – prone to error, many have to speak to someone on the phone anyway. Where chatbots fall short, AI-driven humanoid avatars can do much, much more. In 2021, we will see the first early adopters of this technology, like banks, universities, and retailers, bring their AI avatars to our screens.
For customers, this might mean logging on to do your online grocery shop and being met by a friendly avatar who can run through the latest deals, point you in the direction of ingredients, and actually act as a ‘personal shop assistant’ – far beyond what chatbots are capable of. For businesses, there are many benefits beyond customer experience: opportunities to cross- and upsell, the ability to answer queries to relieve the burden on customer services, and even train customers on new products or services. Humanoid avatars and conversational AI will be a major trend next year, and it will help brands build lasting relationships with their customers.
The power is in the data
In Fortune 500 companies, when senior executives make decisions, it’s typically based on past events within that company – plus some manual forecasting on what might happen next. This might include demand for certain products in certain regions or projections of revenue across business units, for example. In a post-COVID-19 world, this is no longer enough of a basis to make decisions.
Next year, we’ll see a huge move towards using AI and data science to make predictions for the future, as Forrester , enabling business leaders to make informed decisions based on much more data than they ever have before. With datasets going back years – and the need to look at external data like weather records, demographic changes, or government policies – it’s only possible to really leverage all of this data with machine learning and AI as a helping hand. Moreover, machine learning algorithms learn from themselves over time, meaning accuracy continues to grow with each new piece of data that’s added. This gives execs a completely new view of their business, enabling them to see correlations and make predictions they couldn’t before, which will help them propel their business forward.
Laggard industries become AI leaders
Sectors like supply chain, logistics, HR, and finance have long been known for relying on manual, paper-based processes – and for lagging behind their more forward-thinking counterparts. The pandemic changed all that. Now, we will see businesses move operations to a ‘handsfree’ model, to future-proof themselves through innovation.
In supply chains, for example, this will take shape by injecting AI and automation throughout logistics processes and operations. Look at warehouse management: inbound and outbound processing, returns, inventory management, and more were all done by hand. Now, AI can take over, selecting the right pallets and classifying them for a production order, entirely automated.
‘Handsfree’ operations will also enable senior teams to work in predictive mode – to predict future trends and events within their supply chain – and even prescriptive mode, where AI can recommend solutions and advise on the best course of action. Here, AI and automation can enable logistics leaders to weather any potential storms.
5 Minutes With PwC on AI and Big Data in Manufacturing
Please could you define what artificial intelligence is, and what Big Data is?
AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications.
Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data.
How does AI and Big Data work together?
Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important
How can this be applied to a manufacturing setting?
Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.
Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?
One view is, again, the economic benefits of AI, which come in manufacturing as a result of:
1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies.
2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services.
Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030.
How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?
It’s really about the investment you make now, in order to futureproof your business.
We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success.
What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?
AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including:
1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process.
2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation.
Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest.
What are the challenges when it comes to adopting AI and Big Data in manufacturing?
has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically.
Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation.
Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:
- 40% → Technologies not mature
- 40% → Workforce lacks skills to implement and manage AI
- 36% → Uncertain of return on investment
- 33% → Data is not mature yet
- 32% → lack of transparency and trust
- 24% → Work councils and labour unions
- 22% → Regulatory hurdles in home & important markets
One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured.
Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this.
What are the current trends in AI and Big Data in manufacturing?
- We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing
- Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence
- There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation
What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months?
Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys.