Artificial Intelligence – the six priorities for Irish organisations

AI is going to disrupt and transform operations and employment in every organisation.

What are the keys to unlocking its potential?

The AI opportunity Irish business can't afford to ignore

Artificial intelligence (AI) has the potential to transform the Irish economy. In 2017, a report conducted by PwC predicted that Ireland's GDP would be 11.6% higher in 2030 as a result of AI – the equivalent of an additional €48bn. But to reap the benefits of the AI revolution, business leaders need to understand the different types of AI technology, and how to implement them into their organisations effectively.

Recognising the potential of AI, an overwhelming majority of the respondents we contacted in conducting our 2019 analysis believe that it is important that Ireland puts itself at the forefront of the AI revolution, investing in the skills and technology needed to successfully adopt AI.

Our research, conducted by PwC and the Analytics Institute, shows that the majority of Irish leaders know that AI is going to significantly change their operations in the next five years, or they risk losing competitive advantage. To achieve that, companies need to know where AI could be introduced in their business to create the maximum value. But that knowledge is not common, and not every organisation can or should adopt the same AI technology in the same way as their peers.

Half say they do not fully understand the impact that AI will have.

The lack of expertise about where to start AI initiatives is matched by a lack of appetite and clarity about how to introduce new automated technology solutions. 40% of respondents have no plans to roll out any AI initiatives in the next 12 months. Half say they do not fully understand the impact that AI will have, and the same proportion have not developed plans to adapt their workforce's skills to align with the changes AI will bring.

Artificial Intelligence requires organisation-wide strategy as well as an infrastructure and staff equipped to adopt and support the changes that it will bring. However, the deficit between vision and execution remains the largest stumbling block. Organisational leaders know AI is important and needs to be embedded in their operations, and soon. But they are reluctant to take actions necessary to make sure that happens.

To understand where Irish organisations currently stand on AI development and deployment, we surveyed almost a hundred executives and practitioners about their perspectives on the age of automation. We compared our results to the outputs of PwC's equivalent survey in the USA to benchmark local sentiment and thinking on AI.

It is clear from our findings that AI needs to be demystified and woven into the change agenda of Irish organisations. Our analysis delivered some fascinating results, identifying six priorities every leader must know to successfully implement AI in their organisation.

1: Clarity; 2: Strategy; 3: Data.
4: Skills; 5: Trust; 6: Convergence.

1. Clarity: Understand AI and how it fits in your business

We believe a critical aspect in getting businesses to face up to AI is to make it less daunting. The volume of information about AI is overwhelming and might make the uninitiated believe they are facing the rise of the machines or the end of employment. But the reality is very simple, and should be a source of inspiration for leaders.

Once you understand what AI is, you can begin to see its potential. It can deeply support your business strategy, reducing costs, improving products and services and drive efficiencies at every level.

Artificial Intelligence refers to computer systems that can sense their environment, think like humans, learn from the data they process and take appropriate actions as a result. This ability to respond to the environment sets AI apart from the automation of routine tasks. Machine learning algorithms and chatbots are examples of AI that are already used by businesses today.

Our definition of AI, however, goes beyond this. We also include automation, the replacement of repetitive manual and cognitive tasks by machines that are not necessarily 'intelligent' and that instead have basic rules-based capabilities. We include these as we recognise that they are a key step in the evolution of advanced intelligent technologies.

We believe that there are four different types of AI:

Assisted intelligence

Helping people make decisions faster and better. This involves the use of AI systems to inform decision-making. For example, computers help statisticians forecast the future by solving thousands of equations in seconds. Crucially, humans are still necessary to define the problem and define the boundaries of reasonable solutions. This type of AI can be seen as enhancing the decision-maker's cognitive ability.

Augmented intelligence

Working with people to find new solutions. At this stage of AI, humans and machines learn from each other, redefining the breadth and depth of what they do together. For example, augmented intelligence might advise humans what move to play in chess or what variables to use in order to predict GDP or the use of micro-robotics in surgery. These solutions involve the 'learning' from experience.

Autonomous intelligence

Automating decision making processes without human intervention. Think self-driving cars or automated trading. For this type of intelligence the machine is entrusted with decision rights. In many cases the decision making is so complex that it would be too slow or unsafe having the human in the loop. However, as technology develops, decision rights will be passed from machine to machine, as well as human to machine.

Automated intelligence

Doing the tasks that people used to do. Unlike assisted intelligence, automation replaces humans in the task at hand. Automation is most suited to completing clearly defined, rules-based and repeatable tasks, but non-routine tasks are increasingly being automated as well. These include automated assembly lines, software-based agents simulating humans and automated back-office functions.

Illustration explaining assisted intelligence, automation, augmented intelligence and automation intelligence.

2. Strategy: Organise for return on investment

Implementing AI requires unity of purpose and commitment from across your organisation. It has the potential to be completely transformative, so stakeholders from across the business need to be involved in developing a strategy that enables its introduction.

Transformation needs to deliver tangible returns, so it is in your best interest to develop an approach that will create maximum value and is able to scale upwards and across your organisation.

When introducing AI it is essential that a diverse cross-functional multidisciplinary team representing all parts of the business is created. It cannot be done in a silo – as it will change operations and decision making in every function.

Oversight from a cross-functional team that includes all business functions, IT and staff with specialised AI skills and experience will help you to establish a clear AI strategy. The team should be prepared to work in an agile, iterative way, to develop ideas about where and how AI can be introduced, execute those plans and learn from the results. If everyone is involved, everyone wins.  

Formalising your approach to AI in this way means that successful, modular projects can be replicated and incorporated into the wider business. Developing an AI model for one specific task can enhance an existing process or solve a well-defined business problem, while simultaneously having the potential to scale to other parts of your organisation.

One fact about AI algorithms that may surprise you: there aren't that many of them. The same algorithms are capable of solving most business problems for which AI is relevant. If you can successfully apply them in one area of your business, you can usually use them in others.

The goal is to build a portfolio of reusable building blocks, to create both quick ROI and the momentum to scale.

26% say they don’t know who implements and governs AI in their organisation.

Organisational leaders are not clear about the actions they need to take to turn AI into a value-adding strategic component of their operations. Additionally, we can see from the results of our survey that there is a lack of clarity about who in an organisation is responsible for implementing and governing AI.

A structured introduction, clear goals and clear responsibilities are key to making your AI strategy a success. The responsibilities of the cross-functional team should seek to resolve key business questions, such as how to identify use cases and how to develop accountability and governance. It should determine technology standards, including architecture, tools, techniques, vendor and intellectual property management and just how intelligent AI systems need to be. It also needs to address skills gaps across the business and foster a culture of innovation and collaboration.

Most critical of all is to strengthen your business' data function. AI is driven by data, and good data produces good outcomes.

How will you implement and govern AI in 2019?

Graph outlining the response to the question how will you implement and govern AI in 2019?

Actions

AI strategy belongs to everyone

AI strategy belongs to everyone


  • Bring together AI, IT and core operations leaders in a structured way to manage priorities, data strategy, resources and use cases.

Create the blocks to build success

Create the blocks to build success


  • Instead of applying AI to a complete process, focus on specific tasks that are common across the business and develop reusable AI solutions.

3. Data: Locate and label to teach machines

AI can be used with data and analytics to better manage risk, help employees make better decisions and automate customer operations. The top priority when integrating AI and analytics systems is to identify and extract valuable insights from the totality of the data your business holds, right across the organisation.

But our survey highlights a significant challenge. Organisations in Ireland and internationally are not providing the foundation that AI needs to be successful – robust, reliable and complete data. Only one in eight respondents said that data labelling and standardisation was a top priority for their business.

12% say standardising, integrating and labelling data is a top priority for their organisation.

For Machine Learning to detect significant patterns in the present and predict the future, it must be taught. Show it enough historical data on consumer behavior, for example, and it will eventually be able to predict how those consumers—and others like them—will behave going forward.

But to create the data sets needed to instruct your AI, you have to label and standardise data consistently. Your cross-functional team should be tasked with creating and monitoring data standards, as well as developing systems and processes that make it easier for employees to create usable, labeled data sets for future use.

Even with better data governance, there will be challenges. Some business problems have AI solutions that require data that may not be available or may not exist. However, new augmented machine learning techniques can enable AI to produce its own data based on a few samples. They can also transfer models from one task with lots of data to another one that lacks data.

The AI policy landscape is still in its infancy. Many policymakers are calling for comprehensive guidelines that address ethical algorithms, workforce retraining, public safety, antitrust and transparency. National AI strategies are emerging, and three-quarters of Irish respondents are calling on the Irish Government to create a public policy on AI – a sentiment that was also strongly reflected in PwC's CEO Survey this year.

At the same time, emerging regulations around data privacy will also impact AI and may limit its growth because it affects how companies operating globally can use data generated across territories. GDPR gives individuals the right to see and control how organisations collect and use their personal data, as well as recourse should they suffer damages due to bias or cybersecurity breaches.Your AI strategy should be mindful of those rights, and how data is used.

Which AI data-related issues will be the top priorities for your organisation in the year ahead?

Graph outlining the response to the question which AI data-related issues will be the top priorities for your organisation in the year ahead?

Actions

Label and standardise

Label and standardise


  • Identify the data sets you need in order to train AI to solve specific business problems. Prioritise capturing and labeling that data in line with enterprise-wide standards.

Use new AI data tools

Use new AI data tools


  • Lean and augmented data learning, transfer learning and other AI approaches—often integrated into existing applications—can help do more with less much more easily.

Pay attention to policy

Pay attention to policy


  • Align the teams that are helping shape policy in different jurisdictions. Address compliance by applying best practices globally.

4. Skills: Build an AI-ready workforce with an agile mindset

As more tasks become automatable through AI and sophisticated algorithms, jobs are being redefined and recategorised. A third of people worldwide are worried about losing their jobs to automation. But 74% are ready to learn new skills or completely retrain in order to remain employable in the future. More trained users, developers and data scientists are going to be needed to help integrate AI in businesses.

But the talent impact of AI will not be solved solely from hiring outside of your organisation, from what is already a diminishing pool of skilled talent. You have a built-in talent pool already, and it is in your interest to maximise it.

Upskilling staff to be ready to work with AI is an urgent action recognised by Irish business leaders. An overwhelming majority believe that the country needs to invest significantly more in the skills and technologies needed to successfully adopt AI. Over half stated their inability to recruit sufficiently AI-skilled staff is a significant threat to their organisation in the five years.

50% stated that their inability to recruit sufficiently AI-skilled talent is a significant threat.

In spite of seeing the changes coming and the gaps that exist, businesses themselves are not preparing for the coming revolution. Almost half have no plans to help their workforce adapt or learn the skills necessary to support the integration of AI.

As you implement your AI strategy, most employees will need training to become AI users. They'll learn how to work with the company's AI-enhanced applications, support good data governance and get expert help when needed.

A more specialised group, perhaps 5 to 10% of your workforce, should receive further training to become developers: line-of-business professionals who are power users and can identify use cases and work closely with IT specialists to develop new AI applications.

Finally, a small but crucial group of data engineers and data scientists will do the heavy lifting to create, deploy and manage AI applications.

The workforce aspect of your AI strategy should provide a structure in which all three groups work together successfully across your business.

You'll then need an equally systematic approach to filling those roles—both internally and externally—and encouraging the different groups to collaborate. Enterprise-wide upskilling should address both technical skills and digital ways of working. Performance and compensation frameworks will have to adapt at the same time.

Many employees will successfully upskill to fill new roles, but some won't be able to make the transition. So you need to prepare for some turnover which will need to be managed.

Forging partnerships with colleges or launching apprenticeship schemes is another.

Which of the following AI scenarios do you perceive to be a real threat in the next five years?

Graph outlining the response to the question which of the following AI scenarios do you perceive to be a real threat in the next five years?

Actions

Adapt your workforce strategy

Adapt your workforce strategy


  • Recruiting and upskilling are just two pieces of the puzzle. You also need to systematically identify how AI is changing job roles and skills; evolve upskilling, performance and compensation frameworks and develop new collaborative processes.

Cultivate a culture of continuous innovation

Cultivate a culture of continuous innovation


  • To attract and retain talent, use AI responsibly. Involve your workforce in the adoption of AI and make it part of your organisational change toolkit. Give people the resources they need and empower them to innovate in a collaborative environment.

Train, coach, collaborate

Train, coach, collaborate


  • For your business specialists to become AI champions, they’ll need training in basic data science concepts. Your data scientists, in turn, will need coaching and collaborative structures to enable them to partner effectively with business staff.

5. Trust: Make AI responsible in all its dimensions

As AI enters deeper into our daily lives, concerns have grown over how AI and data algorithms could harm society and our own interests, such as  privacy, employment and equality. In business, clients may question the reliability of programs too complex for a human to understand. Boards may ask about risk management and the reliability of their brand when machines are behind business-critical decisions.

Everyone is asking the same question: can we trust AI?

Again, though, it appears that few Irish business leaders consider the trustworthiness of AI as a challenge. Only 4% said that trust in AI was one of their biggest challenges compared to 15% of their US counterparts. While a third of US respondents feel mistrust in AI could lead to lost business, only 16% here feel that is a possibility.

The answer is responsible AI, which incorporates risk mitigation and ethical concerns into algorithms and data sets from the start.

Responsible AI means explainable AI, so that algorithms' decisions are transparent or at least auditable. It means a control framework to catch problems before they start. It means teams and processes which look for bias in data and models, and which monitor ways malicious actors could "trick" algorithms. It means considering AI's impact on employment and the environment. And it means participation in public-private partnerships and transparent self-regulation so that responsible AI becomes the standard.

The biggest benefit of responsible AI is trust: your trust in your own AI and your stakeholders' trust in you.

44% say they have no plans or are not sure how to develop and deploy trustworthy AI systems.

While half of Irish respondents have no plans to address responsible AI in the year ahead, 95% of American companies said they are taking action now. The most important action for those who are taking trust seriously is to develop transparent and explainable AI models, and to enhance the security of AI systems through validation, monitoring and verification.

To make AI more trustworthy, and more acceptable, it needs to be easy to explain and clear in its purpose. The algorithms it uses and the data it interprets need to be transparent and its output needs to be ethically sound and secure.

Some companies in the US believe that the oversight of AI can be achieved through ethics boards or chief ethics officers for technology. You may need to consider job roles that combine technical expertise with an understanding of regulatory, ethical and reputational concerns.

A growing number of enterprises will want to open up AI's black box and make AI's decisions more transparent, interpretable and provable. They also need to anticipate that algorithms may require auditing. In the future, we expect governments will make some level of transparency in AI systems a regulatory requirement. As that is the case, we would recommend developing your AI with trust and transparency in mind, and an expectation that it will be regulated in the future.

In the year ahead, what steps will your organisation take to develop and deploy AI systems that are responsible – trustworthy, fair and stable?

Graph outlining the response to the question what steps will your organisation take to develop and deploy AI systems that are responsible – trustworthy, fair and stable?

Actions

Plan and design with responsible AI in mind

Plan and design with responsible AI in mind


  • Trustworthy AI requires fairness, interpretability, robustness and security, governance and system ethics. Create roles and establish metrics so all teams are working to build responsible AI.

Control based on experience

Control based on experience


  • When building controls for AI, apply what you’ve learned from other technologies. Best practices include developing processes to bring different stakeholders together and continually testing and monitoring AI systems.

Explore how tech can build trust

Explore how tech can build trust


  • Innovations in responsible AI are advancing quickly. Machine learning algorithms, for example, are getting better at explaining their rationale, strengths, weaknesses and likely future behavior.

6. Convergence: Combine AI with analytics, the IoT and more

AI's power grows even greater when it is integrated with other technologies, such as analytics, ERP, the Internet of Things (IoT) and blockchain. Helping advanced, predictive and streaming analytics further evolve with AI is a priority. This convergence can make new data-driven business models more powerful.

The IoT can also reap big benefits when combined with AI. A large enterprise may soon have millions of IoT sensors gathering information from business equipment and consumer devices. AI and analytics will play a critical role in finding patterns in this tidal wave of data to support everything from systems maintenance to marketing insights. Embodied AI, which embeds AI chipsets directly into IoT devices to create local intelligence, will help meet this challenge.

But just 5% of Irish respondents said that managing convergence was one of their greatest challenges. 36% of US executives said integrating AI with other technologies is a top AI challenge for 2019, on a par with retraining employees and just below ensuring trust in AI.

5% said that managing convergence of AI with other technologies was one of their greatest challenges.

Successfully integrating AI with other technologies begins with data. Organisations that have invested in identifying, aggregating, standardising and labeling data—with the data infrastructure and storage to back it up—will be well-placed to combine AI with analytics, the IoT and other technologies.

However, to integrate AI with other enterprise systems, human specialists will have to converge too. Instead of data scientists completing an algorithm, then handing it off to an IT specialist to code an application programming interface (API) or sending it to someone in the business who will then apply it, these teams should work together from the start.

Another point to consider: as AI is integrated with technologies and advanced systems that work around the clock, its algorithms will need a continuous flow of new data from which to learn. Otherwise, AI models will be working with outdated data, which will degrade their performance. Models will also need regular testing, updating and replacement.

In your organisation, what are the biggest challenges you face in implementing your AI initiatives?

Graph outlining the response to the question in your organisation, what are the biggest challenges you face in implementing your AI initiatives?

Actions

Start with analytics and the IoT

Start with analytics and the IoT


  • Many technologies can benefit from AI, but advanced analytics and the IoT will bring sizeable benefits.

Bring it all together

Bring it all together


  • Combining AI with other technologies requires bringing together different teams. Emphasising continual feedback and iteration can speed processes and improve productivity.

Keep your AI systems fed with new data

Keep your AI systems fed with new data


  • Enterprise systems and IoT networks create data continuously. To prevent a decline in performance, continually train AI algorithms with the new data.

Conclusion

As our research shows, AI has the potential to fundamentally disrupt and transform the Irish economy. Every industry and sector will be touched by its influence, and every business leader needs to consider how and where to incorporate it into their operating model.

Irish businesses need to be much more proactive in introducing AI. It is positive to see that those we surveyed acknowledge that AI will transform practically every aspect of their businesses in the next five years. However, it is concerning to see how few organisations are taking control of their own destinies and making plans to put AI at the heart of their operations.

With every month that passes, local and global competitors are embracing and embedding AI in their businesses faster, putting them at a competitive advantage which will eventually become unassailable. Put simply: Do you want to be AOL or Google?

Now is the time to develop your AI strategy. Your company will need to:

  • ensure AI has its own organisational structure and workforce plans

  • generate trustworthy algorithms with the right data to train those algorithms

  • create a plan to reinvent the business to grow revenue and profits with AI

  • converge AI with other existing and emerging technologies to create the most value.

We believe the six AI priorities outlined here are essential considerations to act on if your business wants to keep pace and reap the benefits of the AI revolution. PwC experts have a proven track record of supporting businesses in implementing AI into their organisations – talk to us today. To understand how your peers are embedding AI, contact the Analytics Institute.

Survey Methodology

The Irish research is carried out jointly by PwC Ireland with the Analytics Institute of Ireland involving over 100 Irish business leaders representing key industry sectors. The results are benchmarked against similar US analysis of over 1,000 business executives.

Lorcan Malone
Chief Executive, Analytics Institute
Email

Sheelagh Carroll
Client Director, Analytics Institute
Email

Contact us

Darren O'Neill

Partner, PwC Ireland (Republic of)

Tel: +353 1 792 7521

Martin Duffy

Director, PwC Ireland (Republic of)

Tel: +353 1 792 6552

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