6 steps to sense-check your AI capability

Artificial intelligence (AI) is seen as one of the most powerful emerging technologies that’ll impact business over the next few years. Business leaders see its potential; in our 22nd CEO Survey, 72% of UK CEOs said AI will significantly change the way they do business in the next five years. Estimations are that AI could contribute up to $15.7trillion to the global economy by 2030.

You’d think then that business leaders would be keen to start getting a share of this for their business, especially if it gives them an edge over competitors. But it seems despite recognising its potential, they’re slightly more hesitant about actual implementation. 35.5% of UK CEOs said their organisations had no plans to pursue AI initiatives over the next few years, and just 2% have wide-scale AI initiatives or are using it as a fundamental part of their operations.

We believe companies need to be doing more. This is mainly aimed at those who must seize the initiative and look at how they can quickly enhance their capabilities. But for those we’ve talked to further along the journey, we’re also seeing areas where improvement is needed. AI is a process of evolution, not revolution - but those that start early will have an advantage over their slower competitors.

With this in mind, we’ve laid out six areas to think about to sense-check your own capability, and identify areas you still need to work on to get the most from this technological opportunity. Those companies focusing their efforts in the six key areas below will be leading the pack this time next year - if they can navigate an unfamiliar technology, untangle its potential applications and realise the potential.

A large share of executives globally - 62% - believe AI will have a larger impact on the world than the internet revolution.

22nd CEO Survey

Questions for businesses:

  • What skills or resource will help you firm up your AI strategy and implementation?
  • Where could you be more ambitious with your use of AI to unlock greater benefits?

1. Taking a holistic view

If you’re serious about AI, you can’t go in thinking it’ll only affect certain areas - you need to consider how it’ll affect your entire organisation. Establish a team with relevant data, analytics, automation and AI skills to look at how to implement AI in your business, who are responsible for oversight, data management and ensuring there’s clear communication with all business areas. That way you can avoid siloed thinking and issues identified can be learned from across the company, and successes can be replicated and implemented on a wider scale.

2. Addressing varying levels of familiarity with AI

Business leaders continue to wonder just what the impact of AI will be on jobs over the longer term. UK CEOs are split here: 45% believe AI will displace more jobs than it creates in the long run, while 37% disagree.  

One thing’s for sure though - leaders must address the approaching skills gap, and appreciate there will be different levels of experience across your organisation. Hiring in technical specialists will help on the technology side, but won’t help address people challenges and the overall change in mindset that’ll be required. We think there are three main groups of employees to bear in mind when looking at your AI workforce strategy:

  1. Educate users: On a day-to-day level all employees need to be educated on how to use new AI-enhanced applications, support data governance, and know who to contact for expert help when needed.

  2. Train ‘super users’: A more specialised group, perhaps 5 - 10% of your workforce should receive further training to become developers or ‘super users’. These individuals can be advocates for the technology and identify use cases and data sets, and liaise with the more technically-enabled specialists, acting as a point of contact with everyday staff.

  3. Cultivate specialists: A small but crucial group of data engineers and data scientists will do the heavy lifting to create, deploy, and manage AI applications.

Companies we’ve spoken to often have a strategy in place for the first and third groups, but miss the second. These super users can be very useful to ensure the technical specialists aren’t having to divert time to non-essential tasks, and convince those who might be wary of new technology of the benefits. If you’re starting your AI journey, bear them in mind; if you’ve already started but are struggling to convince your employees of the benefits, it may be worth thinking about creating this group.

3. Trust: Make AI responsible in all its dimensions

With AI expected to have such a large impact on the way businesses currently operate, in addition to the opportunities, there are also clear concerns. Regulatory issues (discussed in more detail here) and how far users are allowed to go in terms of experimentation and implementation may be a reason so many CEOs are taking a ‘wait and see’ approach currently.

We believe the way to combat this is (from the very beginning) to ensure that AI systems are trustworthy by making any use of AI responsible. Increased transparency will offer reassurance to both internal and external stakeholders about the risks involved and should ease minds about adoption. Companies can ensure they’re doing so by:

  1. Ensuring fairness: Are you minimising bias in your data and AI models? Are you addressing bias when planning for AI?
  2. Being transparent about interpretation: Can you explain how your AI model has made decisions? Can you then explain how you’re ensuring those decisions are accurate?
  3. Guaranteeing security and robustness: Can you rely on your AI system’s performance, and guarantee they’re not vulnerable to attack?
  4. Keeping good governance: Who in your organisation is accountable for your AI systems? Do you have the proper controls in place?
  5. Keeping ethical considerations front of mind: As mentioned above, is your AI usage complying with regulation? How will this impact your employees and customers?

4. Successful AI is built on good data

AI answers the big question about data: how to turn it into value. But in order to capitalise on this, there’s a big problem that must be tackled first: Too few companies have adequate data to build their AI capabilities on. In order to successfully adopt AI, companies must ensure they have usable, high-quality data as the foundation they can build on.

The other main issue around data? Emerging regulations around data privacy. These will also impact AI and may limit its growth because countries are operating under different regulatory regimes/regulation, affecting how companies operating globally can use data generated across territories. 34% of UK CEOs believe governments should limit regulations around data collection to facilitate the development of AI.

Europe’s General Data Protection Regulation went live in May 2018, and 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 cyber security breaches. AI’s use of data is broad, so companies must adopt a global mindset when dealing with regulatory issues. If you’re operating globally, make sure your teams that are helping shape policies in different jurisdictions are aligned and communicating and address compliance by applying best practices globally.

5. Beyond productivity, explore monetisation opportunities

Right now, the greatest gains from AI are coming from productivity enhancements, such as automating processes and enabling better analysis for decision making. But as our Global AI Study found, a substantial portion of AI’s economic impact will come from the consumption side, through higher quality, more personalised, and more data-driven products and services. Companies should identify monetisation opportunities, and how it can be used to deliver a better level or service and personalisation for customers - both internally and externally.

6. The power of emerging technologies is bringing them together

AI’s power grows even greater when it is integrated with other technologies, such as analytics, ERP, blockchain and the Internet of Things (IoT).

As better analysis capabilities give strategic advantages, and automation frees up time from manual tasks, we see company strategy moving from how to simply get things done to using this freed up time to look at how to do things better, with more in-depth analysis of how the business model and strategy needs to change. With more data around than ever before, AI and analytics, working with other emerging technologies, will play a crucial role in finding patterns in a sea of data to support everything from systems maintenance to marketing insights.

For example, a leading auto manufacturer has been using AI to test more than 200,000 go-to-market scenarios for autonomous ridesharing fleets. The model has helped identify key economic drivers and optimal levels for infrastructure and vehicles.

Preparing for what’s next

In 2019, to ensure smooth future progress it’s time to solidify your AI strategy. The six key areas above are only the beginning - but regardless of your organisation’s level of maturity they must be addressed in order to provide a solid foundation for future opportunities. For a more detailed discussion on AI, see our 2019 predictions report here. And be sure to check out our other Connected Intelligence AI assets here.

Maria Axente Maria Axente

Maria Axente

AI Programme Driver


Anand Rao Anand Rao

Anand Rao

Global & US Artificial Intelligence and US Data & Analytics Leader


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