How to push past the ‘hAIpe’ and get going with AI

Tuomas Syrjänen
- Technology - Mar 22, 2019

When it comes to the gap between hype and reality, the business community’s approach to AI is hard to beat. A recent report by Microsoft found more than half of UK companies have no AI strategy in place. This situation seems to be mirrored across Europe, with Information Age recently suggesting that the continent is at risk of becoming AI ‘has-beens’.

Part of the problem is that the hype about what AI is and what it can do can make it hard for businesses to know how to apply this transformational technology to their own organisations. Based on our own experience of embracing AI, here are five tips to help businesses cut through the hype and discover AI’s benefits at first hand.

1. Understand AI and how it fits with business strategy

Currently AI experiments tend to be random efforts to understand how the technology works with very little attempt to link them to an organisation’s broader strategic context. Companies considering adopting AI should start by identifying their value levers by asking what business are we in and what makes us successful? Is it R&D? Marketing? Customer service? Cost efficiency? Once these levers are clear, AI can be applied to enhance these critical dimensions of the business. This strategic focus is crucial due to the management focus, data infrastructure and cognitive investment that implementing AI requires.

With the value levers identified and matched to AI’s capabilities, companies are better placed to identify problems to solve that align with their strategic priorities. For example AI can help with forecasting (such as predicting budget overrun or periods when staff will be sick.) In sectors such as retail, project management or healthcare, where the value levers feature a strong customer focus, AI can help to visualise and improve how businesses plan and optimise resources across different scenarios.

AI is also great at knowledge management: Where value levers are based around innovation and R&D, AI-enabled search engines can make the competencies, knowledge base and ideas of a company’s entire workforce visible quickly and then harness these to the data available within the company. This helps to build teams with augmented know-how who are equipped with the depth of knowledge and data to generate new insights and achieve a much greater impact in a much shorter period of time.

2. Assess how AI-ready your business is

What is your company’s AI maturity level? We estimate that 80% of time spent on AI involves working with data: getting access, cleaning, preparing, pre-processing, normalising: the grind that precedes the more ‘fun’ stuff - designing interesting AI applications.

Businesses need to discover what data they have, including data from unusual or unexpected sources such as staff surveys or financial results. Where is the data located? Is it cleansed, correlated, linked and machine-readable? If not, are there guidelines and processes for doing so? Do existing systems and workflows need to be redesigned to make them easier to use and integrate with AI?

Companies that have assiduously collected customer data are ready to consider how AI could deliver monetisable insights. This includes using AI to extract and combine data from unusual sources to reveal unexpected insights which could form the starting point for new products/services or ways of working.

Putting the right data infrastructure in place for all this will take significant investment for a company - simply relying on a “data lake” system won’t be enough. This is another reason why AI needs to be applied in a strategic context, to ensure the infrastructure will eventually deliver a return on investment for the business.

3. Champion the techies!

Benefiting from AI will be tough if data scientists and technologists aren’t integrated into the heart of your business. Tech specialists need to understand and buy in to the commercial importance of their work while senior leaders need to have a meaningful understanding of what tech teams actually do. This doesn’t mean learning to write Python code, however AI implementation does require understanding that there are different  types of algorithms for different tasks and the pitfalls and the trade-offs of each.

Bringing data scientists and technologists together with sales/business development teams and creative teams, while ensuring the C-suite understands the fundamentals of AI, will mean AI projects have a much greater chance of success as they will be rooted in what is technologically feasible and commercially viable.

4. Start small, but scale quickly

Having agreed how AI fits into the overall business strategy, it’s time to start with small experiments that can deliver results quickly. Senior leaders should work with data scientists to identify small problems which AI could help solve. This could include using AI’s automation capabilities to take over logging of invoices or using its pattern recognising functions to help identify why certain teams may be underperforming. They should then take what they learn and iterate their approach to maximise its value. This approach not only minimises risk, it also provides insights into the challenges of integrating AI into a business. At the same time as experimenting with AI in small ways, business leaders should deploy horizon thinking to imagine how it can be used to identify new revenue sources, expand into new sectors and even redefine what business the company is in.

5. Boost your team’s adaptability quotient 

AI is ushering in an era of human-machine collaboration requiring a radical rethink of traditional operating models, role definitions, individual success measures and career progress. For some businesses and their people, this translates reductively into fears about job cuts. In practice, we see an AI-enabled workforce as one where people are supported to continuously learn about new technologies and about societal and behavioural changes and where autonomous teams will be empowered to adapt quickly to market or environmental changes.

To transition to this vision, leaders need to assess how teams can be encouraged to buy into an AI-enabled future. Unless companies understand and adapt to AI’s cultural impact, they won’t be able to change gears operationally. And eventually that systematic failing will translate into an inability to identify and solve problems for customers.

With AI considered by many to represent a fourth industrial revolution, it inevitably attracts a degree of myth and hype. If businesses can look beyond this and take these first steps to integrate the technology into their organisations, the true transformative potential of AI will emerge.

Tuomas Syrjänen is the co-founder AI Renewal at digital engineering and innovation consultancy Futurice

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