For all the hype and talk of AI over the past year or so, actual AI adoption worldwide has been slow. According to the 2018 Gartner CIO survey, only four per cent of companies have invested in AI, with a mere 20 per cent actively experimenting with AI solutions. As only 46 per cent of per cent of companies have developed plans to implement AI solutions, it doesn’t look like widespread uptake is on the way any time soon. And the state of play in the UK is even worse – research conducted by the Royal Society for the encouragement of Arts, Manufactures and Commerce (RSA) in 2017 shows we’re lagging behind, pointing to an AI adoption gap between the UK and other developed economies.
Of course, AI, when implemented well, can bring untold benefits to any organisation. Automation means the machines can take care of simple everyday tasks that don’t necessarily need a human brain. And when humans are freed up to focus on more strategic tasks, the business becomes infinitely more competitive, with customers and prospects seeing the rewards from working with a more productive, innovative supplier.
So why are businesses – especially those in the UK – holding back on Enterprise-Scale AI adoption and how should this issue be addressed? Well, the problem is threefold.
First, there’s a skills-shortage issue, particularly in relation to finding data scientists. Why undergo a complex AI project now, when you don’t have the right staff to successfully bring it to fruition? Thankfully, with the UK government pledging to unlock the power of data as part of its Digital Strategy, the data science skills shortage should be a relatively short-term issue.
The second issue relates to the accessibility and quality of the data. Yes, a lot of organisations hold a lot of data, but they still aren’t fully realising its potential, keeping it siloed between different business units, rather than fully integrated and centralised, so it can be analysed to give the insights necessary to empower tangible, hype-free AI.
The third issue, the most significant, actually comes down to planning and implementation. Many AI projects fail because organisations are still in pilot or proof of concept mode and aren’t considering how they’ll scale their trials across the enterprise. To be successful, an AI implementation needs to be considered as a business change program underpinned by technology. Unfortunately, in many organisations, it’s still viewed as an IT initiative or a tactical, department-level experiment without full consideration to scalability across the Enterprise.
With all this in mind, I wanted to share four tips for making sure your AI implementation will make your organisation smarter and savvier.
- Consider project scalability from the outset
If you’re looking to implement an AI solution, it is imperative you look ahead – consider how, beyond beta, it will be rolled out to benefit your organisation as a whole.
We’d recommend taking a sprint-based approach to ensure the business and IT teams work in tandem around any implementation. That way, leaders can see the impact of the initiative in weeks versus months and years. Avanade helped a leading European insurer automate over 30 processes using Agile Delivery, but it was only a success because we had everyone on board from the outset.
- Understand and implement AI solutions that will work in the short term
For many businesses, Robotic Process Automation (RPA) is an ideal starting point for AI adoption. Through RPA, machines are taught to process repetitive, high-volume, manual tasks that use structured data. AI projects based on RPA should significantly reduce time spent on repetitive tasks and free up employees for more complex and rewarding work. They can also deliver rapid ROI.
- Know what you want to achieve
Establish parameters for what the implementation will and won’t achieve. A good way to approach AI project management is to start with a basic prototype – with limited functionality – and then scale it up as support and capability are built within the organisation. This approach allows the project to show results fast and build from each iteration.
To keep the project on track, put measurable KPI in place. Ideally, businesses should appoint an ‘AI evangelist’ to drive the project.
- Bringing employees with you on the AI journey
Our global research shows 79% of business leaders believe internal resistance to change is limiting the implementation of AI technologies in the workplace.
So you need to get employees on board as part of the AI planning and implementation process! We recommend providing information and insights into how roles may change and evolve as AI comes into force, explaining to employees that they’re being freed from respective tasks, to focus on higher-level, more rewarding work. This engagement should also include a roadmap for employee ‘upskilling’, where possible.
Overall, AI adoption will not only mean making sure everyone knows how to make a success of the project, but also knowing what’s expected of them to bring it to fruition. It will take time to get everyone on board, but the benefits of allowing automation and AI to flow through the company will help staff to focus on more strategic tasks. And that’s something all organisations will reap the benefits from.
As President, Europe, Darren Hardman is responsible for directing strategy and operations across Avanade Europe’s 13 geographies. Darren’s previous roles include General Manager of Avanade UK & Ireland and Chief Operating Officer of Avanade’s North America Area, responsible for developing and executing the North America business strategy, including sales, organisational strategy and core business functions. Earlier in his Avanade career, Darren was Global Senior Vice President, Sales, introducing a global customer segmentation taxonomy, a uniform planning process, a single global sales cycle process and structured account management. Prior to joining Avanade, Darren spent more than 15 years at Accenture, most recently as a senior executive within the Technology Growth Platform.
Darren holds a degree in Econometrics (Economics & Mathematics) from the University of Nottingham, U.K., and an MBA with Distinction from Imperial College London, U.K.