4 core AI principles that fuel transformation success

New projects can elicit a sense of trepidation from employees, and the overall culture into which change is introduced will reflect how that wariness is expressed and handled. But some common characteristics are central to AI transformation success. Here, in an extract from his book, AI for Business: A practical guide for business leaders to extract value from Artificial Intelligence, Peter Verster, founder of Northell Partners, a UK data and AI solutions consultancy, explains four of them.

Agility

Around 86% of software development companies are agile, and with good reason. Adopting an agile mindset and methodologies could give you an edge on your competitors, with companies that do seeing an average 60% growth in revenue and profit as a result. Our research has shown that agile companies are 43% more likely to succeed in their digital projects.

One reason implementing agile makes such a difference is the ability to fail fast. The agile mindset allows teams to push through setbacks and see failures as opportunities to learn, rather than reasons to stop. Agile teams have a resilience that’s critical to success when trying to build and implement AI solutions to problems.

Leaders who display this kind of perseverance are four times more likely to deliver their intended outcomes. Developing the determination to regroup and push ahead within leadership teams is considerably easier if they’re perceived as authentic in their commitment to embed AI into the company. Leaders can begin to eliminate roadblocks by listening to their teams and supporting them when issues or fears arise. That means proactively adapting when changes occur, whether this involves more delegation, bringing in external support, or reprioritizing resources.

This should start with commitment from the top to new ways of working, and an investment in skills, processes, and dedicated positions to scale agile behaviors. Using this approach should lead to change across the organization, with agile principles embedded into teams that then need to become used to working cross-functionally through sprints, rapid escalation, and a fail-fast-and-learn approach.

Trust

One thing we’ve discovered to be almost universally true is that AI transformation comes with a considerable amount of fear from the greater workforce, which can act as a barrier to wider adoption of AI technology. So it’s important to address colleagues’ concerns early in the process.

To help people adjust to the potential shift, I suggest the following:

1. Maintain integrity: There are several ways you can help ease worries across your organization, but first it’s crucial to be honest. If AI will lead to job losses and redeployments, be upfront about it. Building trust begins with honesty and integrity. Giving people a sense of certainty as early as possible, whether that’s reassuring they’ll be retained or putting in place support for redeployment, will help reduce AI anxiety.

2. Support people creatively: Look at the skills you require across your business and create an environment of continuous learning, focusing on adapting skills and roles to the future shape of the business. Empower employees to gain skills in data science, data analytics, ML, and project management. Also consider job shares, part-time hours, or flexible contracts where redeployment isn’t appropriate.

3. Explore applications: Encourage team members to find ways AI will support them to be more efficient and increase their value. Help them visualize AI as another tool they can work with, rather than as a replacement for their capabilities, and enable them to gain the knowledge, skills, and experience to stay current and thrive in the workplace of the future.

Customer orientation

Organizations that center their AI projects around customer ease and experience tend to see more successful outcomes. Ask yourself these three questions and try some of the things suggested before starting:

1. How can I improve the customer journey?

Consider doing a ‘mystery shop’ of your own process to understand where friction occurs. When you have an idea of areas that could be improved, look at the ways AI could be applied to remove barriers or inconveniences. Zara’s trial of self-checkouts in their stores, for instance, was originally met with resistance, but when customers began to benefit from shorter waiting times, they soon accepted the change as a success.

2. How can I save my customers time and money?

Electrical giant Phillips cleverly responded to customer concerns about energy costs, despite not being an energy company. By pivoting toward smart home technology and energy-efficient solutions for their customers, they found a way to use AI to help customers save time and money (remotely starting the washing machine, smart thermostats, occupancy sensors), while encouraging them to buy Phillips products.

3. How can I better align my products and services to customer needs?

Try surveying your customers to find out what they look for from your product or service. It’s easy to assume you know exactly what their pain points are, but as customer expectations rapidly shift in today’s digital world, so too do customer needs. British online bank Monzo asked their customers what support they’d like, and the overwhelming response was help with increasing savings. In response, Monzo implemented AI to analyze customer transaction data and automatically categorize expenses, offering spending summaries, savings targets, and real-time notifications to help customers track their expenses, identify areas of overspending, and encourage better saving habits.

Innovation

It’s companies that encourage and reward innovative thinking at every level that see the most success with their AI and digital projects. Embedding innovation into an organization often requires a change in mindset — one where experimentation is rewarded, and failed projects are seen as an important part of the learning process. To create an environment that fosters innovation, it’s important people are permitted to fail and empowered to take calculated risks.

So consider the following aspects of your business to understand if it’s a place where innovation can thrive:

• Are there any incentives in place to encourage innovative thinking and problem solving?
• Do employees feel empowered to make decisions or feed back ideas to their managers?
• Are team members disincentivised from taking risks due to fear of repercussions or attitudes?
• Is there a team or forum in place to research and support innovation across the business?
• Are leaders open to hearing ideas and feedback from their team members?
• Do leaders introduce new ideas and/or support ideas when they’re presented, and are they themselves open to change?

Setting the groundwork for innovation through these four core principles is what will create a stable foundation for AI implementation in a way that’s helpful, strategic, and lasting. This is what characterizes successful projects, and where organizations properly assess their readiness for AI.