1. Aligning with your strategic goals
It’s vital to align AI innovation with core strategic objectives and performance indicators, rather than allowing a scattered series of initiatives to operate in isolation. In our experience, a lot of organisations have set various pilots in train. What most aren’t doing is taking a fundamental look at how AI could disrupt their particular business and then determining the threats and opportunities this presents.
2. Don’t expect magic
AI may be intelligent, but it’s still a machine. A common problem is believing the AI will magically learn without human intervention. In reality, you have to put a lot of effort into acquiring and cleansing data, labelling and training both machines and employees.
3. Clear about your partners
Everywhere you look, there are start-ups offering solutions to this and opportunities for that. Partnership with these vendors accelerates innovation, agility and speed to market. But it’s clearly important to pick your spot. This includes being clear about the strategic and operational priorities you’re looking to address through the choice of partner. It’s also important to bear in mind that while vendors may be good at selling the possibilities, they’re not always as clear about how to deliver them – the way they look at development risks is certainly very different from what you’re used to.
In a high risk and fast-moving vendor landscape, the first consideration is the financial viability of the potential partners – will they still be there when you need them? It’s also important to determine how to acquire the necessary data, develop the knowledge needed to deploy your new capabilities and how to integrate new platforms into existing infrastructure. When buying commercially available off-the-shelf software, a proof of concept development phase is often necessary.
4. Opening up to scrutiny
Before you adopt AI, you clearly need to know what it’s doing and how. This includes ensuring the software can communicate its decision making process in a way that can be understood and scrutinised by business teams. In particular relation to machine learning, it’s important to think about how to ensure the software will deliver the anticipated results. Boards want this assurance before they proceed. Regulators are also likely to expect it. Algorithmic transparency is part of the solution, though this may require a trade-off between decision making transparency, system performance and functional capabilities.
5. Demonstrating regulatory compliance
Regulators need to move quickly to keep pace with emerging technologies. We may see regulatory constraints that prevent adoption in key regulated industries such as health and financial services. Developments such as the EU’s General Data Protection Regulation (GDPR) are heightening the challenges. Staying compliant with relevant regulatory requirements is essential to build trust in your AI platform.
6. Organisational structure
The changes in your business models as part of your overall AI strategy will also need to be reflected in your organisational structure. Your organisation needs a dedicated AI governance structure, this could include a nominated member of the C-suite and a central hub of technical expertise. Embedding data scientists throughout your business either through training or hiring is essential to achieve AI organisational maturity.