Organizations across sectors are embracing advanced analytics and artificial intelligence in response to the COVID-19 pandemic, with a focus on building greater resilience, adaptability and efficiency in their supply chains. For many this involves building new A.I.-powered capabilities in their business, for example risk-based scenario planning, real-time inventory optimization, and predictive analytics that allow procurement and supply chain to collaborate effectively. 

Along the way have come cultural and organizational hurdles — and five key pitfalls to implementing A.I. have emerged.

1. Failure to plan your A.I. use cases. A.I. shouldn’t be a solution looking for a problem. Executives need to have a crystal-clear idea of why and where they want to apply A.I. The first step is to identify a problem that they think the technology can solve (like, for example, examining how to position your inventory to best fulfill demand).

One benefit of being so laser-focused on specific areas is that results can come fast. In fact, the right A.I. can show where return on investment will happen quickly. Bite off a piece where you know there’s value, and go from there.

2. No C-suite sponsor. It’s not enough to say, “I’ve heard about this, let’s implement it!” Bringing A.I. to any capability or function in a business will mean organizational change. The application of A.I. must be coherent with an organization’s strategy, and must have meaningful business-driven objectives and KPIs. I.T. and logistics teams will need the support of other departments and an executive sponsor to successfully launch and deliver on projects that aim to bring A.I. into their supply chains. 

A.I.-powered tools create new ways of doing business; it’s about building or transforming business capabilities rather than throwing a new technology at a problem and hoping for the best.

3. Building it in-house, or using a consulting firm. A.I. solutions are more challenging than other I.T. systems to build from scratch, and you could find your approach won’t work at all. That concern tends to drive businesses into the arms of a consulting firm, which can also be risky (and extremely expensive).

To make an A.I. system work, organizations need access to good data sets — which neither they nor consulting firms have. And, unless you’re willing to pay exorbitant fees for consultants for as long as your technology is in use, there won’t be anyone around to handle critical maintenance, changes in the real world, complex bugs and necessary improvements. The solution is to use a specialist tool (that comes at a fraction of the cost of building and maintaining your own).

4. No access to quality data. The nature of A.I. makes data quality critical because information is constantly being evaluated, assessed and utilized for actions and suggestions. If you don’t give A.I. good data, or if the quality of the data changes without you knowing, it can make wild predictions and decisions based on that information. 

In order for your business to benefit from A.I. in your supply chain, it’s important to gather good data from as many relevant activities as possible — including manufacturing sites, warehouses, freight tracking apps and order management systems. If that sounds challenging, don’t worry — you don’t have to acquire all the information yourself. Rather than building your own integrations, the right tools will let you simply “plug in” and benefit from partner data (so you’re not just reliant on your own limited dataset). 

5. Not pairing A.I. with change management. Some employees may be intimidated by the introduction of A.I. This fear can result in a failure to fully adopt the technology, which in turn will harm the success of the business.

One way to sell new and potentially scary changes to employees is to emphasize how the technology makes mundane and painful logistics or supply-chain management tasks easier. 
The A.I. can enable teams to do things that would normally require many small calculations and a lot of time — as a result, they can achieve a lot more.

Pointing out these positive human outcomes, and the ways A.I. can improve individual and departmental performance, goes a long way to facilitating the change management process. Employees may worry about their job security, but in reality many use cases for A.I. supplement human experience and expertise.

How 7bridges Can Help 

7bridges is an A.I-powered logistics platform that unifies the global data, operations and supplier ecosystem needed to bring world-class supply-chain capabilities to any company.

The technology enables businesses to deftly react to changing demand patterns, redesign supply chains, and build scenario plans around resource needs. Its capabilities to create a digital twin also allow users to model and parse out any number of “what-if” supply-chain scenarios. 

Most companies have decentralized supply chains that involve multiple stakeholders in different time zones, creating silos where operational decisions are made without incorporating insights and objectives from the entire network. 7bridges’ A.I. is designed to break down those silos and generate smart, split-second decisions. 

7bridges offers multiple ways to get started across numerous A.I. use cases in supply chains, and makes it easy to upgrade from the industry status quo and capture value in weeks. The platform is easy to use for staff across networks, and offers total visibility and control over logistics costs, operations and performance. 

“When I worked in the industry, I realized that the data, computational power, technical talent, and integrations required to effectively build and deploy A.I. in the supply chain was vast  — and  totally unrealistic for nearly every business. We built the 7bridges platform to make this easy for any organization, so our clients can access huge global datasets, an extensive supplier network, and real-time insights and recommendations from a constantly improving A.I.,” says CEO Philip Ashton.

“Don’t be intimidated,” says Sam Herbert, Chief Operating Officer of 7bridges’ customer Clinigen CTS Inc. “Start small and specific, and reap the benefits quickly.”