It is quite obvious that success is determined by human aspects rather than technological factors. We have identified four key organisational actions that enable successful AI implementation at scale (Figure 1).
#1 Establish a Data Culture
The traditional focus for companies has been on ensuring access to good, clean data sets and the proper use of that data. Ecosystm research shows that only 28% of organisations focused on customer service, also focus on creating a data-driven organisational culture. But our experience has shown that culture is more critical than having the data. Does the organisation have a culture of using data to drive decisions? Does every level of the organisation understand and use data insights to do their day-to-day jobs? Is decision-making data-driven and decentralised, needing to be escalated only when there is ambiguity or need for strategic clarity? Do business teams push for new data sources when they are not able to get the insights they need?
Without this kind of culture, it may be possible to implement individual pieces of automation in a specific area or process, applying brute force to see it through. In order to transform the business and truly extract the power of AI, we advise organisations to build a culture of data-driven decision-making first. That organisational mindset, will make you capable implementing AI at scale. Focusing on changing the organisational culture will deliver greater returns than trying to implement piecemeal AI projects – even in the short to mid-term.
#2 Ingrain a Digital-First Mindset
Assuming a firm has passed the data culture hurdle, it needs to consider whether it has adopted a digital-first mindset. AI is one of many technologies that impact businesses, along with AR/VR, IoT, 5G, cloud and Blockchain to name a few. Today’s environment requires firms to be capable of utilising a variety of these technologies – often together – and possessing a workforce capable of using these digital tools.
A workforce with the digital-first mindset looks for a digital solution to problems wherever appropriate. They have a good understanding of digital technologies relevant to their space and understand key digital methodologies – such as Customer 360 to deliver a truly superior customer experience or Agile methodologies to successfully manage AI at scale.
AI needs business managers at the operational levels to work with IT or AI tech teams to pinpoint processes that are right for AI. They need to make an estimation based on historical data of what specific problems require an AI solution. This is enabled by the digital-first mindset.
#3 Demystify AI
The next step is to get business leaders, functional leaders, and business operational teams – not just those who work with AI – to acquire a basic understanding of AI.
They do not need to learn the intricacies of programming or how to create neural networks or anything nearly as technical in nature. However, all levels from the leadership down should have a solid understanding of what AI can do, the basics of how it works, how the process of training data results in improved outcomes and so on. They need to understand the continuous learning nature of AI solutions, getting better over time. While AI tools may recommend an answer, human insight is often needed to make a correct decision off this recommendation.
#4 Drive Implementation Bottom-Up
AI projects need alignment, objectives, strategy – and leadership and executive buy-in. But a very important aspect of an AI-driven organisation that is able to build scalable AI, is letting projects run bottom up.
As an example, a reputed Life Sciences company embarked on a multi-year AI project to improve productivity. They wanted to use NLP, Discovery, Cognitive Assist and ML to augment clinical proficiency of doctors and expected significant benefits in drug discovery and clinical trials by leveraging the immense dataset that was built over the last 20 years.
The company ran this like any other transformation project, with a central program management team taking the lead with the help of an AI Centre of Competency. These two teams developed a compelling business case, and identified initial pilots aligned with the long-term objectives of the program. However, after 18 months, they had very few tangible outcomes. Everyone including doctors, research scientists, technicians, and administrators, who participated in the program had their own interpretation of what AI was not able to do.
Discussion revealed that the doctors and researchers felt that they were training AI to replace themselves. Seeing a tool trying to mimic the same access and understanding of numerous documents baffled them at best. They were not ready to work with AI programs step-by-step to help AI tools learn and discover new insights.
At this point, we suggested approaching the project bottom-up – wherein the participating teams would decide specific projects to take up. This developed a culture where teams collaborated as well as competed with each other, to find new ways to use AI. Employees were shown a roadmap of how their jobs would be enhanced by offloading routine decisions to AI. They were shown that AI tools augment the employees’ cognitive capabilities and made them more effective.
The team working on critical trials found these tools extremely useful and were able to collaborate with other organisations specialising in similar trials. They created the metadata and used ML algorithms to discover new insights. Working bottom-up led to a very successful AI deployment.
We have seen time and again that while leadership may set the strategy and objectives, it is best to let the teams work bottom-up to come up with the projects to implement.
#5 Invest in Upskilling
The four “keys” are important to build an AI-powered, future-proof enterprise. They are all human related – and when they come together to work as a winning formula is when organisations invest in upskilling. Upskilling is the common glue and each factor requires specific kinds of upskilling (Figure 2).
Upskilling needs vary by organisational level and the key being addressed. The bottom line is that upskilling is a universal requirement for driving AI at scale, successfully. And many organisations are realising it fast – Bosch and DBS Bank are some of the notable examples.
How much is your organisation invested in upskilling for AI implementation at scale? Share your stories in the comment box below.
Woolworths have announced the adoption of a new Software-as-a-Service capability from One Door to support the quality and compliance of their in-store merchandising. There are some valuable lessons from this announcement for other retailers.
The power of data, particularly as the capability of specialist AI tools improves, continues to help retailers improve their offering to customers.
SaaS Capabilities Offer Performance Improvements
Woolworths are working on improving the compliance of product merchandising in-store using One Door Visual Merchandising solution.
One Door will improve the accuracy of data available to both the in-store teams and for the central supermarket merchandising team. The supply chain in Woolworths is already highly automated but getting the shelf presence right is dependent on the quality of data being captured. While store teams already use a range of electronic tools to capture this information, the compliance with store planograms and visual merchandising standards has been difficult to automate.
One Door’s solution provides a single source of this information in an easy to use digital format. The AI tools that One Door have developed appear to be able to show the degree of compliance of the actual shelf layout and stock position.
For store teams, One Door will simplify tracking layout changes by highlighting them and making the data available on the shop floor. This should deliver productivity benefits to the store – benefits that can be reinvested in new activities or on better customer service.
Store teams will be able to verify that third party merchandisers are compliant. Major product manufacturers often use their own merchandising teams in supermarkets and One Door will provide a simple mechanism to verify they have done their jobs properly.
The central merchandise teams will be able to quickly get data-driven feedback on how the stores are making planned changes, as well as verifying the quality of compliance with their store layouts.
All of these factors should mean that the product that is available in-store is presented in the manner that the merchandising teams have defined, and the customers will see a more consistent presentation of products.
Integration is Critical for Rapid Deployment
Effective integration with existing systems and new cloud capabilities is critical to support the real-time operation in Retail.
The ability to introduce and scale up new capabilities that can be delivered by cloud services such as One Door will only be effective if integration is simple and quick. This requires compatibility at a number of levels including data semantics and the ability to exchange data effectively. Woolworths have been growing their capability for managing and supporting APIs that will make this integration smoother.
In addition, the cloud service providers have made the development of integration capabilities an investment priority.
The introduction of One Door is showing how the company can integrate new capability and introduce it to almost 10% of their stores as a pilot capability, with the full deployment to be completed across their chain during 2022.
Other retailers who don’t have this capability to integrate cloud services quickly, reliably and cost-effectively are going to lag companies that have invested to achieve this capability.
CIOs and CDOs should be leading their organisations in the development of a rich and scalable set of APIs to enable the integration of this type of high-value specialised solution.
Deployment without Consistent Architectures will be Complex
Rapid deployment of new capabilities requires a well-architected cloud, network, and edge infrastructure – and a well-trained team.
It is highly likely that the deployment of the One Door solution will be delivered over the existing Woolworths infrastructure. The capability is delivered from the cloud, with little or no deployment costs or time required. With the existing network and hybrid cloud capabilities that Woolworths have developed this type of rollout will be a relatively simple technical activity.
The integration of the service into the Woolworths environment is likely to be the most complex activity to make sure accurate data is exchanged.
It doesn’t take long to identify a wide range of different digital initiatives that Woolworths are pursuing. With the platform that they have established, they are well-positioned to take advantage of new capabilities as start-ups and existing suppliers develop them.
Every retailer needs to maintain their focus on their digital capabilities. As companies such as One Door develop AI-based enhancements, CIOs and their teams need to be ready to integrate these capabilities quickly.
Strong architectures for both infrastructure and digital services are needed to achieve these outcomes.
Recommendations for Retailers
Retail organisations continue to find new ways to leverage the power of the data that they are able to collect. The flexibility that SaaS developments deliver will be essential to maintaining an organisation’s competitive positioning.
CIOs and their teams need to lead their organisations and ecosystems by:
Identifying new SaaS capabilities that support the strategic positioning of their companies
Preparing their environments by supporting a rich set of APIs to support the rapid integration of these new capabilities
Developing and maintaining strong architectures that provide organisations a solid framework to develop within
Checkout Alan’s previous insight on Woolworths micro automation technology adopted to speed up the fulfilment of online grocery orders