The Manufacturing industry is at crossroads today. It faces challenges such as geopolitical risks, supply chain disruptions, changing regulatory environments, workforce shortages, and changing consumer demands. Overcoming these requires innovation, collaboration, and proactive adaptation.
Fortunately, many of these challenges can be mitigated by technology. The future of Manufacturing will be shaped by advanced technology, automation, and AI. We are seeing early evidence of how smart factories, robotics, and 3D printing are transforming production processes for increased efficiency and customisation.
Manufacturing is all set to become more agile, efficient, and sustainable.
Read on to find out the changing priorities and key trends in Manufacturing; about the World Economic Forum’s Global Lighthouse Network initiative; and where Ecosystm advisor Kaushik Ghatak sees as the Future of Manufacturing.
Click here to download ‘The Future of Manufacturing’ as a PDF
Organisations are uncertain about how 2023 will shape up for them, amidst concerns about recessions, supply chain uncertainties, continued geopolitical volatility, energy crisis, and labour disruptions. At the same time, they have to continue to evolve their products and services, the customer experiences they deliver, and overall brand image.
If you are a tech leader, your first instinct would be to cut down on technology spend to align with your organisation’s cost optimisation strategy. And that is where you would make the first mistake – this is the time to invest in the right technologies to help your organisation face the uncertainties with agility.
Here are 5 things that you should keep in mind when shaping your organisation’s tech landscape in 2023.
- Focus on the shortest time to value. Choose a few smart digital improvements that are aligned with the strategic goals of the business and deliver value quickly.
- Drive better corporate outcomes through Sustainability programs. The transition to smart and sustainable digital assets and infrastructure should be a top priority for today’s technology leaders.
- Build resilience by improving value chain visibility. Digital technologies will continue to play an important role in providing visibility and insights across the value chains for risk management and resilience.
- Treat location data as a feedstock for AI & Automation. With the increasing importance of automation, especially to contemporary service models like digital twins and metaverse, incorporating spatial and location data into your strategy is essential for staying ahead of the competition and driving meaningful business outcomes.
- Find allies against cyber adversaries. Join the cybersecurity communities that exist in your geography and industry. Participate openly as possible so that lessons are shared quickly and widely. Don’t try to defeat the flood on your own.
Read on to find more.
Download Making the Right Tech Decisions for Better Value as a PDF
Customer experience (CX) is an integral part of a brand today – and excellence in CX is a moving target (think how tools such as ChatGPT can revolutionise communications and CX). Organisations will find themselves aiming for personalised CX across channels of preference, with convenience, empathy, and speed at the core.
Here are the top 5 trends for the Experience Economy for 2023 according to Ecosystm analysts Audrey William, Melanie Disse, and Tim Sheedy.
- Organisations Will Focus on Building a “One CX Workforce”
- AI Will Lead Voice of Customer Programs
- Metadata Will Become Important
- The Conversational AI Market Will Mature
- Organisations Will Go Back to Focusing on Web Experience
Read on for more details.
Download Ecosystm Predicts: The Top 5 Trends for the Experience Economy in 2023 as a PDF
Organisations will continue their quest to become digital and data-first in 2023. Business process automation will be a priority for the majority; but many will look at their data strategically to derive better business value.
As per Ecosystm’s Digital Digital Enterprise Study 2022, organisations will focus equally on Automation and Strategic AI in 2023.
Here are the top 5 trends for the Intelligent Enterprise in 2023 according to Ecosystm analysts, Alan Hesketh, Peter Carr, Sash Mukherjee and Tim Sheedy.
- Cloud Will Be Replaced by AI as the Right Transformation Goal
- Adoption of Data Platform Architecture Will See an Uptick
- Tech Teams Will Finally Focus on Internal Efficiency
- Data Retention/Deletion and Records Management Will Be Top Priority
- AI Will Replace Entire Human Jobs
Read on for more details.
Download Ecosystm Predicts: The Top 5 Trends for the Intelligent Enterprise in 2023 as a PDF
In the rush towards digital transformation, individual lines of business in organisations, have built up collections of unconnected systems, each generating a diversity of data. While these systems are suitable for rapidly launching services and are aimed at solving individual challenges, digital enterprises will need to take a platform approach to unlock the full value of the data they generate.
Data-driven enterprises can increase revenue and shift to higher margin offerings through personalisation tools, such as recommendation engines and dynamic pricing. Cost cutting can be achieved with predictive maintenance that relies on streaming sensor data integrated with external data sources. Increasingly, advanced organisations will monetise their integrated data by providing insights as a service.
Digital enterprises face new challenges – growing complexity, data explosion, and skills gap.
Here are 5 ways in which IT teams can mitigate these challenges.
- Data & AI projects must focus on data access. When the organisation can unify data and transmit it securely wherever it needs to, it will be ready to begin developing applications that utilise machine learning, deep learning, and AI.
- Transformation requires a hybrid cloud platform. Hybrid cloud provides the ability to place each workload in an environment that makes the most sense for the business, while still reaping the benefits of a unified platform.
- Application modernisation unlocks future value. The importance of delivering better experiences to internal and external stakeholders has not gone down; new experiences need modern applications.
- Data management needs to be unified and automated. Digital transformation initiatives result in ever-expanding technology estates and growing volumes of data that cannot be managed with manual processes.
- Cyber strategy should be Zero Trust – backed by the right technologies. Organisations have to build Digital Trust with privacy, protection, and compliance at the core. The Zero Trust strategy should be backed by automated identity governance, robust access and management policies, and least privilege.
Read below to find out more.
Download The Future of Business: 5 Ways IT Teams Can Help Unlock the Value of Data as a PDF
In recent years, businesses have faced significant disruptions. Organisations are challenged on multiple fronts – such as the continuing supply chain disruptions; an ongoing energy crisis that has led to a strong focus on sustainability; economic uncertainty; skills shortage; and increased competition from digitally native businesses. The challenge today is to build intelligent, data-driven, and agile businesses that can respond to the many changes that lie ahead.
Leading organisations are evaluating ways to empower the entire business with data, machine learning, automation, and AI to build agile, innovative, and customer-focused businesses.
Here are 7 steps that will help you deliver business value with data and AI:
- Understand the problems that need solutions. Before an organisation sets out on its data, automation, and AI journey, it is important to evaluate what it wants to achieve. This requires an engagement with the Tech/Data Teams to discuss the challenges it is trying to resolve.
- Map out a data strategy framework. Perhaps the most important part of this strategy are the data governance principles – or a new automated governance to enforce policies and rules automatically and consistently across data on any cloud.
- Industrialise data management & AI technologies. The cumulation of many smart, data-driven initiatives will ultimately see the need for a unified enterprise approach to data management, AI, and automation.
- Recognise the skills gap – and start closing it today. There is a real skills gap when it comes to the ability to identify and solve data-centric issues. Many businesses today turn to technology and business consultants and system integrators to help them solve the skills challenge.
- Re-start the data journey with a pilot. Real-world pilots help generate data and insights to build a business case to scale capabilities.
- Automate the outcomes. Modern applications have made it easier to automate actions based on insights. APIs let systems integrate with each other, share data, and trigger processes; and RPA helps businesses automate across applications and platforms.
- Learn and improve. Intelligent automation tools and adaptive AI/machine learning solutions exist today. What organisations need to do is to apply the learnings for continuous improvements.
Find more insights below.
Download The Future of Business: 7 Steps to Delivering Business Value with Data & AI as a PDF
It is true that the Retail industry is being forced to evolve the experiences they deliver to their customers. However, if Retail organisations are only focused on creating digital experiences, they are not creating the differentiation that will be required to leap ahead of the competition.
It is time for Retail organisations to leverage data to empower multiple roles across the organisation to prepare for the different ways customers want to engage with their brands.
So what are the phases of customer engagement? How are companies such as Singapore Airlines and TikTok preparing for the future of Retail?
Last week I wrote about the need to remove hype from reality when it comes to AI. But what will ensure that your AI projects succeed?
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.
Written with contributions from Ravi Pattamatta and Ratnesh Prasad
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
