India is undergoing a remarkable transformation across various industries, driven by rapid technological advancements, evolving consumer preferences, and a dynamic economic landscape. From the integration of new-age technologies like GenAI to the adoption of sustainable practices, industries in India are redefining their operations and strategies to stay competitive and relevant.
Here are some organisations that are leading the way.
Download ‘From Tradition to Innovation: Industry Transformation in India’ as a PDF
Redefining Customer Experience in the Financial Sector
Financial inclusion. India’s largest bank, the State Bank of India, is leading financial inclusion with its YONO app, to enhance accessibility. Initial offerings include five core banking services: cash withdrawals, cash deposits, fund transfers, balance inquiries, and mini statements, with plans to include account opening and social security scheme enrollments.
Customer Experience. ICICI Bank leverages RPA to streamline repetitive tasks, enhancing customer service with its virtual assistant, iPal, for handling queries and transactions. HDFC Bank customer preference insights to offer tailored financial solutions, while Axis Bank embraces a cloud-first strategy to digitise its platform and improve customer interfaces.
Indian banks are also collaborating with fintechs to harness new technologies for better customer experiences. YES Bank has partnered with Paisabazaar to simplify loan applications, and Canara HSBC Life Insurance has teamed up with Artivatic.AI to enhance its insurance processes via an AI-driven platform.
Improving Healthcare Access
Indian healthcare organisations are harnessing technology to enhance efficiency, improve patient experiences, and enable remote care.
Apollo Hospitals has launched an automated patient monitoring system that alerts experts to health deteriorations, enabling timely interventions through remote monitoring. Manipal Hospitals’ video consultation app reduces emergency department pressure by providing medical advice, lab report access, bill payments, appointment bookings, and home healthcare requests, as well as home medication delivery and Fitbit monitoring. Omni Hospitals has also implemented AI-based telemedicine for enhanced patient engagement and remote monitoring.
The government is also driving the improvement of healthcare access. eSanjeevani is the world’s largest government-owned telemedicine system, with the capacity to handle up to a million patients a day.
Driving Retail Agility & Consumer Engagement
India’s Retail sector, the fourth largest globally, contributes over 10% of the nation’s GDP. To stay competitive and meet evolving consumer demands, Indian retailers are rapidly adopting digital technologies, from eCommerce platforms to AI.
Omnichannel Strategies. Reliance Retail integrates physical stores with digital platforms like JioMart to boost sales and customer engagement. Tata CLiQ’s “phygital” approach merges online and offline shopping for greater convenience while Shoppers Stop uses RFID and data analytics for improved in-store experiences, online shopping, and targeted marketing.
Retail AI. Flipkart’s AI-powered shopping assistant, Flippi uses ML for conversational product discovery and intuitive guidance. BigBasket employs IoT-led AI to optimise supply chain and improve product quality.
Reshaping the Automotive Landscape
Tech innovation, from AI/ML to connected vehicle technologies, is revolutionising the Automotive sector. This shift towards software-defined vehicles and predictive supply chain management underscores the industry’s commitment to efficiency, transparency, safety, and environmental sustainability.
Maruti Suzuki’s multi-pronged approach includes collaborating with over 60 startups through its MAIL program and engaging Accenture to drive tech change. Maruti has digitised 24 out of 26 customer touchpoints, tracking every interaction to enhance customer service. In the Auto OEM space, they are shifting to software-defined vehicles and operating models.
Tata Motors is leveraging cloud, AI/ML, and IoT to enhancing efficiency, improving safety, and driving sustainability across its operations. Key initiatives include connected vehicles, automated driving, dealer management, cybersecurity, electric powertrains, sustainability, and supply chain optimisation.
Streamlining India’s Logistics Sector
India’s logistics industry is on the cusp of a digital revolution as it embraces cutting-edge technologies to streamline processes and reduce environmental impact.
Automation and Predictive Analytics. Automation is transforming warehousing operations in India, with DHL India automating sortation centres to handle 6,000 shipments per hour. Predictive analytics is reshaping logistics decision-making, with Delhivery optimising delivery routes to ensure timely service.
Sustainable Practices. The logistics sector contributes one-third of global carbon emissions. To combat this, Amazon India will convert its delivery fleet to 100% EVs by 2030 to reduce emissions and fuel costs. Blue Energy Motors is also producing 10,000 heavy-duty LNG trucks annually for zero-emission logistics.
Generative AI has stolen the limelight in 2023 from nearly every other technology – and for good reason. The advances made by Generative AI providers have been incredible, with many human “thinking” processes now in line to be automated.
But before we had Generative AI, there was the run-of-the-mill “traditional AI”. However, despite the traditional tag, these capabilities have a long way to run within your organisation. In fact, they are often easier to implement, have less risk (and more predictability) and are easier to generate business cases for. Traditional AI systems are often already embedded in many applications, systems, and processes, and can easily be purchased as-a-service from many providers.
Unlocking the Potential of AI Across Industries
Many organisations around the world are exploring AI solutions today, and the opportunities for improvement are significant:
- Manufacturers are designing, developing and testing in digital environments, relying on AI to predict product responses to stress and environments. In the future, Generative AI will be called upon to suggest improvements.
- Retailers are using AI to monitor customer behaviours and predict next steps. Algorithms are being used to drive the best outcome for the customer and the retailer, based on previous behaviours and trained outcomes.
- Transport and logistics businesses are using AI to minimise fuel usage and driver expenses while maximising delivery loads. Smart route planning and scheduling is ensuring timely deliveries while reducing costs and saving on vehicle maintenance.
- Warehouses are enhancing the safety of their environments and efficiently moving goods with AI. Through a combination of video analytics, connected IoT devices, and logistical software, they are maximising the potential of their limited space.
- Public infrastructure providers (such as shopping centres, public transport providers etc) are using AI to monitor public safety. Video analytics and sensors is helping safety and security teams take public safety beyond traditional human monitoring.
AI Impacts Multiple Roles
Even within the organisation, different lines of business expect different outcomes for AI implementations.
- IT teams are monitoring infrastructure, applications, and transactions – to better understand root-cause analysis and predict upcoming failures – using AI. In fact, AIOps, one of the fastest-growing areas of AI, yields substantial productivity gains for tech teams and boosts reliability for both customers and employees.
- Finance teams are leveraging AI to understand customer payment patterns and automate the issuance of invoices and reminders, a capability increasingly being integrated into modern finance systems.
- Sales teams are using AI to discover the best prospects to target and what offers they are most likely to respond to.
- Contact centres are monitoring calls, automating suggestions, summarising records, and scheduling follow-up actions through conversational AI. This is allowing to get agents up to speed in a shorter period, ensuring greater customer satisfaction and increased brand loyalty.
Transitioning from Low-Risk to AI-Infused Growth
These are just a tiny selection of the opportunities for AI. And few of these need testing or business cases – many of these capabilities are available out-of-the-box or out of the cloud. They don’t need deep analysis by risk, legal, or cybersecurity teams. They just need a champion to make the call and switch them on.
One potential downside of Generative AI is that it is drawing unwarranted attention to well-established, low-risk AI applications. Many of these do not require much time from data scientists – and if they do, the challenge is often finding the data and creating the algorithm. Humans can typically understand the logic and rules that the models create – unlike Generative AI, where the outcome cannot be reverse-engineered.
The opportunity today is to take advantage of the attention that LLMs and other Generative AI engines are getting to incorporate AI into every conceivable aspect of a business. When organisations understand the opportunities for productivity improvements, speed enhancement, better customer outcomes and improved business performance, the spend on AI capabilities will skyrocket. Ecosystm estimates that for most organisations, AI spend will be less than 5% of their total tech spend in 2024 – but it is likely to grow to over 20% within the next 4-5 years.
November has seen uncertainties in the technology market with news of layoffs and hiring freezes from big names in the industry – Meta, Amazon, Salesforce, and Apple to name a few. These have impacted thousands of people globally, leaving tech talent with one common question, ‘What next?’
While the current situation and economic trends may seem grim, it is not all bad news for tech workers. It is true that people strategies in the sector may be impacted, but there are still plenty of opportunities for tech experts in the industry.
Here is what Ecosystm Analysts say about what’s next for technology workers.
Today, we are seeing two quite conflicting signals in the market: Tech vendors are laying off staff; and IT teams in businesses are struggling to hire the people they need.
At Ecosystm, we still expect a healthy growth in tech spend in 2023 and 2024 regardless of economic conditions. Businesses will be increasing their spend on security and data governance to limit their exposure to cyber-attacks; they will spend on automation to help teams grow productivity with current or lower headcount; they will continue their cloud investments to simplify their technology architectures, increase resilience, and to drive business agility. Security, cloud, data management and analytics, automation, and digital developers will all continue to see employment opportunities.
If this is the case, then why are tech vendors laying off headcount?
The slowdown in the American economy is a big reason. Tech providers that are laying of staff are heavily exposed to the American market.
- Salesforce – 68% Americas
- Facebook – 44% North America
- Genesys – around 60% in North America
Much of the messaging that these providers are giving is it is not that business is performing poorly – it is that growth is slowing down from the fast pace that many were witnessing when digital strategies accelerated.
Some of these tech providers might also be using the opportunity to “trim the fat” from their business – using the opportunity to get rid of the 2-3% of staff or teams that are underperforming. Interestingly, many of the people that are being laid off are from in or around the sales organisation. In some cases, tech providers are trimming products or services from their business and associated product, marketing, and technical staff are also being laid off.
While the majority of the impact is being felt in North America, there are certainly some people being laid off in Asia Pacific too. Particularly in companies where the development is done in Asia (India, China, ASEAN, etc.), there will be some impact when products or services are discontinued.
While it is not all bad news for tech talent, there is undoubtedly some nervousness. So this is what you should think about:
Change your immediate priorities. Ecosystm research found that 40% of digital/IT talent were looking to change employers in 2023. Nearly 60% of them were also thinking of changes in terms of where they live and their career.
This may not be the right time to voluntarily change your job. Job profiles and industry requirements should guide your decision – by February 2023, a clearer image of the job market will emerge. Till then, upskill and get those certifications to stay relevant!
Be prepared for contract roles. With a huge pool of highly skilled technologists on the hunt for new opportunities, smaller technology providers and start-ups have a cause to celebrate. They have faced the challenge of getting the right talent largely because of their inability to match the remunerations offered by large tech firms.
These companies may still not be able to match the benefits offered by the large tech firms – but they provide opportunities to expand your portfolio, industry expertise, and experience in emerging technologies. This will see a change in job profiles. It is expected that more contractual roles will open up for the technology industry. You will have more opportunities to explore the option of working on short-term assignments and consulting projects – sometimes on multiple projects and with multiple clients at the same time.
Think about switching sides. The fact remains that digital and technology upgrades continue to be organisational priorities, across all industries. As organisations continue on their digital journeys, they have an immense potential to address their skills gap now with the availability of highly skilled talent. In a recently conducted Ecosystm roundtable, CIOs reported that new graduates have been demanding salaries as high as USD 200,000 per annum! Even banks and consultancies – typically the top paying businesses – have been finding it hard to afford these skills! These industries may well benefit from the layoffs.
If you look at technology job listings, we see no signs of the demand abating!
Life never gets any easier for the digital and information technology teams in organisations. The range and reach of the different technologies continue to open new opportunities for organisations that have the foresight and strategy to chase them. Improving offers for existing customers and reaching new segments depend on the organisation’s ability to innovate.
But the complexity of the digital ecosystem means this ability to innovate will be heavily constrained, causing improvements to take longer and cost more in many cases. Addressing the top business priorities expressed in the Ecosystm Digital Enterprise Study, 2022, will need tech teams to look to simplify as well as add features.
Complexity is Not Just an IT Issue
Many parts of an organisation have been making decisions on implementing new digital capabilities, particularly those involved in remote working. Frequently, the IT organisation has not been involved in the selection, implementation and use of these new facilities.
The number of start-up organisations delivering SaaS has continued to explode. A particular area has been the expansion of co-creation tools used by teams to deliver outcomes. In many cases, these have been introduced by enthusiastic users looking to improve their immediate working environment without the understanding of single-sign-on requirements, security and privacy of information or the importance of backup and business continuity planning.
SaaS tools such as Notion, monday.com and ClickUp (amongst many, many others), are being used to coordinate and manage teams across organisations of all sizes. While these are all cloud services, the support and maintenance of them ultimately will fall to the IT organisation. And they won’t be integrated at all with the tools the IT organisation uses to manage and improve user experience.
Every new component adds to the complexity of the tech environment – but with that complexity comes increased dependencies between components, which slows an organisation’s ability to adapt and evolve. This means each change needs more work to deliver, costs increase, and it takes longer to deliver value.
And this increasing complexity causes further problems with cybersecurity. Without regular attention, legacy systems will increase the attack surface of organisations, making it easier to compromise an organisation’s environment. At a recent executive forum with CISOs, attendees rated the risks caused by their legacy systems as their most significant concern.
An organisation’s leadership needs to both simplify and advance their organisation’s digital capabilities to remain competitive. This balance should not be left to the IT organisation to achieve as they will not be able to deliver both without wider support and recognition of the problems.
Discriminate on Differentiating Skills
One thing we can be sure of is that we won’t be able to employ all the skills we need for our future capabilities. We are not training enough people in the skills that we need now and for the future, and the range of technologies continues to expand, increasing the number of skills that we will need to keep an organisation running.
Most organisations are not removing or replacing ageing systems, preferring to keep them running at an apparently low cost. Often these legacy systems are fully depreciated, have low maintenance costs and have few changes made to them, as other areas of the organisation offer better investment options. But this also means that the old skills remain necessary.
So organisational leaders are adding new skills requirements on top of old, with the older skills being less attractive with so many new languages, frameworks and databases becoming available. Wikipedia has a very long list of languages that have been developed over the years. Some from the 1950s, like FORTRAN and LISP, continue to be used today.
Organisations will not be in a position to employ all the skills it needs to implement, develop and maintain for its digital infrastructure and applications. The choice is going to be which skills are most important to an organisation. This selection needs to be very discriminating and focus on differentiating skills – those that really make a difference within your ecosystem, particularly for your customers and employees.
Organisations will need a great partner who can deliver generic skills and more services. They will have better economies of scale and skill and will free management to attend to those things most important to customers and employees.
Hybrid Cloud has an Edge
Almost every organisation has a hybrid cloud environment. This is not a projection – it has already happened. And most organisations are not well equipped to deal with this situation.
Organisations may not be aware that they are using multiple public clouds. Many of the niche SaaS applications used by an organisation will use Microsoft Azure, AWS or GCP, so it is highly likely organisations are already using multiple public clouds. Not to mention the offerings from vendors such as Oracle, Salesforce, SAP and IBM. IT teams need to be able to monitor, manage and maintain this complex set of environments. But we are only in the early stages of integrating these different services and systems.
But there is a third leg to this digital infrastructure stool that is becoming increasingly important – what we call “the Edge” – where applications are deployed as part of the sensors that collect data in different environments. This includes applications such as pattern recognition systems embedded in cameras so that network and server delays cannot affect the performance of the edge systems. We can see this happening even in our homes. Google supports their Nest domestic products, while Alexa uses AWS. Not to mention Amazon’s Ring home security products.
With the sheer number of these edge devices that already exist, the complexity it adds to the hybrid environment is huge. And we expect IT organisations to be able to support and manage these.
Simplify, Specialise, Scale
The lessons for IT organisations are threefold:
- Simplify as much as possible while you are implementing new features and facilities. Retiring legacy infrastructure elements should be consistently included in the IT Team objectives. This should be done as part of implementing new capabilities in areas that are related to the legacy.
- Specialise in the skills that are the differentiators for your organisation with its customers and employees. Find great partners who can provide the more generic skills and services to take this load off your team.
- Scale your hybrid management environment so that you can automate as much of the running of your infrastructure as possible. You need to make your IT Team as productive as possible, and they will need power tools.
For IT vendors, the lessons are similar.
- Simplify customer offers as much as possible so that integration with your offering is fast and frugal. Work with them to reduce and retire as much of their legacy as possible as you implement your services. Duplication of even part of your offer will complicate your delivery of high-quality services.
- Understand where your customers have chosen to specialise and look to complement their skills. And consistently demonstrate that you are the best in delivering these generic capabilities.
- Scale your integration capabilities so that your customers can operate through that mythical single pane of glass. They will be struggling with the complexities of the hybrid infrastructure that include multiple cloud vendors, on-premises equipment, and edge services.
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In comparison to the golden days of the second half of the 20th century, the last two decades have been hard hit. The fragility of globalisation and that prosperous economic model so beautifully enabled by a 70-year technology revolution, have tested business continuity and disaster recovery plans like never before.
Looking Back
During this time the global community has lurched through tech and property driven financial crisis. It has endured endemic terrorism, and a crippling pandemic. And it has been fragmented by the existential threats of energy and climate, world order dislocation, and challenges to once unshakable fiat money. The wonderfully efficient business models and supply chains enabled by W. Edwards Deming following World War II are fractured and broken. Despite all these challenges, the desire for a hopeful return to a golden age of global prosperity is clearly evident. Just maybe not as we know it.
The period between W. Edwards Deming and Dotcom (let’s say 1950-2000) ushered in ERP and the modern software revolution. Over decades, highly refined processes and perfected workflows shifted from paper and clipboards into mainframe environments – from conveyor belts to computing and from ledgers to LANs.
In the progression to slightly less monolithic server-based business applications, millions of lines of customised code are transferred into configurable data fields, coupled with ready-made workflow connections, and processes based on standards set by leading companies and their representative bodies. The standardisation of business systems lowered the entry point for new enterprises, spawned new industries, and ultimately allowed SaaS to proliferate.
ERP was a true revolution in automating process and quality management systems and building the modern world. Cloud was then a transformation for ERP. It was an innovation on an original idea, but it wasn’t the next revolution. In many ways, by standardising business systems, we went too far. The vendor market over-estimated what configuration over customisation could achieve and ultimately set unachievable expectations in relation to client outcomes. On the client side, end-user organisations seized on vanilla processes and workflows and got lazy about working out solutions to their own problems. In chasing out-of-the-box software they sought to expedite, and even outsource, the hard work. In doing so, the core driver of 20th century post war economic prosperity was forgotten.
Looking Ahead
In business transformations there are no short cuts to results
One of the defining social drivers of the 21st century is a move towards the concept of individualism. We see it everywhere. In the transformation of traditional marriage, family, and identity structures. In the migration away from the concept of houses and homes, in the rise of the gig economy, and even in the regulatory schemes of government, financial and insurance services. The individual sits at the centre of new globalisation economic design and is giving rise to the next business systems revolution. At ServiceNow Knowledge 2022 I was fortunate to hear Dr Catriona Wallace and the Hon Victor Dominello MP discuss it in the context of their recent research. Dr Wallace described the trend as, “know me and care about me”, and discussed the requirements to operate within a world of both hyper-personalisation and ethical restraint.
This time however the business systems revolution to support this change is not being driven by process efficiencies and quality management, though they remain important tools. It is being driven by the pursuit of Experiential Excellence. You’ve heard it many times before and once you’ve seen it you can’t unsee it – Customer Experience, Employee Experience, Digital Experience. These are all ambitions of populist organisational and service transformation agendas with Experiential Excellence at their core.
For business and technology leaders it requires a mental shift. Traditional ERP alone will not get us there. It means a new business systems methodology is required to accompany, and reflect the challenges of the modern world, not one created more than 70 years ago.
An Experiential Excellence platform isn’t just a new ERP. It’s a new type of system capable of operating at speed and with breakthrough power; but it is also capable of breaking the intellectual shackles of pre-configuration to help organisations recapture the essence of what Deming started so long ago and we somehow lost along the way: The ability to think about and solve any kind of complex, innovative and multi-objective, multi-stakeholder problem. And I think that ServiceNow, and the Now Platform, is the first company (and business system) to do it.
The sense of something special was clearly evident among ServiceNow staff and partners, at the event. But I don’t think they have yet nailed the messaging. And the reason is because there is still such a strong gravitational pull towards the old ERP model among end-user clients. This reinforces a need for ServiceNow to still define itself by the last 50 years of system technology rather than the next 50.
That needs to change. So, next time when a client asks, is ServiceNow an ERP or is it an RPA platform or something else, the answer is – it is neither, and both, and all, and sometimes at the same time. This wonderful superposition, the same quantum computing characteristic that allows a particle to be one thing, or either, or both, all at the same time, is the very essence of their opportunity – should they wish to take it.
To be a leader in the new quantum age of computing will mean taking the brave step of unshackling themselves from the 20th century view of ERP and lead the redefinition of business systems for the quantum age. Let the revolution begin.
Digital transformation has been a key company objective over the last two years – and more than a third of enterprises in ASEAN have it as their key business priority in 2022-23. They are aiming to be agile and digital organisations – with access to real-time data insights at their core.
Businesses have learned that their technology systems need to be scalable, accessible, easy to manage, fast to deploy and cost effective. Cloud infrastructure, platforms and software has become key enablers of business agility and innovation.
But the expansion of cloud applications has also seen an infrastructure and applications sprawl – which makes it essential for organisation to re-evaluate their cloud strategy.
Here are 5 insights that will help you shape your Cloud Strategy.
- Technology Change Management. Your cloud strategy must define the infrastructure and data architecture, security and resiliency measures, the technology environment management model, and IT operations.
- Building Scalable Enterprises. Focus on seamless access to all organisational data, irrespective of where they are generated (enterprise systems, IoT devices or AI solutions) and where they are stored (public cloud, on-premises, Edge, or co-location facilities).
- A Hybrid Multicloud Environment. For a successful hybrid multi cloud environment, keep a firm eye on hybrid cloud management, a suitable FinOps framework that balances performance and cost, and integration.
- A Technology-Neutral Approach. Partnering with a technology-neutral cloud services provider that leverages the entire tech ecosystem, will be critical.
- “Hybrid Cloud” Can Mean Many Things. Work with a cloud services partner, that has broad and deep capabilities across multiple hyperscalers and is able to address the unique requirements of your organisation.
Read on for more insights
Download 5 Key Insights to Shape Your Cloud Strategy – An ASEAN View as a PDF
In this Insight, guest author Anirban Mukherjee lists out the key challenges of AI adoption in traditional organisations – and how best to mitigate these challenges. “I am by no means suggesting that traditional companies avoid or delay adopting AI. That would be akin to asking a factory to keep using only steam as power, even as electrification came in during early 20th century! But organisations need to have a pragmatic strategy around what will undoubtedly be a big, but necessary, transition.”
After years of evangelising digital adoption, I have more of a nuanced stance today – supporting a prudent strategy, especially where the organisation’s internal capabilities/technology maturity is in question. I still see many traditional organisations burning budgets in AI adoption programs with low success rates, simply because of poor choices driven by misplaced expectations. Without going into the obvious reasons for over-exuberance (media-hype, mis-selling, FOMO, irrational valuations – the list goes on), here are few patterns that can be detected in those organisations that have succeeded getting value – and gloriously so!
Data-driven decision-making is a cultural change. Most traditional organisations have a point person/role accountable for any important decision, whose “neck is on the line”. For these organisations to change over to trusting AI decisions (with its characteristic opacity, and stochastic nature of recommendations) is often a leap too far.
Work on your change management, but more crucially, strategically choose business/process decision points (aka use-cases) to acceptably AI-enable.
Technical choice of ML modeling needs business judgement too. The more flexible non-linear models that increase prediction accuracy, invariably suffer from lower interpretability – and may be a poor choice in many business contexts. Depending upon business data volumes and accuracy, model bias-variance tradeoffs need to be made. Assessing model accuracy and its thresholds (false-positive-false-negative trade-offs) are similarly nuanced. All this implies that organisation’s domain knowledge needs to merge well with data science design. A pragmatic approach would be to not try to be cutting-edge.
Look to use proven foundational model-platforms – such as those for NLP, visual analytics – for first use cases. Also note that not every problem needs AI; a lot can be sorted through traditional programming (“if-then automation”) and should be. The dirty secret of the industry is that the power of a lot of products marketed as “AI-powered” is mostly traditional logic, under the hood!
In getting results from AI, most often “better data trumps better models”. Practically, this means that organisations need to spend more on data engineering effort, than on data science effort. The CDO/CIO organisation needs to build the right balance of data competencies and tools.
Get the data readiness programs started – yesterday! While the focus of data scientists is often on training an AI model, deployment of the trained model online is a whole other level of technical challenge (particularly when it comes to IT-OT and real-time integrations).
It takes time to adopt AI in traditional organisations. Building up training data and model accuracy is a slow process. Organisational changes take time – and then you have to add considerations such as data standardisation; hygiene and integration programs; and the new attention required to build capabilities in AIOps, AI adoption and governance.
Typically plan for 3 years – monitor progress and steer every 6 months. Be ready to kill “zombie” projects along the way. Train the executive team – not to code, but to understand the technology’s capabilities and limitations. This will ensure better informed buyers/consumers and help drive adoption within the organisation.
I am by no means suggesting that traditional companies avoid or delay adopting AI. That would be akin to asking a factory to keep using only steam as power, even as electrification came in during early 20th century! But organisations need to have a pragmatic strategy around what will undoubtedly be a big, but necessary, transition.
These opinions are personal (and may change with time), but definitely informed through a decade of involvement in such journeys. It is not too early for any organisation to start – results are beginning to show for those who started earlier, and we know what they got right (and wrong).
I would love to hear your views, or even engage with you on your journey!
The views and opinions mentioned in the article are personal.
Anirban Mukherjee has more than 25 years of experience in operations excellence and technology consulting across the globe, having led transformations in Energy, Engineering, and Automotive majors. Over the last decade, he has focused on Smart Manufacturing/Industry 4.0 solutions that integrate cutting-edge digital into existing operations.
The semiconductor industry is 70-years old and has a prominent – and sometimes inconspicuous – presence in our daily lives. Many of us, however, have become more aware of the industry and the ramifications of its disruption, because of recent events. The pandemic, natural disasters, power outages, geo-political conflicts, and accelerated digital transformation have all combined to disrupt the semiconductor sector, leaving no organisation immune to the impacts of the continuing global chip crisis.
It is estimated that 200 downstream industries have failed to fulfill customer demands owing to the silicon scarcity, ranging from automotive, consumer electronics, utilities and even the supply of light fixtures.
This Ecosystm Bytes discusses the impact of the crisis and highlights major initiatives that chip manufacturers and governments are taking to combat it, including:
- The factors leading to the shortage in the semiconductor industry
- The impact on industry sectors such as Automotive, Consumer Electronics and MedTech
- How leading chip makers such as TSMC, Intel and Samsung are increasing their manufacturing capabilities
- The importance of Asia to the semiconductor industry
- How countries such as Malaysia and India are aiming to build self-sufficiency in the industry
Read on to find out more.
It is an incredible time of change for the city and regional governments where every strategic activity – especially in these globally challenging times – presents a significant opportunity for transformation. To continue to meet the changing needs of the communities they serve, every modern city government’s technology story is a work in progress. While this is the mantra for successful continuous improvement it also describes the best strategic approach for how municipalities should manage their corporate application replacement programs.
Unfortunately, significant systems upgrade and replacement programs are regularly approached as complex, multi-tasking activities that have a hard start, a defined program, and a date-stamped end. In taking this traditional project implementation approach, intuitively, many organisations believe that doing as much as possible, in as quick a time as possible, ultimately helps to achieve twice as much within the same time. The result is more likely to be half as much, and at lower levels of quality and enjoyment for all involved. This manifests as project scope creep and budget overruns.
Aside from these big bang approaches, thanks to large implementation costs and stringent regulatory oversight, local governments are also forced to think upfront about the potential future value created by a significant core system technology change. The pressure of moving at high speed, and with a dominant technology focus, can obscure both the true organisational cost and ultimate value of the program. This mentality prevails even when it is acknowledged that activities associated with a transformation program will eventually usher in a period of significant change – that is not limited to the changing core corporate applications environment itself.
The 4-Part ERP Transformation Trap is All Too Common in City Government
An over-reliance on technology to deliver business transformation outcomes. Local governments everywhere continue to pursue strategic plans that are either wholly defined or implicitly reliant on world-class customer experience (CX), employee experience (EX), and digital transformation (DX) capabilities. Despite these being business-oriented strategies, organisations then pursue an over-reliance on technology – usually winner-take-all ERP led procurements – to achieve them.
Choosing an industry solution focused on the wrong business model. The chance of achieving these digital transformation outcomes is further obscured when the customer is not central to the data model. The core corporate application technology underpinning the sector’s leading ERP programs is largely based on a property-centric model – where the customer is a subordinate attribute of a property, and the property asset defines the business process and individual. It is a challenge for any council to deliver contemporary customer-first digital transformation with a property-centric approach. To realise customer and employee-centric outcomes, councils must therefore rethink their project’s business methodology and ask themselves, “what is our primary focus here?”. This is never more important than when replacing legacy systems.
Inability to realise that a winner-take-all ERP solution is not an architectural choice. ERP is important but it is not everything. The traditional council ERP is just one important part of an overall capability that allows authorities to longitudinally manage the impacts and opportunities of change across their organisation, communities, and stakeholder ecosystems. Having chosen a sector specific ERP solution, city governments realise too late that no single technology vendor has a best-of-breed solution to achieve the desired DX outcomes. That requires a more sophisticated architectural approach.
Failure to acknowledge there is no finish line to transformation. Like many worthwhile activities, the prize in DX is in the journey, not in the cup. While there can be an end to “project scope”, there should be no “end point” for an ERP transformation program. Only once these challenges are acknowledged and accepted, can transformation be assimilated into the organisation to ensure the council is technically capable of delivering the implicit outcome for the organisation. This could simply be defined as ‘a contemporary business approach to managing the money, the assets, the community, the customers, and the staff of regional government.’
A Better Way: Re-Architecting for Project Success
Where opportunities to meet increasing CX and EX demands arise, especially through ERP and corporate application renewal programs, successful projects in contemporary councils require a service-oriented architecture not found in contemporary or legacy ERP systems alone.
Beyond the property-centric challenges already outlined, even contemporary systems and suppliers can be among the least flexible to the changing data management requirements of many organisations which call for significantly more robust data, integration and application friendly infrastructure management environments.
Customer centricity, data management, integration and software infrastructure capabilities must take precedent over an aging view of single-vendor dominance in the city government sector, especially in middle- and back-office functions, which are typically void of true differentiation opportunities and prone to confining organisations to technology-led and locked projects.
Rather than tendering for a single software provider or platform, contemporary city governments must ditch the old approach to procuring a winning ERP vendor and take steps to establish the following Big 5 platform capabilities (Figure 1). And then foster the contemporary workforce to support them.
For several decades now many organisations have attempted to short-circuit the city government ERP challenge. Fundamentally, technology transformation is not possible without technology change. A non-negotiable part of that change is a shift away from the psychology of brand-based procurement towards a new architectural approach which, like all businesses, is adaptable to change over a long period of time.
