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The Evolution of Global Capability Centres in India

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In this Insight, our guest author Anupam Verma talks about how the Global Capability Centres (GCCs) in India are poised to become Global Transformation Centres. “In the post-COVID world, industry boundaries are blurring, and business models are being transformed for the digital age. While traditional functions of GCCs will continue to be providing efficiencies, GCCs will be ‘Digital Transformation Centres’ for global businesses.”

Anupam Verma, Senior Leadership Team, ICICI Bank

India has a lot to offer to the world of technology and transformation. Attracted by the talent pool, enabling policies, digital infrastructure, and competitive cost structure, MNCs have long embraced India as a preferred destination for Global Capability Centres (GCCs). It has been reported that India has more than 1,700 GCCs with an estimated global market share of over 50%.

GCCs employ around 1 million Indian professionals and has an immense impact on the economy, contributing an estimated USD 30 billion. US MNCs have the largest presence in the market and the dominating industries are BSFI, Engineering & Manufacturing, Tech & Consulting.

GCC capabilities have always been evolving

The journey began with MNCs setting up captives for cost optimisation & operational excellence. GCCs started handling operations (such as back-office and business support functions), IT support (such as app development and maintenance, remote IT infrastructure, and help desk) and customer service contact centres for the parent organisation.

In the second phase, MNCs started leveraging GCCs as centers of excellence (CoE). The focus then was product innovation, Engineering Design & R&D. BFSI and Professional Services firms started expanding the scope to cover research, underwriting, and consulting etc. Some global MNCs that have large GCCs in India are Apple, Microsoft, Google, Nissan, Ford, Qualcomm, Cisco, Wells Fargo, Bank of America, Barclays, Standard Chartered, and KPMG.

In the post-COVID world, industry boundaries are blurring, and business models are being transformed for the digital age. While traditional functions of GCCs will continue to be providing efficiencies, GCCs will be “Digital Transformation Centres” for global businesses.

The New Age GCC in the post-COVID world

On one hand, the pandemic broke through cultural barriers that had prevented remote operations and work. The world became remote everything! On the other hand, it accelerated digital adoption in organisations. Businesses are re-imagining customer experiences and fast-tracking digital transformation enabled by technology (Figure 1). High digital adoption and rising customer expectations will also be a big catalyst for change.

Impact of COVID-19 on Digital Transformation

In last few years, India has seen a surge in talent pool in emerging technologies such as data analytics, experience design, AI/ML, robotic process automation, IoT, cloud, blockchain and cybersecurity. GCCs in India will leverage this talent pool and play a pivotal role in enabling digital transformation at a global scale. GCCs will have direct and significant impacts on global business performance and top line growth creating long-term stakeholder value – and not be only about cost optimisation.

GCCs in India will also play an important role in digitisation and automation of existing processes, risk management and fraud prevention using data analytics and managing new risks like cybersecurity.

More and more MNCs in traditional businesses will add GCCs in India over the next decade and the existing 1,700 plus GCCs will grow in scale and scope focussing on innovation. Shift of supply chains to India will also be supported by Engineering R & D Centres. GCCs passed the pandemic test with flying colours when an exceptionally large workforce transitioned to the Work from Home model. In a matter of weeks, the resilience, continuity, and efficiency of GCCs returned to pre-pandemic levels with a distributed and remote workforce.

A Final Take

Having said that, I believe the growth spurt in GCCs in India will come from new-age businesses. Consumer-facing platforms (eCommerce marketplaces, Healthtechs, Edtechs, and Fintechs) are creating digital native businesses. As of June 2021, there are more than 700 unicorns trying to solve different problems using technology and data. Currently, very few unicorns have GCCs in India (notable names being Uber, Grab, Gojek). However, this segment will be one of the biggest growth drivers.

Currently, only 10% of the GCCs in India are from Asia Pacific organisations. Some of the prominent names being Hitachi, Rakuten, Panasonic, Samsung, LG, and Foxconn. Asian MNCs have an opportunity to move fast and stay relevant. This segment is also expected to grow disproportionately.

New age GCCs in India have the potential to be the crown jewel for global MNCs. For India, this has a huge potential for job creation and development of Smart City ecosystems. In this decade, growth of GCCs will be one of the core pillars of India’s journey to a USD 5 trillion economy.

The views and opinions mentioned in the article are personal.
Anupam Verma is part of the Senior Leadership team at ICICI Bank and his responsibilities have included leading the Bank’s strategy in South East Asia to play a significant role in capturing Investment, NRI remittance, and trade flows between SEA and India.

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Personalisation – The New Digital Imperative in Financial Services

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If you are a digital leader in the Financial Services industry (FSI), you have already heard this from your customers: ‘Why is it that Netflix and Amazon can make more relevant and personalised offers than my bank or wealth manager?’ Digital first players are obsessed with using data to understand their customer’s commercial and consumer behaviour. Financial Services will need to become just as obsessed with personalisation of offerings and services if they want to remain relevant to their customers. Ecosystm research finds that leveraging data to offer personalised service and product offerings to their clients is the leading digital priority in more than 50% of FSI organisations. 

Banks, particularly, are both in a strong position and have a strong incentive to offer this personalisation. Their retail customers’ expectations are now shaped by the experience they have received from their favorite digital first firms, and they are making it increasingly clear that they expect personalised offerings from their banks.  Furthermore, they are well positioned as a facilitator of commercial relationships between two segments of customers – consumers and merchants. The amount of data they hold on consumer interactions is comprehensive – and more importantly they are a trusted custodian of their customers’ data and privacy. 

The Barriers to Personalisation

So, what is stopping them? Here are three insights from over 12 years of experience driving digitisation of Financial Services:

  • Systems Legacy.  Often the data and core banking systems do not allow for easy access and analysis of the required data across the data sets required (eg. Consumers and Merchants).
  • Investment Priorities. There is still a significant investment happening in compliance and modernisation of core banking systems. Too often the focus of these programs can be myopic, and banks miss the opportunity to solve multiple pain points with their investments driven by overly focused problem statements.
  • Culture and Purpose. Are banks stuck in a paradigm of their own making – defining their business models by what has served them well in the past? Will Amazon think about its provision of working capital to their small and medium business partners the same way as a bank does?

Vendor Focus – Crayon Data

Thankfully, there is a new breed of tech vendors who is making it easier for banks to drive personalisation of their offerings and connect customers from across segments. Crayon Data is a good example, with their maya.ai engine unearthing the preferences of customers and matching them to offerings from qualified merchants. It benefits all parties:

  • The Consumer receives relevant offers, is served from discovery to fulfillment on a single platform and all personal data and information guarded by their bank. 
  • For Merchants, it allows them to reach the right customers at the right moment, develop valuable marketing and insights and all this directly from their bank partner’s platform.
  • For Banks, it provides a scalable model for offer acquisition and easily configurable and measurable consumer engagement.

maya.ai leverages patented AI to create a powerful profile of each customer based on their buying habits and comparing these with millions of other consumers drawn in from their unstructured data sets and graph-based methodology. They then use their algorithms to assist their Financial Services client to make relevant offerings from qualified merchants to consumers in the right channel, at the right moment. All of this is done without exposing personal client information, as the data sets are based on behaviour rather than identity.

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Conclusion

There are significant considerations for banks in offering these types of capabilities, such as:

  • Privacy. While the technology operates on non-identifiable information, the perception of clients being ‘stalked’ by their bank in order to drive business to a merchant is one that would need to be managed carefully. 
  • Consumer opt-out. The ability for customers to opt out of this type of service is critical.
  • Consumer financial wellbeing. It may be in the best interests of some consumer to not receive merchant offers, for instance where they are managing to a strict budget. These considerations can be baked into the overall customer journey (eg. prompts when the consumer is nearing their self-imposed monthly budget for a category), but care will need to be taken to keep customers’ best interests at heart.

While there are multiple challenges to overcome, the fact remains that personalisation is quickly becoming a core expectation for consumers. How will banks respond, and will we see AI use cases like Crayon Data become more prominent?

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Personalising Your Customer Experience is Standard Practice Today

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Back in 2019 – when life was simpler and customers only expected minor miracles from the brands they interacted with – personalisation of the customer experience (CX) was a “good idea but the time has not yet come” for the majority of marketing and CX professionals.

Fast forward 24 months and the world has changed – in more ways than we could have imagined! For a start, CX dropped off the top business priority during 2020 as businesses adapted to the changing market and employee experiences. But as some economies start to create a new sense of normal, CX has returned to the top of the list of business priorities (Figure 1) – renewing pressure on CX teams to create great experiences for customers.

Key Organisational Priorities - 2020 vs 2021

In 2020 many marketing teams went back to the drawing board to create truly meaningful customer experiences. Suddenly “trust” was a core expectation of a brand, and that lens allowed marketers and CX teams to rethink what a personalised experience looks like. It is no longer about selling more products and creating more chances for commerce – it is now about creating an experience that makes brands easy to deal with. It is about understanding the customer and creating an optimised experience when they want or need to interact with the brand. A great personalised experience feels normal today – it has lost the “creepy” edge and is now about the brand giving customers the service, products, or levers that they need when and where they need them.

For some brands and customers, a personalised experience is about getting out of the way of customers and just giving them the outcome they desire. For others it is about creating a memorable journey. Some customers require that extra hand holding along the way and need to be nudged in the right direction, and just need to be left alone to make their decisions – not requiring that extra EDM, alert or message.

In some sectors – such as Banking and eCommerce – if you are not personalising your CX you are a long way behind, but in others, such as Government and Insurance, personalisation is only beginning to gain traction today, and will see slow and steady growth over the next few years.

Good Data is Key to a Great Personalisation Strategy

Lack of data is the primary reason personalisation fails and why some marketing teams have abandoned their personalisation efforts. The right data may not exist completely within your business – you may need to partner or work with ecosystem providers to create a complete view of your customers – and new restrictions around the use of cookies is making this harder to achieve. Forward thinking businesses have already forged partnerships with third parties and partners to share relevant data to help them create the personalised experience their customers demand.

Personalisation Should Apply Across the Customer Journey  

A clear understanding of brand values, customer desires and ideal customer journeys is also important to ensure personalised experiences meet the needs of customers. Creating a personalised experience that deviates from brand values means that either brands don’t understand their customers, or customer experience professionals don’t understand their brands (or both!).

Personalisation needs to focus on the entire customer journey – from prospect through to customer and even through to churn. While you have significantly more data about your customers than your prospects, a personalised experience for non-customers is still possible and sets the scene for better and easier CX once your prospects take the longer journey with your brand. Creating a personalised churn experience – making the departure from your brand memorable, friendly and easy – provides the perfect springboard for return and tells your customers that you care about them through the entire journey.

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Build a Proof of Concept for Personalisation

If you have not yet started personalising your customer’s experience, now is the perfect time to build a Proof of Concept (POC) demonstrating the business and customer outcomes you can achieve. This will help the CX and/or marketing teams to understand what data you need to collect from existing systems and processes – or source externally to create the desired experience. Initially your personalisation experience may target a limited number of key personas – but it should have the capability to roll out to all customers and/or prospects, eventually considering many scenarios and requirements. It should continue to learn and adapt. Too many businesses discovered during the pandemic that static personalisation programs will fail when market conditions change.

The POC can provide the data that your senior leadership will need to deepen their investments in and think of personalisation as a business capability – not a single project. They can demonstrate the ROI (or lack of return) and will help to guide the larger spend should the POC be a success.

Invest in Behavioural Science Skills

Building a successful personalisation strategy often goes beyond simply listening to the experts within the business and even listening to your customers. Often your customers don’t know what affects their behaviour – and will mis-report motivations or mis-attribute actions. It is important to understand the science behind behaviour – what is possible, what can work, what is guidance and what is coercion. These experts, along with your legal or privacy teams, can help to set up the guide rails for the personalisation program to operate within, and help you create customer journeys where customers can achieve their desired outcomes.

Target Consent as a Key Customer KPI

Consent is a key enabler of deep personalisation capabilities. While some level of personalisation without formal consent can be created, the real benefits of personalised journeys come with consent to use customer data to offer better services. Many businesses ask for consent in the sign-up process, but often it feels like wishful thinking – not a serious attempt to offer a better customer experience. Businesses that make “Consent to Use Data” a CX KPI think more broadly of the customer journey, the brand promise and what that means to levels of consent. It isn’t a “tick-a-box” activity at sign-up – it considers what the customer wants to get out of the engagement or a longer relationship. It focuses on helping customers achieve their instant goals more effectively and the benefits the data can bring to nurture a longer-term relationship.

Businesses that seek a higher level of consent use more tangible outcomes, simpler language and no “sweeping statements” in their consent request. They are explicit how they will use data and what data they will use. Sometimes they don’t even ask for consent to use data at sign-up – they ask after they have formed a relationship and the customer has developed a level of trust in the brand or company.

Start Your Personalisation Journey Today

Your competitors are already thinking about personalisation – some have even implemented personalised elements within their existing or new customer journeys. Personalisation – while easier than ever – is still a significant capability to build within your business. You are likely to need new technology tools and/or platforms, new skills, and new budgets. The impact for your customers – and therefore for your business – can be significant. And the impact of no action can potentially be damaging. Start your personalisation journey today to help your business take the next step towards becoming a customer-obsessed, agile, and digital business.

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Ecosystm RNx: Top 10 Global AI & Automation Vendor Rankings

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Ecosystm RNx is an objective vendor ranking based on in-depth, quantified ratings from technology decision-makers on the Ecosystm platform. In this edition, we rank the Top 10 Global AI & Automation Vendors.

If you are an End-User, you are realising that the right investments in Data & AI now will be the key to your future success. This vendor ranking will help you evaluate your buying decisions based on key evaluation ratings by your peers across a number of key metrics and benchmarks, including customer experience.

If you are an AI & Automation Vendor, it’s an opportunity to understand how your customers rate you on capabilities and their overall customer experience.

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Fragmenting Services Complicating Contracting

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New ‘as-a-service’ products are continuing to expand the options that are available to organisations, fragmenting the functionality used across many technology providers. Choosing the right products is getting more and more difficult – kind of like trying to choose your VOD service at home.

How many VOD networks do you use?

Do you remember the time when your access to video content was through either broadcast TV or cable TV? And when a regular trip to the video store was part of life? And you had to watch programs when the networks scheduled them?

Most programming was only available through one of those media, particularly if you wanted to see the latest series or movies. In some countries, we paid a TV license fee that partly paid for the government-funded free-to-air network. For those with access to cable or satellite TV, we paid a monthly fee for a smorgasbord of channels, most of which we never watched.

Today, we still have those options, but an explosion of video-on-demand (VOD) options has occurred. Each of them requires a separate contract that users rarely read. So we have little knowledge of what these services have contracted to deliver or how they will use our data. But we have an amazing range of content available if we want.

At least most are monthly services that we can cancel at short notice unless you take advantage of the price reduction for subscribing for a year or longer.

How many as-a-service products are you choosing?

Translate this to an organisation that is increasingly using as-a-service products. Choosing a service to use has much more complex requirements, there are more options available and happens more frequently.

If we think the domestic landscape for video is complicated, the options for as-a-service are significantly more fragmented. The most frequently used approach to selecting VOD services is to choose those that offer the content you want to see.

Choosing the appropriate as-a-service offering is much harder as the functional, security, integration and pricing requirements are much more complex than wanting to see a particular movie or drama series.

Suppliers of these services do not make it easy to understand what you get for your money. They decide to bundle or unbundle functions depending on a mix of factors to differentiate themselves for customers. This makes value and price comparisons difficult.

For example, there is a myriad of CRM suppliers out there offering a complex matrix of pricing and functionality options. And in each case, there is often an ecosystem of suppliers providing different pieces of functionality. No one provider delivers all the functionality that we desire.

Organisations wanting to choose as-a-service products need to be very clear on which requirements are the most important to them, and how well each supplier meets those specific needs. They cannot afford to be distracted by less valuable features.

It is extremely unlikely that one tech vendor will be able to provide all the desired features. Increasingly, other vendors will supplement the core functionality with niche features. So the selection has to take into account the ecosystem around the core as-a-service functionality.

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Recommendations

Tech buyers should make sure they have a clear definition and priority for the features that they require for each purpose. Added to this they need to be very clear what they want in a single package, and what they are prepared to purchase from a wider ecosystem. Both need to be part of the selection criteria. 

Once implemented, changing as-a-service products is a much more difficult proposition than switching VOD providers.

Tech vendors need to stay away from confusion marketing to make it possible for buyers to understand what they are getting. They need to help customers gain a clear understanding of any ecosystem they participate in, and what this means for the buyer.

The consequences of getting this choice right are dramatically more important than choosing the VOD provider with the programming you want.

And we know how difficult that choice has become.

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Ecosystm RNx: Top 10 Global Cloud Vendor Rankings

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In this first edition of Ecosystm RNx we rank the Top 10 Global Cloud Vendors.

Ecosystm RNx is an objective vendor ranking based on in-depth and quantified ratings from technology decision-makers on the Ecosystm platform.  

If you are an technology user, this Cloud Vendor ranking will help you evaluate your buying decisions based on key evaluation ratings by your peers across a number of key metrics and benchmarks, including customer experience.

If you are a Cloud Vendor, this is an opportunity to understand how your customers rate you on capabilities and their overall customer experience.

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Intelligent ‘postcards’ from the Edge: Machine learning model usage

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Organisations have found that it is not always desirable to send data to the cloud due to concerns about latency, connectivity, energy, privacy and security. So why not create learning processes at the Edge? 

What challenges does IoT bring?

Sensors are now generating such an increasing volume of data that it is not practical that all of it be sent to the cloud for processing. From a data privacy perspective, some sensor data is sensitive and sending data and images to the cloud will be subject to privacy and security constraints.

Regardless of the speed of communications, there will always be a demand for more data from more sensors – along with more security checks and higher levels of encryption – causing the potential for communication bottlenecks.

As the network hardware itself consumes power, sending a constant stream of data to the cloud can be taxing for sensor devices. The lag caused by the roundtrip to the cloud can be prohibitive in applications that require real-time response inputs.

Machine learning (ML) at the Edge should be prioritised to leverage that constant flow of data and address the requirement for real-time responses based on that data. This should be aided by both new types of ML algorithms and by visual processing units (VPUs) being added to the network.

By leveraging ML on Edge networks in production facilities, for example, companies can look out for potential warning signs and do scheduled maintenance to avoid any nasty surprises. Remember many sensors are linked intrinsically to public safety concerns such as water processing, supply of gas or oil, and public transportation such as metros or trains.

Ecosystm research shows that deploying IoT has its set of challenges (Figure 1) – many of these challenges can be mitigated by processing data at the Edge.

Challenges of IoT Deployment

Predictive analytics is a fundamental value proposition for IoT, where responding faster to issues or taking action before issues occur, is key to a high return on investment. So, using edge computing for machine learning located within or close to the point of data gathering can in some cases be a more practical or socially beneficial approach. 

In IoT the role of an edge computer is to pre-process data and act before the data is passed on to the main server. This allows a faster, low latency response and minimal traffic between the cloud server processing and the Edge. However, a better understanding of the benefits of edge computing is required if it has to be beneficial for a number of outcomes.

Perception on Edge Analytics in IoT Users
AI Research and Reports

If we can get machine learning happening in the field, at the Edge, then we reduce the time lag and also create an extra trusted layer in unmanned production or automated utilities situations. This can create more trusted environments in terms of possible threats to public services.

What kind of examples of machine learning in the field can we see?

Healthcare

Health systems can improve hospital patient flow through machine learning (ML) at the Edge. ML offers predictive models to assist decision-makers with complex hospital patient flow information based on near real-time data.

For example, an academic medical centre created an ML pipeline that leveraged all its data – patient administration, EHR and clinical and claims data – to create learnings that could predict length of stay, emergency department (ED) arrival models, ED admissions, aggregate discharges, and total bed census. These predictive models proved effective as the medical centre reduced patient wait times and staff overtime and was able to demonstrate improved patient outcomes.  And for a medical centre that use sensors to monitor patients and gather requests for medicine or assistance, Edge processing means keeping private healthcare data in-house rather than sending it off to cloud servers.

Retail

A retail store could use numerous cameras for self-checkout and inventory management and to monitor foot traffic. Such specific interaction details could slow down a network and can be replaced by an on-site Edge server with lower latency and a lower total cost. This is useful for standalone grocery pop-up sites such as in Sweden and Germany.

In Retail, k-nearest neighbours is often used in ML for abnormal activity analysis – this learning algorithm can also be used for visual pattern recognition used as part of retailers’ loss prevention tactics.

Summary

Working with the data locally on the Edge, creates reduced latency, reduced cloud usage and costs, independence from a network connection, more secure data, and increased data privacy.

Cloud and Edge computing that uses machine learning can together provide the best of both worlds: decentralised local storage, processing and reaction, and then uploading to the cloud, enabling additional insights, data backups (redundancy), and remote access.

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Whitepaper- Delivering exceptional customer experiences in an OJK-compliant environment
Whitepaper – Delivering exceptional customer experiences in an OJK-compliant environment

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The Financial Services industry (FSI) in Indonesia is increasingly investing in digital. This is driven by both user demand and governmental incentives. However, organisations can be at very different stages of their digital maturity. Some organisations are in the early transitional stages while others are investing to enhance capabilities, they started investments in long ago. An understanding of the digital maturity of a typical financial services organisation in Indonesia gives their industry peers an opportunity to benchmark their technology and digital roadmap.

FSI in Indonesia is intensely competitive with a large number of players. There is competition from the fintech start-up community that is increasing market size and has driven a growing reliance on digital. Why is creating an exceptional customer experience a key differentiator in this competitive market? What are the impacts of OJK regulations on customer data management? How can a financial organisation create a single customer view across multiple channels and deliver personalised marketing initiatives?

Read this whitepaper to find out how digital-savvy customers are driving transformation in the financial services industry in Indonesia including:

  • The key business priorities for 2021
  • The shift in engagement strategies and channels
  • The dependence on technology to support their marketing strategy

Click below to download the whitepaper

Whitepaper-Delivering Exceptional Customer Experiences In An OJK-Compliant Environment

(Clicking on this link will take you to the Sitecore website where you can download the whitepaper)


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The Winning Formula - Achieving Success with AI
The Winning Formula – Achieving Success with AI

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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).

Keys to Unlock AI Nirvana - Enabled by Upskilling

#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.

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#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

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