“SaaS is dead!” – this paraphrased comment from Satya Nadella during an interview was taken wildly out of context. It reminded me of those 2014-2017 industry reports predicting that voice commerce would be a USD 500B market by 2025, or that self-driving cars would be everywhere by 2027 – just two years from now. As it turns out, people still prefer ordering groceries themselves rather than relying on smart speakers connected to IoT fridges. And those early chatbot pop-ups? More annoying than intelligent. As for autonomous cars, we might still be better drivers – though that’s starting to shift. But I digress.
Back to SaaS. A global industry with over 30,000 companies, mostly in the US, now finds itself under the shadow of the latest buzz: AI agents (still software, not humanoid robots). These agents – programs built on top of LLMs – take actions within set parameters, showing a degree of autonomy.
But to make AI agents enterprise-ready, we’ll need to rethink access control, ethics, authentication, and compliance. So far, they’ve mostly tackled low-value, repetitive tasks. And despite the hype, we’re still some distance from real, meaningful impact.
Predictions Are Fine – But Best Taken with a Pinch of Salt
Salesforce, the world’s largest SaaS company, has played its part in driving this shift — alongside, of course, Microsoft. Microsoft’s aggressive push into AI, with a massive USD 80 billion CapEx on AI data centres and a flurry of product launches like Copilot chat, may just be the beginning. Microsoft even describes Copilot as the “UI for AI.” Despite its size, Salesforce has moved quickly, rolling out Agentforce, its enterprise AI agent suite. While still early days, Salesforce is leveraging its formidable sales and marketing muscle to push the AI agent narrative — while upselling Agentforce to thousands of existing customers.
For context: Salesforce, the largest player, generates around USD 35 billion in annual revenue. Across the industry, there are roughly 300 SaaS unicorns – but even combined, the entire global SaaS sector brings in only about USD 300B a year. Beyond big names like Salesforce, HubSpot, and Atlassian, the market is dominated by a long tail of smaller, vertical SaaS firms that serve niche sub-industries and specialised use cases.
Today, about 70% of enterprise software is delivered through SaaS. But beyond the top 30 vendors, the landscape is highly fragmented — and arguably primed for disruption by AI agents that can automate and streamline more bespoke, industry-specific workflows.
But the thousands of smaller SaaS firms haven’t all moved as quickly as Salesforce has. Most will likely stick to announcements and incremental upgrades rather than radical deployments – especially as enterprises tread carefully while every vendor suddenly becomes “AI-inside”, the new “Intel-inside.”
AI Washing, Hype, and a Flood of Start-Ups
Since ChatGPT’s historic launch in late 2022, the GenAI AI hype curve hasn’t slowed. In SaaS, the early impact has largely been “AI washing”: companies hastily sprinkling “Generative AI” across their websites, collateral, and social feeds while snapping up .ai domains at premium prices.
Meanwhile, over 3,000 AI-first start-ups have emerged, building wrappers around foundational models to deliver bespoke inferences and niche services. Thanks to ongoing hype, some of these are flush with venture capital – even without revenue. At the same time, traditional SaaS firms face tough investor scrutiny over profitability and free cash flow. The contrast couldn’t be starker.
Yet, both the AI upstarts and the older SaaS players face similar go-to-market challenges. Early product-market fit (PMF) is no guarantee of real traction, especially as most enterprise clients are still experimenting, rather than committing, to AI agents. That’s prompting start-ups to build agentic layers atop inference services to bridge the gap.
The Real Race: Embedding AI with Real Impact
It’s too early to call winners. Whether it’s cloud-first SaaS firms evolving into “AI-inside” platforms, or AI agent start-ups challenging the status quo, success will hinge on more than just AI. It will come down to who can combine proprietary data, compelling use cases, and proven workflow impact.
McKinsey sees AI agents serving two broad patterns: the “factory” model for predictable, routine tasks, and the “artisan” model for augmenting more strategic, executive functions. Another compelling narrative does not make the distinction between the earlier crop of cloud-first and the recent crop of AI-first companies. They see this as a natural progression of the SaaS business model, with VSaaS or “vertical Saas with AI-inside” becoming the broader industry.
I’d argue the original cloud-first SaaS firms might actually be better positioned. Their biggest moat? Existing customer relationships. AI start-ups haven’t yet faced the reality of renewing their first multi-year enterprise contracts. That’s where theory meets enterprise buying behaviour – and where this battle will get interesting.
The Playbook for SaaS Winners in the Age of AI Agents
The SaaS companies that will thrive over the next few years will, in my view, focus on these key elements:
- Leverage Early Clients as a Moat. Invest in the success of your first enterprise clients, ensuring they extract real, sustainable value before chasing new logos. Build enough trust, and you could co-create AI agents trained on their proprietary data, enhancing your core product in the process. Snowflake, with its broad enterprise footprint, has a head start here, but start-ups like Collectivei and Beam are targeting similar use cases, while platforms like Letta help companies deploy their own agents.
- Codify the Use Case. Build products that go deep – not broad. Focus on specific use cases or verticals that a horizontal SaaS company is unlikely to prioritise. Eventually, most enterprise users will care less about which foundation model powers your tool and more about the outcomes.
- Operate with a GTM-First Mindset. Many SaaS firms struggle with margins because of high sales and marketing costs, often wavering between sales-led and product-led growth without a clear go-to-market (GTM) plan. AI start-ups, too, are learning that pure product-led growth doesn’t scale in crowded markets and often pivot to sales-led motions too late. Companies like Chargeflow show why a GTM-first approach is key to building real traction and a growth flywheel.
- Rethink Bundling. Bundling has long been a SaaS pricing play – slicing features into tiers. AI-first start-ups are poised to disrupt this. The shift will be towards outcome-based pricing rather than packaging features. Winners will iterate constantly, tuning bundles to different user cohorts and business goals.
- Charge for Success, Not Seats. AI’s biggest impact may be on pricing. Traditional seat-based models will give way to success or outcome-based pricing, with minimal or no set-up fees. Professional services for customisation will still have value, especially where products align deeply with client workflows and outcomes.
- Prioritise Renewal Over Acquisition. Many AI-first start-ups focus on acquiring logos but underestimate that enterprises are still experimenting – switching costs are low, and loyalty is thin. Building for retention, renewal, and upselling will separate winners from the rest. Focus on churn early.
The Next Chapter in Enterprise Automation
Automation has always been a continuum. Remember when cloud vs. on-prem dominated enterprise debates? Or when RPA was expected to replace most workflows as we knew them? The reality was more measured, and we’re seeing a similar pattern with AI today. Enterprises will first focus on making AI co-pilots work safely, reliably, and effectively before they’re ready to hand over the keys to AI agents running workflows on autopilot. This shift won’t happen overnight.
We’re already seeing early winners capable of negotiating this shift, on both sides: established SaaS giants adapting and AI-native start-ups rising. But make no mistake, this will be a long, hard-fought race. Sustained value capture will demand more than just better tech; it will require a fundamental shift in mindset, go-to-market strategies, and sales motions.
Don’t be surprised if the acronym flips along the way – with Software-as-a-Service giving way to Service-as-Software, as AI agents begin to run entire business processes end to end.
Through it all, one principle will remain timeless: an obsession with customer success – whether the agent is human or machine.
The promise of AI agents – intelligent programs or systems that autonomously perform tasks on behalf of people or systems – is enormous. These systems will augment and replace human workers, offering intelligence far beyond the simple RPA (Robotic Process Automation) bots that have become commonplace in recent years.
RPA and AI Agents both automate tasks but differ in scope, flexibility, and intelligence:
7 Lessons for AI Agents: Insights from RPA Deployments
However, in many ways, RPA and AI agents are similar – they both address similar challenges, albeit with different levels of automation and complexity. RPA adoption has shown that uncontrolled deployment leads to chaos, requiring a balance of governance, standardisation, and ongoing monitoring. The same principles apply to AI agent management, but with greater complexity due to AI’s dynamic and learning-based nature.
By learning from RPA’s mistakes, organisations can ensure AI agents deliver sustainable value, remain secure, and operate efficiently within a governed and well-managed environment.
#1 Controlling Sprawl with Centralised Governance
A key lesson from RPA adoption is that many organisations deployed RPA bots without a clear strategy, resulting in uncontrolled sprawl, duplicate bots, and fragmented automation efforts. This lack of oversight led to the rise of shadow IT practices, where business units created their own bots without proper IT involvement, further complicating the automation landscape and reducing overall effectiveness.
Application to AI Agents:
- Establish centralised governance early, ensuring alignment between IT and business units.
- Implement AI agent registries to track deployments, functions, and ownership.
- Enforce consistent policies for AI deployment, access, and version control.
#2 Standardising Development and Deployment
Bot development varied across teams, with different toolsets being used by different departments. This often led to poorly documented scripts, inconsistent programming standards, and difficulties in maintaining bots. Additionally, rework and inefficiencies arose as teams developed redundant bots, further complicating the automation process and reducing overall effectiveness.
Application to AI Agents:
- Standardise frameworks for AI agent development (e.g., predefined APIs, templates, and design patterns).
- Use shared models and foundational capabilities instead of building AI agents from scratch for each use case.
- Implement code repositories and CI/CD pipelines for AI agents to ensure consistency and controlled updates.
#3 Balancing Citizen Development with IT Control
Business users, or citizen developers, created RPA bots without adhering to IT best practices, resulting in security risks, inefficiencies, and technical debt. As a result, IT teams faced challenges in tracking and supporting business-driven automation efforts, leading to a lack of oversight and increased complexity in maintaining these bots.
Application to AI Agents:
- Empower business users to build and customise AI agents but within controlled environments (e.g., low-code/no-code platforms with governance layers).
- Implement AI sandboxes where experimentation is allowed but requires approval before production deployment.
- Establish clear roles and responsibilities between IT, AI governance teams, and business users.
#4 Proactive Monitoring and Maintenance
Organisations often underestimated the effort required to maintain RPA bots, resulting in failures when process changes, system updates, or API modifications occurred. As a result, bots frequently stopped working without warning, disrupting business processes and leading to unanticipated downtime and inefficiencies. This lack of ongoing maintenance and adaptation to evolving systems contributed to significant operational disruptions.
Application to AI Agents:
- Implement continuous monitoring and logging for AI agent activities and outputs.
- Develop automated retraining and feedback loops for AI models to prevent performance degradation.
- Create AI observability dashboards to track usage, drift, errors, and security incidents.
#5 Security, Compliance, and Ethical Considerations
Insufficient security measures led to data leaks and access control issues, with bots operating under overly permissive settings. Also, a lack of proactive compliance planning resulted in serious regulatory concerns, particularly within industries subject to stringent oversight, highlighting the critical need for integrating security and compliance considerations from the outset of automation deployments.
Application to AI Agents:
- Enforce role-based access control (RBAC) and least privilege access to ensure secure and controlled usage.
- Integrate explainability and auditability features to comply with regulations like GDPR and emerging AI legislation.
- Develop an AI ethics framework to address bias, ensure decision-making transparency, and uphold accountability.
#6 Cost Management and ROI Measurement
Initial excitement led to unchecked RPA investments, but many organisations struggled to measure the ROI of bots. As a result, some RPA bots became cost centres, with high maintenance costs outweighing the benefits they initially provided. This lack of clear ROI often hindered organisations from realising the full potential of their automation efforts.
Application to AI Agents:
- Define success metrics for AI agents upfront, tracking impact on productivity, cost savings, and user experience.
- Use AI workload optimisation tools to manage computing costs and avoid overconsumption of resources.
- Regularly review AI agents’ utility and retire underperforming ones to avoid AI bloat.
#7 Human Oversight and Hybrid Workflows
The assumption that bots could fully replace humans led to failures in situations where exceptions, judgment, or complex decision-making were necessary. Bots struggled to handle scenarios that required nuanced thinking or flexibility, often leading to errors or inefficiencies. The most successful implementations, however, blended human and bot collaboration, leveraging the strengths of both to optimise processes and ensure that tasks were handled effectively and accurately.
Application to AI Agents:
- Integrate AI agents into human-in-the-loop (HITL) systems, allowing humans to provide oversight and validate critical decisions.
- Establish AI escalation paths for situations where agents encounter ambiguity or ethical concerns.
- Design AI agents to augment human capabilities, rather than fully replace roles.
The lessons learned from RPA’s journey provide valuable insights for navigating the complexities of AI agent deployment. By addressing governance, standardisation, and ethical considerations, organisations
can shift from reactive problem-solving to a more strategic approach, ensuring AI tools deliver value while operating within a responsible, secure, and efficient framework.
We spoke about what public sector agencies should consider when building citizen-centric services. Integrating technology into organisational processes requires a similarly strategic approach that considers immediate needs, emerging enablers, and futuristic innovations.
Here is a comprehensive look at what public sector organisations should consider when integrating technology into processes.
1. Process Essentials: Laying the Groundwork
The immediate view focuses on deploying technologies that are widely adopted and essential for current digital service provision. These foundational technologies serve as the backbone for enhancing process efficiency.
- Code. At the most basic level, the foundation is built on code – the programming languages and frameworks used to create digital services. This includes technologies like HTML, CSS, JavaScript, Java, Python, etc. A typical approach is to have a front-end web layer for the user interface and a back-end application layer for processing.
- Monolithic ERP. These systems are also crucial, especially in the early stages. These integrated software suites help manage core functions like customer management and document handling. They provide comprehensive, pre-built solutions that can be customised to specific needs. ERPs enable organisations to effectively manage complex processes from the start.
2. Emerging Catalysts: Accelerating Processes
As organisations establish foundational technologies, they should look towards second-generation enablers. Although less mature, these technologies offer emerging digital opportunities, and can significantly enhance service differentiation, through improved processes.
- PaaS. As digital services mature, organisations can leverage platform-as-a-service (PaaS) solutions hosted in the cloud. PaaS provides greater scalability, flexibility, and reduced infrastructure management overhead compared to custom development approaches. Adopting a microservices architecture on PaaS allows for developing independent components that can be updated independently, promoting continuous improvement. This modern, modular approach is highly efficient.
- Low Code/ No Code. LC/NC platforms further simplify application development by providing intuitive, visual tools that don’t require extensive coding expertise. They build on PaaS capabilities while minimising the need for deep technical skills. These environments also facilitate collaboration by enabling partners and third-parties to easily create custom solutions that integrate with the organisation’s systems. This spurs innovation through an ecosystem of complementary apps and services.
3. Future-Forward Capabilities: Next-Gen Processes
The futuristic view focuses on forward-looking technologies that address long-term roadblocks and offer transformative potential. These technologies are currently speculative but hold the promise of significantly reshaping the market.
- Complex RPA and ML. Robotic process automation (RPA) and machine learning take technological maturity to the next level by automating routine tasks and optimising decision-making through intelligent algorithms. The integration of RPA with machine learning goes beyond simple automation to enable more complex, data-driven decision processes across the workflow. Analysts predict that by 2025, up to 50% of work could be automated this way, drastically improving efficiency.
- Enterprise-Wide Microservices Architecture. An enterprise-wide microservices architecture represents an advanced approach suitable for collaboration between agencies, technical service providers, and partners. Each microservice is designed to be independently deployable, testable, and focused on specific capabilities. This decentralised model allows services to be updated or replaced without disrupting the entire system, enhancing resilience. On a PaaS platform, it enables an agile, scalable approach aligned with modern e-government needs.
- Industry Cloud. The Industry Cloud is essentially a highly configurable PaaS solution, designed to meet the specific needs of not just one government agency or jurisdiction, but with adaptability for broader use.
Ecosystm Opinion
A comprehensive roadmap should outline how to build upon current process foundations with emerging catalysts like cloud platforms and low-code development, while actively preparing for future-forward capabilities around automation, microservices architectures, and industry cloud solutions.
By taking a long-term, systematic approach to integrating technology at every stage of the process lifecycle, agencies can cultivate an adaptable digital process ecosystem that continually evolves in lockstep with technological innovation. The goal is to foster processes that don’t just endure disruption, but fundamentally improve because of it – cementing organisational resilience and agility for decades to come.
The Banking, Financial Services, and Insurance (BFSI) industry, known for its cautious stance on technology, is swiftly undergoing a transformational modernisation journey. Areas such as digital customer experiences, automated fraud detection, and real-time risk assessment are all part of a technology-led roadmap. This shift is transforming the cybersecurity stance of BFSI organisations, which have conventionally favoured centralising everything within a data centre behind a firewall.
Ecosystm research finds that 75% of BFSI technology leaders believe that a data breach is inevitable. This requires taking a new cyber approach to detect threats early, reduce the impact of an attack, and avoid lateral movement across the network.
BFSI organisations will boost investments in two main areas over the next year: updating infrastructure and software, and exploring innovative domains like digital workplaces and automation. Cybersecurity investments are crucial in both of these areas.
As a regulated industry, breaches come with significant cost implications, underscoring the need to prioritise cybersecurity. BFSI cybersecurity and risk teams need to constantly reassess their strategies for safeguarding data and fulfilling compliance obligations, as they explore ways to facilitate new services for customers, partners, and employees.
The primary concerns of BFSI CISOs can be categorised into two distinct groups:
- Expanding Technology Use. This includes the proliferation of applications and devices, as well as data access beyond the network perimeter.
- Employee-Related Vulnerabilities. This involves responses to phishing and malware attempts, as well as intentional and unintentional misuse of technology.
Vulnerabilities Arising from Employee Actions
Security vulnerabilities arising from employee actions and unawareness represent a significant and ongoing concern for businesses of all sizes and industries – the risks are just much bigger for BFSI. These vulnerabilities can lead to data breaches, financial losses, damage to reputation, and legal ramifications. A multi-pronged approach is needed that combines technology, training, policies, and a culture of security consciousness.
Training and Culture. BFSI organisations prioritise comprehensive training and awareness programs, educating employees about common threats like phishing and best practices for safeguarding sensitive data. While these programs are often ongoing and adaptable to new threats, they can sometimes become mere compliance checklists, raising questions about their true effectiveness. Conducting simulated phishing attacks and security quizzes to assess employee awareness and identify areas where further training is required, can be effective.
To truly educate employees on risks, it’s essential to move beyond compliance and build a cybersecurity culture throughout the organisation. This can involve setting organisation-wide security KPIs that cascade from the CEO down to every employee, promoting accountability and transparency. Creating an environment where employees feel comfortable reporting security concerns is critical for early threat detection and mitigation.
Policies. Clear security policies and enforcement are essential for ensuring that employees understand their roles within the broader security framework, including responsibilities on strong password use, secure data handling, and prompt incident reporting. Implementing the principle of least privilege, which restricts access based on specific roles, mitigates potential harm from insider threats and inadvertent data exposure. Policies should evolve through routine security audits, including technical assessments and evaluations of employee protocol adherence, which will help organisations with a swifter identification of vulnerabilities and to take the necessary corrective actions.
However, despite the best efforts, breaches do happen – and this is where a well-defined incident response plan, that is regularly tested and updated, is crucial to minimise the damage. This requires every employee to know their roles and responsibilities during a security incident.
Tech Expansion Leading to Cyber Complexity
Cloud. Initially hesitant to transition essential workloads to the cloud, the BFSI industry has experienced a shift in perspective due to the rise of inventive SaaS-based Fintech tools and hybrid cloud solutions, that have created new impetus for change. This new distributed architecture requires a fresh look at cyber measures. Secure Access Service Edge (SASE) providers are integrating a range of cloud-delivered safeguards, such as FWaaS, CASB, and ZTNA with SD-WAN to ensure organisations can securely access the cloud without compromising on performance.
Data & AI. Data holds paramount importance in the BFSI industry for informed decision-making, personalised customer experiences, risk assessment, fraud prevention, and regulatory compliance. AI applications are being used to tailor products and services, optimise operational efficiency, and stay competitive in an evolving market. As part of their technology modernisation efforts, 47% of BFSI institutions are refining their data and AI strategies. They also acknowledge the challenges associated – and satisfying risk, regulatory, and compliance requirements is one of the biggest challenges facing BFSI organisations in the AI deployments.
The rush to experiment with Generative AI and foundation models to assist customers and employees is only heightening these concerns. There is an urgent need for policies around the use of these emerging technologies. Initiatives such as the Monetary Authority of Singapore’s Veritas that aim to enable financial institutions to evaluate their AI and data analytics solutions against the principles of fairness, ethics, accountability, and transparency (FEAT) are expected to provide the much-needed guidance to the industry.
Digital Workplace. As with other industries with a high percentage of knowledge workers, BFSI organisations are grappling with granting remote access to staff. Cloud-based collaboration and Fintech tools, BYOD policies, and sensitive data traversing home networks are all creating new challenges for cyber teams. Modern approaches, such as zero trust network access, privilege management, and network segmentation are necessary to ensure workers can seamlessly but securely perform their roles remotely.
Looking Beyond Technology: Evaluating the Adequacy of Compliance-Centric Cyber Strategies
The BFSI industry stands among the most rigorously regulated industries, with scrutiny intensifying following every collapse or notable breach. Cyber and data protection teams shoulder the responsibility of understanding the implications of and adhering to emerging data protection regulations in areas such as GDPR, PCI-DSS, SOC 2, and PSD2. Automating compliance procedures emerges as a compelling solution to streamline processes, mitigate risks, and curtail expenses. Technologies such as robotic process automation (RPA), low-code development, and continuous compliance monitoring are gaining prominence.
The adoption of AI to enhance security is still emerging but will accelerate rapidly. Ecosystm research shows that within the next two years, nearly 70% of BFSI organisations will have invested in SecOps. AI can help Security Operations Centres (SOCs) prioritise alerts and respond to threats faster than could be performed manually. Additionally, the expanding variety of network endpoints, including customer devices, ATMs, and tools used by frontline employees, can embrace AI-enhanced protection without introducing additional onboarding friction.
However, there is a need for BFSI organisations to look beyond compliance checklists to a more holistic cyber approach that can prioritise cyber measures continually based on the risk to the organisations. And this is one of the biggest challenges that BFSI CISOs face. Ecosystm research finds that 72% of cyber and technology leaders in the industry feel that there is limited understanding of cyber risk and governance in their organisations.
In fact, BFSI organisations must look at the interconnectedness of an intelligence-led and risk-based strategy. Thorough risk assessments let organisations prioritise vulnerability mitigation effectively. This targeted approach optimises security initiatives by focusing on high-risk areas, reducing security debt. To adapt to evolving threats, intelligence should inform risk assessment. Intelligence-led strategies empower cybersecurity leaders with real-time threat insights for proactive measures, actively tackling emerging threats and vulnerabilities – and definitely moving beyond compliance-focused strategies.
Ecosystm launches the Trending-Vendor series showcasing the Top 5 disruptive vendors that are shaking up their market segment. Our first Trending-Vendor RNx is focused on Robotic Process Automation (RPA) – Ecosystm research shows that process automation is the biggest driver for tech adoption, with a staggering 127% investment growth in 2021. The Ecosystm RNx – Top 5 Trending-Vendor for RPA evaluates Automation vendors based on in-depth, quantified ratings from technology decision-makers on the Ecosystm platform.
If you are an End User and looking to automate your back-end or customer processes, 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, integration capabilities, and strategy.
If you are an RPA vendor, you operate in a competitive work with several enterprise vendors vying for a larger share of the pie – this is an opportunity to understand how your customers rate you on capabilities and their overall customer experience.
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.”
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.
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.
ServiceNow announced their intention to acquire robotic process automation (RPA) provider, Intellibot, for an undisclosed sum. Intellibot is a significant tier 2 player in the RPA market, that is rapidly consolidating into the hands of the big three – UiPath, Automation Everywhere, and Blue Prism – and other acquisition-hungry software providers. This is unlikely to be the last RPA acquisition that we see this year with smaller players looking to either go niche or sell out while the market is hot.
Expanding AI/Automation Capabilities
Intellibot is the latest in a string of purchases by ServiceNow that reveals their intention to embed AI and machine learning into offerings. In 2020, they acquired Loom Systems, Passage AI (both January), Sweagle (June), and Element AI (November) in addition to Attivio in 2019. These acquisitions were integrated into the latest version of their Now Platform, code-named Quebec, which was launched earlier this month. As a result, Predictive AIOps and AI Search were newly added to the platform while the low-code tools were expanded upon and became Creator Workflows. This means ServiceNow now offers four primary solutions – IT Workflows, Employee Workflows, Customer Workflows, and Creator Workflows – demonstrating the importance they are placing on low-code and RPA.
ServiceNow was quick to remind the market that although they will be able to offer RPA functionality natively once Intellibot is integrated into their platform, they are still willing to work with competitors. They specifically highlighted that they would continue partnering with UiPath, Automation Anywhere, and Blue Prism, suggesting they plan to use RPA as a complementary technology to their current offerings rather than going head-to-head with the Big Three. Only a month ago, UiPath announced deeper integration with ServiceNow, by expanding automation capabilities for Test Management 2.0 and Agile Development projects.
Expansion in India
The acquisition of Intellibot, based in Hyderabad, is part of ServiceNow’s expansion strategy in India – one of their fastest growing markets. The country is already home to their largest R&D centre outside of the US and they intend to launch a couple of data centres there by March 2022. The company plans to double their local staff levels by 2024, having already tripled the number of employees there in the last two years. The expansion in India means they can increasingly offer services from there to global customers.
Market Consolidation Accelerates
In the Ecosystm Predicts: The Top 5 AI & AUTOMATION Trends for 2021, Ecosystm had talked about technology vendors adding RPA functionality either organically or through acquisitions, this year.
“Buyers will find that many of the automation capabilities that they currently purchase separately will increasingly be integrated in their enterprise applications. This will resolve integration challenges and will be more cost-effective.”
ServiceNow’s purchase is one of several recent examples of low-code vendors acquiring their way into the RPA space. Last year, Appian acquired Novayre Solutions for their Jidoka product and Microsoft snapped up Softomotive. Speculation continues to build that Salesforce could also be assessing RPA targets. Considering RPA market leader, UiPath recently announced that their Series F funding round values the company at USD 35 billion, there is pressure on acquirers to gobble up the remaining smaller players before they are all gone or become prohibitively expensive.
The cloud hyperscalers are also likely to play a growing role in the RPA market over the next year. Microsoft and IBM have already entered the market, coming from the angle of office productivity and business process management (BPM), respectively. Google announced just last week that they will work closely with Automation Anywhere to integrate RPA into their cloud offerings, such as Apigee, AppSheet, and AI Platform. More interestingly, they plan to co-develop new solutions, which might for now satisfy Google’s appetite for RPA rather than requiring an acquisition.
Here are some of the trends to watch for RPA, AI and Automation in 2021. Signup for Free to download Ecosystm’s Top 5 AI & Automation Trends Report.
Authored by Alea Fairchild and Audrey William
There is a lot of hope on AI and automation to create intellectual wealth, efficiency, and support for some level of process stability. After all, can’t we just ask Siri or Alexa and get answers so we can make a decision and carry on?
Automation has been touted as the wonder formula for workplace process optimisation. In reality it’s not the quick fix that many business leaders desire. But we keep raising the bar on expectations from automation. Investments in voice technologies, intelligent assistants, augmented reality and touchscreens are changing customer experience (Figure 1). Chatbots are ubiquitous, and everything has the potential to be personalised. But will they solve our problems?
100 percent automation is not effective
Let’s first consider using automation to replace face-to-face interactions. There was a time when people were raving about the check-in experience at some of the hotels in Japan where robots and automated systems would take care of the check-in, in-stay and check-out processes. Sounds simple and good? Till 2019, if you checked into the Henn-na Hotel in Japan, you would be served and taken care of by 243 robots. It was viewed by many as a template for what a fully automated hotel could look like in the future.
The hotel had an in-room voice assistant called Churi. It could cope with basic commands, such as turning the lights on and off, but it was found to be deficient when guests started asking questions about places to visit or other more sophisticated queries. It was not surprising that the hotel decided to retire their robots. In the end it created more work for the hotel staff on-site.
People love the personal touch when they are in a hotel; and talking to someone at the front desk, requesting assistance from hotel staff, or even just a short chat over breakfast are some of the small nuances of why the emotional connection matters. Many quarantine hotels today use robots for food delivery, but the hotel staff is still widely available for questions. That automation is good, but you need the human intervention. So, getting the balance right is key.
Empathy plays a big role in delivering great Customer Experience
Similarly, there was a time when many industry observers and technology providers said that a contact centre will be fully automated, reducing the number of agents. While technologies such as Conversational AI have come along where you can now automate common or repetitive questions and with higher accuracy levels, the human agent still plays a critical role in answering the more complex queries. When the customer has a complicated question or request, then they will WANT to speak to an agent.
When it reaches a point where the conversation with the chatbot starts getting complicated and the customers need more help there should be the option – within the app, website or any other channel – to escalate the call seamlessly to a human agent. Sometimes, a chat is where the good experience happens – the emotional side of the conversation, the laughter, the detailed explanation. This human touch cannot be replaced by machines. Disgruntled customers are happier when an agent shows empathy. Front line staff and human agents act as the face of a company’s brand. Complete automation will not allow the individual to understand the culture of the company. These can be attained through conversations.
Humans as supervisors for AI – The New Workplace
Empathy, intuitiveness, and creativity are all human elements in the intelligence equation. Workers in the future will need to make their niche in a fluid and unpredictable environment; and translating data into action in a non-replicable way is one of the values of human input. The essence of engineering is the capacity to design around human limitations. This requires an understanding of how humans behave and what they want. We call that empathy. It is the difference between the engineer who designs a product, and the engineer who delivers a solution. We don’t teach our computer scientists and engineering students a formula for empathy. But we do try to teach them respect for both the people and the process.
For efficiency, we turn to automation of processes, such as RPA. This is designed to try to eradicate human error and assist us in doing our job better, faster and at a lower cost by automating routine processes. If we design it right, humans take the role of monitoring or supervisory controlling, rather than active participation.
At present, AI is not seen as a replacement for our ingenuity and knowledge, but as a support tool. The value in AI is in understanding and translating human preferences. Humans-in-the-loop AI system building puts humans in the decision loop. They also shift pressure away from building “perfect” algorithms. Having humans involved in the ethical norms of the decision allows the backstop of overly orchestrated algorithms.
That being said, the astute use of AI can deepen insights into what truly makes us human and can humanise experiences by setting a better tone and a more trusted engagement. Using things like sentiment analysis can de-escalate customer service encounters to regain customer loyalty.
The next transformational activity for renovating work is to advance interactions with customers by interpreting what they are asking for and humanising the experience of acquiring it which may include actually dealing with a human contact centre agent – decisions that are supported at the edge by automation, but at the core by a human being.
Implications
Ecosystm research shows that process automation will be a key priority for technology investments in 2021 (Figure 2).
With AI and automation, a priority in 2021, it will be important to keep these considerations in mind:
- Making empathy and the human connection the core of customer experiences will bring success.
- Rigorous, outcome-based testing will be required when process automation solutions are being evaluated. In areas where there are unsatisfactory results, human interactions cannot – and should not – be replaced.
- It may be easy to achieve 90% automation for dealing with common, repetitive questions and processes. But there should always be room for human intervention in the event of an issue – and it should be immediate and not 24 hours later!
- Employees can drive greater value by working alongside the chatbot, robot or machine.
Ecosystm Predicts: The Top 5 Customer Experience Trends for 2021
Download Ecosystm’s complimentary report detailing the top 5 customer experience trends for 2021 that your company should pay attention to along with tips on how to stay ahead of the curve.
The disruption that we faced in 2020 has created a new appetite for adoption of technology and digital in a shorter period. Crises often present opportunities – and the FinTech and Financial Services industries benefitted from the high adoption of digital financial services and eCommerce. In 2021, there will be several drivers to the transformation of the Financial Services industry – the rise of the gig economy will give access to a larger talent pool; the challenges of government aid disbursement will be mitigated through tech adoption; compliance will come sharply back into focus after a year of ad-hoc technology deployments; and social and environmental awareness will create a greater appetite for green financing. However, the overarching driver will be the heightened focus on the individual consumer (Figure 1).
2021 will finally see consumers at the core of the digital financial ecosystem.
Ecosystm Advisors Dr. Alea Fairchild, Amit Gupta and Dheeraj Chowdhry present the top 5 Ecosystm predictions for FinTech in 2021 – written in collaboration with the Singapore FinTech Festival. This is a summary of the predictions; the full report (including the implications) is available to download for free on the Ecosystm platform.
The Top 5 FinTech Trends for 2021
#1 The New Decade of the ‘Empowered’ Consumer Will Propel Green Finance and Sustainability Considerations Beyond Regulators and Corporates
We have seen multiple countries set regulations and implement Emissions Trading Systems (ETS) and 2021 will see Environmental, Social and Governance (ESG) considerations growing in importance in the investment decisions for asset managers and hedge funds. Efforts for ESG standards for risk measurement will benefit and support that effort.
The primary driver will not only be regulatory frameworks – rather it will be further propelled by consumer preferences. The increased interest in climate change, sustainable business investments and ESG metrics will be an integral part of the reaction of the society to assist in the global transition to a greener and more humane economy in the post-COVID era. Individuals and consumers will demand FinTech solutions that empower them to be more environmentally and socially responsible. The performance of companies on their ESG ratings will become a key consideration for consumers making investment decisions. We will see corporate focus on ESG become a mainstay as a result – driven by regulatory frameworks and the consumer’s desire to place significant important on ESG as an investment criterion.
#2 Consumers Will Truly Be ‘Front and Centre’ in Reshaping the Financial Services Digital Ecosystems
Consumers will also shape the market because of the way they exercise their choices when it comes to transactional finance. They will opt for more discrete solutions – like microfinance, micro-insurances, multiple digital wallets and so on. Even long-standing customers will no longer be completely loyal to their main financial institutions. This will in effect take away traditional business from established financial institutions. Digital transformation will need to go beyond just a digital Customer Experience and will go hand-in-hand with digital offerings driven by consumer choice.
As a result, we will see the emergence of stronger digital ecosystems and partnerships between traditional financial institutions and like-minded FinTechs. As an example, platforms such as the API Exchange (APIX) will get a significant boost and play a crucial role in this emerging collaborative ecosystem. APIX was launched by AFIN, a non-profit organisation established in 2018 by the ASEAN Bankers Association (ABA), International Finance Corporation (IFC), a member of the World Bank Group, and the Monetary Authority of Singapore (MAS). Such platforms will create a level playing field across all tiers of the Financial Services innovation ecosystem by allowing industry participants to Discover, Design and rapidly Deploy innovative digital solutions and offerings.
#3 APIfication of Banking Will Become Mainstream
2020 was the year when banks accepted FinTechs into their product and services offerings – 2021 will see FinTech more established and their technology offerings becoming more sophisticated and consumer-led. These cutting-edge apps will have financial institutions seeking to establish partnerships with them, licensing their technologies and leveraging them to benefit and expand their customer base. This is already being called the “APIficiation” of banking. There will be more emphasis on the partnerships with regulated licensed banking entities in 2021, to gain access to the underlying financial products and services for a seamless customer experience.
This will see the growth of financial institutions’ dependence on third-party developers that have access to – and knowledge of – the financial institutions’ business models and data. But this also gives them an opportunity to leverage the existent Fintech innovations especially for enhanced customer engagement capabilities (Prediction #2).
#4 AI & Automation Will Proliferate in Back-Office Operations
From quicker loan origination to heightened surveillance against fraud and money laundering, financial institutions will push their focus on back-office automation using machine learning, AI and RPA tools (Figure 3). This is not only to improve efficiency and lower risks, but to further enhance the customer experience. AI is already being rolled out in customer-facing operations, but banks will actively be consolidating and automating their mid and back-office procedures for efficiency and automation transition in the post COVID-19 environment. This includes using AI for automating credit operations, policy making and data audits and using RPA for reducing the introduction of errors in datasets and processes.
There is enormous economic pressure to deliver cost savings and reduce risks through the adoption of technology. Financial Services leaders believe that insights gathered from compliance should help other areas of the business, and this requires a completely different mindset. Given the manual and semi-automated nature of current AML compliance, human-only efforts slow down processing timelines and impact business productivity. KYC will leverage AI and real-time environmental data (current accounts, mortgage payment status) and integration of third-party data to make the knowledge richer and timelier in this adaptive economic environment. This will make lending risk assessment more relevant.
#5 Driven by Post Pandemic Recovery, Collaboration Will Shape FinTech Regulation
Travel corridors across border controls have started to push the boundaries. Just as countries develop new processes and policies based on shared learning from other countries, FinTech regulators will collaborate to harmonise regulations that are similar in nature. These collaborative regulators will accelerate FinTech proliferation and osmosis i.e. proliferation of FinTechs into geographies with lower digital adoption.
Data corridors between countries will be the other outcome of this collaboration of FinTech regulators. Sharing of data in a regulated environment will advance data science and machine learning to new heights assisting credit models, AI, and innovations in general. The resulting ‘borderless nature’ of FinTech and the acceleration of policy convergence across several previously siloed regulators will result in new digital innovations. These Trusted Data Corridors between economies will be further driven by the desire for progressive governments to boost the Digital Economy in order to help the post-pandemic recovery.
