At the Agentforce World Tour in Singapore, Salesforce presented their vision for Agentic AI – showcasing how they’re helping customers stay ahead of rapid technological change and unlock stronger business outcomes with speed, trust, and agility.
Ecosystm Advisors, Ullrich Loeffler, Sash Mukherjee, Achim Granzen, and Manish Goenka share their take on Salesforce’s announcements, demos, and messaging, highlighting what resonated, what stood out, and what it means for the future.
Click here to download “Agentforce World Tour: Highlights from Singapore” as a PDF.
What truly stood out in Salesforce’s messaging?
ULLRICH LOEFFLER, CEO & Co-Founder
What stood out at the Salesforce event was their pragmatic, integrated approach to scaling AI. They made it clear AI isn’t plug-and-play, emphasising the complexity and cost involved in what they call ‘self-plumbing’ AI – spanning infrastructure, data management, model development, governance, and application integration. Their answer is a unified platform that lowers costs, accelerates time to market, and reduces risk by removing the need to manage multiple disconnected tools. This seamless environment tackles the real challenge of building and running a layered AI stack.
Equally notable is their view of Agentic AI as a capability refined through iteration, not a sudden overhaul. By urging businesses to start with the right use cases for faster adoption, less disruption, and tangible impact, they show a realistic grasp of enterprise change.
Salesforce offers a clear, practical path to AI: simplifying complexity through integration and driving adoption with measured, value-focused steps.
SASH MUKHERJEE, VP Industry Insights
What truly stood out at the Salesforce event was their unwavering commitment to Trust. They understand that AI agents are only as reliable as the data they use, and they’ve built their platform to address this head-on. Salesforce emphasises that building trusted AI means more than just powerful models; it requires a secure and well-governed data foundation. They highlighted how their platform, with 25 years of embedded security, ensures data resilience, protects sensitive information during development and testing, and provides robust visibility into how AI interacts with your data.
A key assurance is their Trust Layer, a unique innovation that safeguards your data when interacting with AI models. This layer automatically masks sensitive data, ensures zero data retention by LLM providers, and detects harmful language. This means organisations can leverage GenAI’s power without compromising sensitive information.
Ultimately, Salesforce is empowering organisations to confidently deploy AI by making trust non-negotiable, ensuring organisational data is used responsibly and securely to drive real business value.
How does Salesforce differentiate their approach to Agentic AI?
ACHIM GRANZEN, Principal Advisor
Salesforce’s focus on Agentic AI focus stands out for its clarity and depth. The Agentforce platform takes centre stage, demonstrating how clients can now build Agentic AI with little or no code and deploy agents seamlessly across the Salesforce environment.
But beyond the polished demos and compelling customer stories, the most critical takeaway risked being overlooked: Agentforce is not a standalone capability. It’s tightly integrated with Data Cloud and the broader Salesforce platform. That layered architecture is more than just a technical decision; it’s what ensures every AI agent is governed, auditable, and constrained to what’s been provisioned in Data Cloud. It’s the foundational safeguard that makes Agentic AI viable in the enterprise.
And that’s the message that needs greater emphasis. As organisations move from experimentation to real-world deployment, trust and control become just as vital as ease of use. Salesforce’s architecture delivers both – and that balance is a key differentiator in the crowded enterprise AI space.
MANISH GOENKA, Principal Advisor
Salesforce has moved beyond passive AI assistance to autonomous agents that can take meaningful action within trusted boundaries. Rather than focusing solely on chat-based copilots, Salesforce emphasises intelligent agents embedded into business workflows, capable of executing tasks like claims processing or personalised service without human intervention.
What sets Salesforce apart is how deeply this vision is integrated into their platform. With Einstein Copilot and Copilot Studio, customers can build their own cross-system agents, not just those limited to Salesforce apps. And by enabling partners to create and monetise agents via AppExchange, Salesforce is building a full-fledged AI ecosystem, positioning themselves as a platform for enterprise AI, not just a CRM.
Trust is a cornerstone of this approach. Salesforce’s focus on governance, auditability, and ethical AI ensures that Agentic AI is not only powerful, but also secure and accountable – key concerns as agents become more autonomous.
In a crowded AI space, Salesforce stands out by offering a grounded, scalable vision of Agentic AI, anchored in real use cases, platform extensibility, and responsible innovation.
Where are Salesforce’s biggest growth opportunities in APAC?
MANISH GOENKA
Salesforce has significant growth opportunities across Asia Pacific, with Singapore playing a pivotal role in its regional strategy. The company’s USD 1 billion investment and the launch of their first overseas AI research hub firmly position Singapore as more than just a sales market. It becomes a core engine for product innovation and a key driver of Salesforce’s long-term AI leadership.
Across the region, public sector transformation and SME digitisation represent major areas of opportunity. Salesforce’s secure and compliant Government Cloud is well suited to support Smart Nation goals and modernise public digital services. At the same time, governments are actively pushing SME digitisation, creating demand for scalable, modular platforms that can grow from basic CRM solutions to AI-enabled automation.
Sustainability is also emerging as a strong growth vector. As ESG reporting becomes commonplace in more markets, tools like Net Zero Cloud are well positioned to help businesses meet compliance requirements and improve data transparency.
Finally, the rapidly expanding ecosystem of certified professionals and ISV partners across Asia Pacific is enabling faster, more localised implementations. This grounds Salesforce’s capabilities in local context, accelerating time to value and delivering business outcomes that are tailored to the region’s diverse needs.
What does the Informatica acquisition mean for Salesforce’s AI strategy?
ACHIM GRANZEN
The planned acquisition of Informatica is a strategically important move that completes Salesforce’s Agentforce narrative. At the World Tour, Agentforce was positioned as the future of enterprise AI, allowing organisations to build and deploy autonomous agents across the Salesforce ecosystem. But some lingering concerns remained around how deeply Data Cloud could handle governance, especially as AI agents begin making decisions and executing tasks without human oversight.
Informatica answers that question. With proven tools for data quality, lineage, and policy enforcement, Informatica brings a level of governance maturity that complements Salesforce’s ambition. Its integration into Data Cloud strengthens the trust layer that underpins Agentforce and reinforces Salesforce’s positioning as an enterprise-grade AI platform.
Of course, there are broader implications too. Salesforce will gain access to Informatica’s installed base, potentially opening up cross-sell opportunities. And there are questions to resolve, such as how Informatica will operate as a product line within the larger Salesforce ecosystem.
But the core value of the deal is clear: by bringing Informatica’s governance expertise into the fold, Salesforce can significantly accelerate its ability to deliver trusted, production-ready AI at scale. From a risk and compliance standpoint, that governance capability may prove to be the most valuable part of the acquisition.
What will define Salesforce’s next chapter of growth in APAC?
SASH MUKHERJEE
Just as Salesforce is driving an integrated enterprise platform from the CRM and customer experience lens, competitors (and partners) are taking a similar platform-centric approach from other functional vantage points – whether it’s HR (like Workday), Finance (like Oracle), or IT (like ServiceNow). In fast-growing, cost-sensitive markets across APAC, competing on price alone won’t be sustainable, especially with strong regional players offering leaner, localised alternatives.
To win, Salesforce must adopt a nuanced strategy that goes beyond product breadth. This means addressing local economic realities – offering right-sized solutions for businesses at different stages of digital maturity – while consistently reinforcing the long-term value, resilience, and global standards that set Salesforce apart. Their differentiators in data security, compliance, and ecosystem depth must be positioned not as add-ons, but as essential foundations for future-ready growth.
More flexible entry points – whether modular offerings, usage-based pricing, or vertical-specific bundles – can reduce friction and make the platform more accessible. At the same time, strengthening local partnerships with ISVs, system integrators, and government bodies can help tailor offerings to market-specific needs, ensuring relevance and faster implementation.
Ultimately, Salesforce’s growth across APAC will depend on their ability to balance global strengths with local agility.
ULLRICH LOEFFLER
Salesforce is well positioned to lead in AI-driven transformation, but doing so will require evolving their sales approach to match the complexity and expectations of today’s enterprise buyers. With a strong foundation selling to marketing and customer leaders, the company now has an opportunity to deepen engagement with CIOs and CTOs, reframing themselves not just as a CRM provider, but as a full-spectrum enterprise platform.
Traditional sales reps who excel at pitching features to business users are no longer enough. Selling AI – particularly agentic, autonomous AI – demands sales professionals who can link technical capabilities to strategic outcomes and lead conversations around risk, compliance, and long-term value.
To sustain their leadership, Salesforce will need to invest in a new generation of sales talent: domain-fluent, consultative, and able to navigate complex, cross-functional buying journeys.
“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.
Exiting the North-South Highway 101 onto Mountain View, California, reveals how mundane innovation can appear in person. This Silicon Valley town, home to some of the most prominent tech giants, reveals little more than a few sprawling corporate campuses of glass and steel. As the industry evolves, its architecture naturally grows less inspiring. The most imposing structures, our modern-day coliseums, are massive energy-rich data centres, recursively training LLMs among other technologies. Yet, just as the unassuming exterior of the Googleplex conceals a maze of shiny new software, GenAI harbours immense untapped potential. And people are slowly realising that.
It has been over a year that GenAI burst onto the scene, hastening AI implementations and making AI benefits more identifiable. Today, we see successful use cases and collaborations all the time.
Finding Where Expectations Meet Reality
While the data centres of Mountain View thrum with the promise of a new era, it is crucial to have a quick reality check.
Just as the promise around dot-com startups reached a fever pitch before crashing, so too might the excitement surrounding AI be entering a period of adjustment. Every organisation appears to be looking to materialise the hype.
All eyes (including those of 15 million tourists) will be on Paris as they host the 2024 Olympics Games. The International Olympic Committee (IOC) recently introduced an AI-powered monitoring system to protect athletes from online abuse. This system demonstrates AI’s practical application, monitoring social media in real time, flagging abusive content, and ensuring athlete’s mental well-being. Online abuse is a critical issue in the 21st century. The IOC chose the right time, cause, and setting. All that is left is implementation. That’s where reality is met.
While the Googleplex doesn’t emanate the same futuristic aura as whatever is brewing within its walls, Google’s AI prowess is set to take centre stage as they partner with NBCUniversal as the official search AI partner of Team USA. By harnessing the power of their GenAI chatbot Gemini, NBCUniversal will create engaging and informative content that seamlessly integrates with their broadcasts. This will enhance viewership, making the Games more accessible and enjoyable for fans across various platforms and demographics. The move is part of NBCUniversal’s effort to modernise its coverage and attract a wider audience, including those who don’t watch live television and younger viewers who prefer online content.
From Silicon Valley to Main Street
While tech giants invest heavily in GenAI-driven product strategies, retailers and distributors must adapt to this new sales landscape.
Perhaps the promise of GenAI lies in the simple storefronts where it meets the everyday consumer. Just a short drive down the road from the Googleplex, one of many 37,000-square-foot Best Buys is preparing for a launch that could redefine how AI is sold.
In the most digitally vogue style possible, the chain retailer is rolling out Microsoft’s flagship AI-enabled PCs by training over 30,000 employees to sell and repair them and equipping over 1,000 store employees with AI skillsets. Best Buy are positioning themselves to revitalise sales, which have been declining for the past ten quarters. The company anticipates that the augmentation of AI skills across a workforce will drive future growth.
The Next Generation of User-Software Interaction
We are slowly evolving from seeking solutions to seamless integration, marking a new era of User-Centric AI.
The dynamic between humans and software has mostly been transactional: a question for an answer, or a command for execution. GenAI however, is poised to reshape this. Apple, renowned for their intuitive, user-centric ecosystem, is forging a deeper and more personalised relationship between humans and their digital tools.
Apple recently announced a collaboration with OpenAI at its WWDC, integrating ChatGPT into Siri (their digital assistant) in its new iOS 18 and macOS Sequoia rollout. According to Tim Cook, CEO, they aim to “combine generative AI with a user’s personal context to deliver truly helpful intelligence”.
Apple aims to prioritise user personalisation and control. Operating directly on the user’s device, it ensures their data remains secure while assimilating AI into their daily lives. For example, Siri now leverages “on-screen awareness” to understand both voice commands and the context of the user’s screen, enhancing its ability to assist with any task. This marks a new era of personalised GenAI, where technology understands and caters to individual needs.
We are beginning to embrace a future where LLMs assume customer-facing roles. The reality is, however, that we still live in a world where complex issues are escalated to humans.
The digital enterprise landscape is evolving. Examples such as the Salesforce Einstein Service Agent, its first fully autonomous AI agent, aim to revolutionise chatbot experiences. Built on the Einstein 1 Platform, it uses LLMs to understand context and generate conversational responses grounded in trusted business data. It offers 24/7 service, can be deployed quickly with pre-built templates, and handles simple tasks autonomously.
The technology does show promise, but it is important to acknowledge that GenAI is not yet fully equipped to handle the nuanced and complex scenarios that full customer-facing roles need. As technology progresses in the background, companies are beginning to adopt a hybrid approach, combining AI capabilities with human expertise.
AI for All: Democratising Innovation
The transformations happening inside the Googleplex, and its neighbouring giants, is undeniable. The collaborative efforts of Google, SAP, Microsoft, Apple, and Salesforce, amongst many other companies leverage GenAI in unique ways and paint a picture of a rapidly evolving tech ecosystem. It’s a landscape where AI is no longer confined to research labs or data centres, but is permeating our everyday lives, from Olympic broadcasts to customer service interactions, and even our personal devices.
The accessibility of AI is increasing, thanks to efforts like Best Buy’s employee training and Apple’s on-device AI models. Microsoft’s Copilot and Power Apps empower individuals without technical expertise to harness AI’s capabilities. Tools like Canva and Uizard empower anybody with UI/UX skills. Platforms like Coursera offer certifications in AI. It’s never been easier to self-teach and apply such important skills. While the technology continues to mature, it’s clear that the future of AI isn’t just about what the machines can do for us—it’s about what we can do with them. The on-ramp to technological discovery is no longer North-South Highway 101 or the Googleplex that lays within, but rather a network of tools and resources that’s rapidly expanding, inviting everyone to participate in the next wave of technological transformation.
When OpenAI released ChatGPT, it became obvious – and very fast – that we were entering a new era of AI. Every tech company scrambled to release a comparable service or to infuse their products with some form of GenAI. Microsoft, piggybacking on its investment in OpenAI was the fastest to market with impressive text and image generation for the mainstream. Copilot is now embedded across its software, including Microsoft 365, Teams, GitHub, and Dynamics to supercharge the productivity of developers and knowledge workers. However, the race is on – AWS and Google are actively developing their own GenAI capabilities.
AWS Catches Up as Enterprise Gains Importance
Without a consumer-facing AI assistant, AWS was less visible during the early stages of the GenAI boom. They have since rectified this with a USD 4B investment into Anthropic, the makers of Claude. This partnership will benefit both Amazon and Anthropic, bringing the Claude 3 family of models to enterprise customers, hosted on AWS infrastructure.
As GenAI quickly emerges from shadow IT to an enterprise-grade tool, AWS is catching up by capitalising on their position as cloud leader. Many organisations view AWS as a strategic partner, already housing their data, powering critical applications, and providing an environment that developers are accustomed to. The ability to augment models with private data already residing in AWS data repositories will make it an attractive GenAI partner.
AWS has announced the general availability of Amazon Q, their suite of GenAI tools aimed at developers and businesses. Amazon Q Developer expands on what was launched as Code Whisperer last year. It helps developers accelerate the process of building, testing, and troubleshooting code, allowing them to focus on higher-value work. The tool, which can directly integrate with a developer’s chosen IDE, uses NLP to develop new functions, modernise legacy code, write security tests, and explain code.
Amazon Q Business is an AI assistant that can safely ingest an organisation’s internal data and connect with popular applications, such as Amazon S3, Salesforce, Microsoft Exchange, Slack, ServiceNow, and Jira. Access controls can be implemented to ensure data is only shared with authorised users. It leverages AWS’s visualisation tool, QuickSight, to summarise findings. It also integrates directly with applications like Slack, allowing users to query it directly.
Going a step further, Amazon Q Apps (in preview) allows employees to build their own lightweight GenAI apps using natural language. These employee-created apps can then be published to an enterprise’s app library for broader use. This no-code approach to development and deployment is part of a drive to use AI to increase productivity across lines of business.
AWS continues to expand on Bedrock, their managed service providing access to foundational models from companies like Mistral AI, Stability AI, Meta, and Anthropic. The service also allows customers to bring their own model in cases where they have already pre-trained their own LLM. Once a model is selected, organisations can extend its knowledge base using Retrieval-Augmented Generation (RAG) to privately access proprietary data. Models can also be refined over time to improve results and offer personalised experiences for users. Another feature, Agents for Amazon Bedrock, allows multi-step tasks to be performed by invoking APIs or searching knowledge bases.
To address AI safety concerns, Guardrails for Amazon Bedrock is now available to minimise harmful content generation and avoid negative outcomes for users and brands. Contentious topics can be filtered by varying thresholds, and Personally Identifiable Information (PII) can be masked. Enterprise-wide policies can be defined centrally and enforced across multiple Bedrock models.
Google Targeting Creators
Due to the potential impact on their core search business, Google took a measured approach to entering the GenAI field, compared to newer players like OpenAI and Perplexity. The useability of Google’s chatbot, Gemini, has improved significantly since its initial launch under the moniker Bard. Its image generator, however, was pulled earlier this year while it works out how to carefully tread the line between creativity and sensitivity. Based on recent demos though, it plans to target content creators with images (Imagen 3), video generation (Veo), and music (Lyria).
Like Microsoft, Google has seen that GenAI is a natural fit for collaboration and office productivity. Gemini can now assist the sidebar of Workspace apps, like Docs, Sheets, Slides, Drive, Gmail, and Meet. With Google Search already a critical productivity tool for most knowledge workers, it is determined to remain a leader in the GenAI era.
At their recent Cloud Next event, Google announced the Gemini Code Assist, a GenAI-powered development tool that is more robust than its previous offering. Using RAG, it can customise suggestions for developers by accessing an organisation’s private codebase. With a one-million-token large context window, it also has full codebase awareness making it possible to make extensive changes at once.
The Hardware Problem of AI
The demands that GenAI places on compute and memory have created a shortage of AI chips, causing the valuation of GPU giant, NVIDIA, to skyrocket into the trillions of dollars. Though the initial training is most hardware-intensive, its importance will only rise as organisations leverage proprietary data for custom model development. Inferencing is less compute-heavy for early use cases, such as text generation and coding, but will be dwarfed by the needs of image, video, and audio creation.
Realising compute and memory will be a bottleneck, the hyperscalers are looking to solve this constraint by innovating with new chip designs of their own. AWS has custom-built specialised chips – Trainium2 and Inferentia2 – to bring down costs compared to traditional compute instances. Similarly, Microsoft announced the Maia 100, which it developed in conjunction with OpenAI. Google also revealed its 6th-generation tensor processing unit (TPU), Trillium, with significant increase in power efficiency, high bandwidth memory capacity, and peak compute performance.
The Future of the GenAI Landscape
As enterprises gain experience with GenAI, they will look to partner with providers that they can trust. Challenges around data security, governance, lineage, model transparency, and hallucination management will all need to be resolved. Additionally, controlling compute costs will begin to matter as GenAI initiatives start to scale. Enterprises should explore a multi-provider approach and leverage specialised data management vendors to ensure a successful GenAI journey.
2024 and 2025 are looking good for IT services providers – particularly in Asia Pacific. All types of providers – from IT consultants to managed services VARs and systems integrators – will benefit from a few converging events.
However, amidst increasing demand, service providers are also challenged with cost control measures imposed in organisations – and this is heightened by the challenge of finding and retaining their best people as competition for skills intensifies. Providers that service mid-market clients might find it hard to compete and grow without significant process automation to compensate for the higher employee costs.
Why Organisations are Opting for IT Service
- Organisations are seeking further cost reductions. Managed services providers will see more opportunities to take cost and complexity out of organisation’s IT functions. The focus in 2024 will be less on “managing” services and more on “transforming” them using ML, AI, and automation to reduce cost and improve value.
- Big app upgrades are back on the agenda. SAP is going above and beyond to incentivise their customers and partners to migrate their on-premises and hyperscale hosted instances to true cloud ERP. Initiatives such as Rise with SAP have been further expanded and improved to accelerate the transition. Salesforce customers are also looking to streamline their deployments while also taking advantage of the new AI and data capabilities. But many of these projects will still be complex and time-consuming.
- Cloud deployments are getting more complex. For many organisations, the simple cloud migrations are done. This is the stage of replatforming, retiring, and refactoring applications to take advantage of public and hybrid cloud capabilities. These are not simple lift and shift – or switch to SaaS – engagements.
- AI will drive a greater need for process improvement and transformation. This will happen along with associated change management and training programs. While it is still early days for GenAI, before the end of 2024, many organisations will move beyond experimentation to department or enterprise wide GenAI initiatives.
- Increasing cybersecurity and data governance demands will prolong the security skill shortage. More organisations will turn to managed security services providers and cybersecurity consultants to help them develop their strategy and response to the rising threat levels.
Choosing the Right Cost Model for IT Services
Buyers of IT services must implement strict cost-control measures and consider various approaches to align costs with business and customer outcomes, including different cost models:
Fixed-Price Contracts. These contracts set a firm price for the entire project or specific deliverables. Ideal when project scope is clear, they offer budget certainty upfront but demand detailed specifications, potentially leading to higher initial quotes due to the provider assuming more risk.
Time and Materials (T&M) Contracts with Caps. Payment is based on actual time and materials used, with negotiated caps to prevent budget overruns. Combining flexibility with cost predictability, this model offers some control over total expenses.
Performance-Based Pricing. Fees are tied to service provider performance, incentivising achievement of specific KPIs or milestones. This aligns provider interests with client goals, potentially resulting in cost savings and improved service quality.
Retainer Agreements with Scope Limits. Recurring fees are paid for ongoing services, with defined limits on work scope or hours within a given period. This arrangement ensures resource availability while containing expenses, particularly suitable for ongoing support services.
Other Strategies for Cost Efficiency and Effective Management
Technology leaders should also consider implementing some of the following strategies:
Phased Payments. Structuring payments in phases, tied to the completion of project milestones, helps manage cash flow and provides a financial incentive for the service provider to meet deadlines and deliverables. It also allows for regular financial reviews and adjustments if the project scope changes.
Cost Transparency and Itemisation. Detailed billing that itemises the costs of labour, materials, and other expenses provides transparency to verify charges, track spending against the budget, and identify areas for potential savings.
Volume Discounts and Negotiated Rates. Negotiating volume discounts or preferential rates for long-term or large-scale engagements, makes providers to offer reduced rates for a commitment to a certain volume of work or an extended contract duration.
Utilisation of Shared Services or Cloud Solutions. Opting for shared or cloud-based solutions where feasible, offers economies of scale and reduces the need for expensive, dedicated infrastructure and resources.
Regular Review and Adjustment. Conducting regular reviews of the services and expenses with the provider to ensure alignment with the budget and objectives, prepares organisations to adjust the scope, renegotiate terms, or implement cost-saving measures as needed.
Exit Strategy. Planning an exit strategy that include provisions for contract termination, transition services, protects an organisation in case the partnership needs to be dissolved.
Conclusion
Many businesses swing between insourcing and outsourcing technology capabilities – with the recent trend moving towards insourcing development and outsourcing infrastructure to the public cloud. But 2024 will see demand for all types of IT services across nearly every geography and industry. Tech services providers can bring significant value to your business – but improved management, monitoring, and governance will ensure that this value is delivered at a fair cost.
Organisations are moving beyond digitalisation to a focus on building market differentiation. It is widely acknowledged that customer-centric strategies lead to better business outcomes, including increased customer satisfaction, loyalty, competitiveness, growth, and profitability.
AI is the key enabler driving personalisation at scale. It has also become key to improving employee productivity, empowering them to focus on high-value tasks and deepening customer engagements.
Over the last month – at the Salesforce World Tour and over multiple analyst briefings – Salesforce has showcased their desire to solve customer challenges using AI innovations. They have announced a range of new AI innovations across Data Cloud, their integrated CRM platform.
Ecosystm Advisors Kaushik Ghatak, Niloy Mukherjee, Peter Carr, and Sash Mukherjee comment on Salesforce’s recent announcements and messaging.
Read on to find out more.
Download Ecosystm VendorSphere: Salesforce AI Innovations Transforming CRM as a PDF
November has seen uncertainties in the technology market with news of layoffs and hiring freezes from big names in the industry – Meta, Amazon, Salesforce, and Apple to name a few. These have impacted thousands of people globally, leaving tech talent with one common question, ‘What next?’
While the current situation and economic trends may seem grim, it is not all bad news for tech workers. It is true that people strategies in the sector may be impacted, but there are still plenty of opportunities for tech experts in the industry.
Here is what Ecosystm Analysts say about what’s next for technology workers.
Today, we are seeing two quite conflicting signals in the market: Tech vendors are laying off staff; and IT teams in businesses are struggling to hire the people they need.
At Ecosystm, we still expect a healthy growth in tech spend in 2023 and 2024 regardless of economic conditions. Businesses will be increasing their spend on security and data governance to limit their exposure to cyber-attacks; they will spend on automation to help teams grow productivity with current or lower headcount; they will continue their cloud investments to simplify their technology architectures, increase resilience, and to drive business agility. Security, cloud, data management and analytics, automation, and digital developers will all continue to see employment opportunities.
If this is the case, then why are tech vendors laying off headcount?
The slowdown in the American economy is a big reason. Tech providers that are laying of staff are heavily exposed to the American market.
- Salesforce – 68% Americas
- Facebook – 44% North America
- Genesys – around 60% in North America
Much of the messaging that these providers are giving is it is not that business is performing poorly – it is that growth is slowing down from the fast pace that many were witnessing when digital strategies accelerated.
Some of these tech providers might also be using the opportunity to “trim the fat” from their business – using the opportunity to get rid of the 2-3% of staff or teams that are underperforming. Interestingly, many of the people that are being laid off are from in or around the sales organisation. In some cases, tech providers are trimming products or services from their business and associated product, marketing, and technical staff are also being laid off.
While the majority of the impact is being felt in North America, there are certainly some people being laid off in Asia Pacific too. Particularly in companies where the development is done in Asia (India, China, ASEAN, etc.), there will be some impact when products or services are discontinued.
While it is not all bad news for tech talent, there is undoubtedly some nervousness. So this is what you should think about:
Change your immediate priorities. Ecosystm research found that 40% of digital/IT talent were looking to change employers in 2023. Nearly 60% of them were also thinking of changes in terms of where they live and their career.
This may not be the right time to voluntarily change your job. Job profiles and industry requirements should guide your decision – by February 2023, a clearer image of the job market will emerge. Till then, upskill and get those certifications to stay relevant!
Be prepared for contract roles. With a huge pool of highly skilled technologists on the hunt for new opportunities, smaller technology providers and start-ups have a cause to celebrate. They have faced the challenge of getting the right talent largely because of their inability to match the remunerations offered by large tech firms.
These companies may still not be able to match the benefits offered by the large tech firms – but they provide opportunities to expand your portfolio, industry expertise, and experience in emerging technologies. This will see a change in job profiles. It is expected that more contractual roles will open up for the technology industry. You will have more opportunities to explore the option of working on short-term assignments and consulting projects – sometimes on multiple projects and with multiple clients at the same time.
Think about switching sides. The fact remains that digital and technology upgrades continue to be organisational priorities, across all industries. As organisations continue on their digital journeys, they have an immense potential to address their skills gap now with the availability of highly skilled talent. In a recently conducted Ecosystm roundtable, CIOs reported that new graduates have been demanding salaries as high as USD 200,000 per annum! Even banks and consultancies – typically the top paying businesses – have been finding it hard to afford these skills! These industries may well benefit from the layoffs.
If you look at technology job listings, we see no signs of the demand abating!
COP26 has firmly put environmental consciousness as a leading global priority. While we have made progress in the last 30 odd years since climate change began to be considered as a reality, a lot needs to be done.
No longer is it enough for only governments to lead on green initiatives. Now is the time for non-profit organisations, investors, businesses – corporate and SMEs – and consumers to come together to ensure we leave a safer planet for our children.
February saw examples of how technology providers and large corporates are delivering on their environmental consciousness and implementing meaningful change.
Here are some announcements that show how tech providers and corporates are strengthening the Sustainability cause:
- IBM launches Sustainability Accelerator Program
- Microsoft boosts their Sustainability offerings by extending extend their EID tool for Microsoft 365
- Salesforce officially announce sustainability as a core company value
- Google enables Sustainable AIOps
- The Aviation industry (Southwest Airlines, ANA, Norwegian Air and Singapore Airlines) appears to be making a concerted effort to reduce carbon footprint.
Read on to find more.
Click here to download a copy of The Future of Sustainability as a PDF.
The first impact of the pandemic and the disruption it caused, was organisations scrambling to empower their remote employees. Over the last 2 years, significant investments have been made on collaboration platforms and tools. Now organisations are having to work towards making these workplaces truly hybrid where organisations have to ensure that all employees get the same experience, irrespective of where they choose to work from.
In 2022, organisations will continue to invest in building the Digital Workplace and address the associated technology, people, and process challenges.
Read on to find out what Ecosystm Analysts, Audrey William, Tim Sheedy and Venu Reddy think will be the key trends for the Digital Workplace in 2022.
Click here to download Ecosystm Predicts: The Top 5 Trends for the Digital Workplace in 2022 as PDF
