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.
In AI’s early days, enterprise leaders asked a straightforward question: “What can this automate?” The focus was on speed, scale, and efficiency and AI delivered. But that question is evolving. Now, the more urgent ask is: “Can this AI understand people?”
This shift – from automation to emotional intelligence – isn’t just theoretical. It’s already transforming how organisations connect with customers, empower employees, and design digital experiences. We’re shifting to a phase of humanised AI – systems that don’t just respond accurately, but intuitively, with sensitivity to mood, tone, and need.
One of the most unexpected, and revealing, AI use cases is therapy. Millions now turn to AI chat tools to manage anxiety, process emotions, and share deeply personal thoughts. What started as fringe behaviour is fast becoming mainstream. This emotional turn isn’t a passing trend; it marks a fundamental shift in how people expect technology to relate to them.
For enterprises, this raises a critical challenge: If customers are beginning to turn to AI for emotional support, what kind of relationship do they expect from it? And what does it take to meet that expectation – not just effectively, but responsibly, and at scale?
The Rise of Chatbot Therapy
Therapy was never meant to be one of AI’s first mass-market emotional use cases; and yet, here we are.
Apps like Wysa, Serena, and Youper have been quietly reshaping the digital mental health landscape for years, offering on-demand support through chatbots. Designed by clinicians, these tools draw on established methods like Cognitive Behavioural Therapy (CBT) and mindfulness to help users manage anxiety, depression, and stress. The conversations are friendly, structured, and often, surprisingly helpful.
But something even more unexpected is happening; people are now using general-purpose AI tools like ChatGPT for therapeutic support, despite them not being designed for it. Increasingly, users are turning to ChatGPT to talk through emotions, navigate relationship issues, or manage daily stress. Reddit threads and social posts describe it being used as a therapist or sounding board. This isn’t Replika or Wysa, but a general AI assistant being shaped into a personal mental health tool purely through user behaviour.
This shift is driven by a few key factors. First, access. Traditional therapy is expensive, hard to schedule, and for many, emotionally intimidating. AI, on the other hand, is always available, listens without judgement, and never gets tired.
Tone plays a big role too. Thanks to advances in reinforcement learning and tone conditioning, models like ChatGPT are trained to respond with calm, non-judgmental empathy. The result feels emotionally safe; a rare and valuable quality for those facing anxiety, isolation, or uncertainty. A recent PLOS study found that not only did participants struggle to tell human therapists apart from ChatGPT, they actually rated the AI responses as more validating and empathetic.
And finally, and perhaps surprisingly, is trust. Unlike wellness apps that push subscriptions or ads, AI chat feels personal and agenda-free. Users feel in control of the interaction – no small thing in a space as vulnerable as mental health.
None of this suggests AI should replace professional care. Risks like dependency, misinformation, or reinforcing harmful patterns are real. But it does send a powerful signal to enterprise leaders: people now expect digital systems to listen, care, and respond with emotional intelligence.
That expectation is changing how organisations design experiences – from how a support bot speaks to customers, to how an internal wellness assistant checks in with employees during a tough week. Humanised AI is no longer a niche feature of digital companions. It’s becoming a UX standard; one that signals care, builds trust, and deepens relationships.
Digital Companionship as a Solution for Support
Ten years ago, talking to your AI meant asking Siri to set a reminder. Today, it might mean sharing your feelings with a digital companion, seeking advice from a therapy chatbot, or even flirting with a virtual persona! This shift from functional assistant to emotional companion marks more than a technological leap. It reflects a deeper transformation in how people relate to machines.
One of the earliest examples of this is Replika, launched in 2017, which lets users create personalised chatbot friends or romantic partners. As GenAI advanced, so did Replika’s capabilities, remembering past conversations, adapting tone, even exchanging voice messages. A Nature study found that 90% of Replika users reported high levels of loneliness compared to the general population, but nearly half said the app gave them a genuine sense of social support.
Replika isn’t alone. In China, Xiaoice (spun off from Microsoft in 2020) has hundreds of millions of users, many of whom chat with it daily for companionship. In elder care, ElliQ, a tabletop robot designed for seniors has shown striking results: a report from New York State’s Office for the Aging cited a 95% drop in loneliness among participants.
Even more freeform platforms like Character.AI, where users converse with AI personas ranging from historical figures to fictional characters, are seeing explosive growth. People are spending hours in conversation – not to get things done, but to feel seen, inspired, or simply less alone.
The Technical Leap: What Has Changed Since the LLM Explosion
The use of LLMs for code editing and content creation is already mainstream in most enterprises but use cases have expanded alongside the capabilities of new models. LLMs now have the capacity to act more human – to carry emotional tone, remember user preferences, and maintain conversational continuity.
Key advances include:
- Memory. Persistent context and long-term recall
- Reinforcement Learning from Human Feedback (RLHF). Empathy and safety by design
- Sentiment and Emotion Recognition. Reading mood from text, voice, and expression
- Role Prompting. Personas using brand-aligned tone and behaviour
- Multimodal Interaction. Combining text, voice, image, gesture, and facial recognition
- Privacy-Sensitive Design. On-device inference, federated learning, and memory controls
Enterprise Implications: Emotionally Intelligent AI in Action
The examples shared might sound fringe or futuristic, but they reveal something real: people are now open to emotional interaction with AI. And that shift is creating ripple effects. If your customer service chatbot feels robotic, it pales in comparison to the AI friend someone chats with on their commute. If your HR wellness bot gives stock responses, it may fall flat next to the AI that helped a user through a panic attack the night before.
The lesson for enterprises isn’t to mimic friendship or romance, but to recognise the rising bar for emotional resonance. People want to feel understood. Increasingly, they expect that even from machines.
For enterprises, this opens new opportunities to tap into both emotional intelligence and public comfort with humanised AI. Emerging use cases include:
- Customer Experience. AI that senses tone, adapts responses, and knows when to escalate
- Brand Voice. Consistent personality and tone embedded in AI interfaces
- Employee Wellness. Assistants that support mental health, coaching, and daily check-ins
- Healthcare & Elder Care. Companions offering emotional and physical support
- CRM & Strategic Communications. Emotion-aware tools that guide relationship building
Ethical Design and Guardrails
Emotional AI brings not just opportunity, but responsibility. As machines become more attuned to human feelings, ethical complexity grows. Enterprises must ensure transparency – users should always know they’re speaking to a machine. Emotional data must be handled with the same care as health data. Empathy should serve the user, not manipulate them. Healthy boundaries and human fallback must be built in, and organisations need to be ready for regulation, especially in sensitive sectors like healthcare, finance, and education.
Emotional intelligence is no longer just a human skill; it’s becoming a core design principle, and soon, a baseline expectation.
Those who build emotionally intelligent AI with integrity can earn trust, loyalty, and genuine connection at scale. But success won’t come from speed or memory alone – it will come from how the experience makes people feel.
“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.
In 2024, business and technology leaders will leverage the opportunity presented by the attention being received by Generative AI engines to test and integrate AI comprehensively across the business. Many organisations will prioritise the alignment of their initial Generative AI initiatives with broader AI strategies, establishing distinct short-term and long-term goals for their AI investments.
AI adoption will influence business processes, technology skills, and, in turn, reshape the product/service offerings of AI providers.
Ecosystm analysts Achim Granzen, Peter Carr, Richard Wilkins, Tim Sheedy, and Ullrich Loeffler present the top 5 AI trends in 2024.
Click here to download ‘Ecosystm Predicts: Top 5 AI Trends in 2024.
#1 By the End of 2024, Gen AI Will Become a ‘Hygiene Factor’ for Tech Providers
AI has widely been commended as the ‘game changer’ that will create and extend the divide between adopters and laggards and be the deciding factor for success and failure.
Cutting through the hype, strategic adoption of AI is still at a nascent stage and 2024 will be another year where companies identify use cases, experiment with POCs, and commit renewed efforts to get their data assets in order.
The biggest impact of AI will be derived from integrated AI capability in standard packaged software and products – and this will include Generative AI. We will see a plethora of product releases that seamlessly weave Generative AI into everyday tools generating new value through increased efficiency and user-friendliness.
Technology will be the first industry where AI becomes the deciding factor between success and failure; tech providers will be forced to deliver on their AI promises or be left behind.
#2 Gen AI Will Disrupt the Role of IT Architects
Traditionally, IT has relied on three-tier architectures for applications, that faced limitations in scalability and real-time responsiveness. The emergence of microservices, containerisation, and serverless computing has paved the way for event-driven designs, a paradigm shift that decouples components and use events like user actions or data updates as triggers for actions across distributed services. This approach enhances agility, scalability, and flexibility in the system.
The shift towards event-driven designs and advanced architectural patterns presents a compelling challenge for IT Architects, as traditionally their role revolved around designing, planning and overseeing complex systems.
Generative AI is progressively demonstrating capabilities in architectural design through pattern recognition, predictive analytics, and automated decision-making.
With the adoption of Generative AI, the role of an IT Architect will change into a symbiotic relationship where human expertise collaborates with AI insights.
#3 Gen AI Adoption Will be Confined to Specific Use Cases
A little over a year ago, a new era in AI began with the initial release of OpenAI’s ChatGPT. Since then, many organisations have launched Generative AI pilots.
In its second-year enterprises will start adoption – but in strictly defined and limited use cases. Examples such as Microsoft Copilot demonstrate an early adopter route. While productivity increases for individuals can be significant, its enterprise impact is unclear (at this time).
But there are impactful use cases in enterprise knowledge and document management. Organisations across industries have decades (or even a century) of information, including digitised documents and staff expertise. That treasure trove of information can be made accessible through cognitive search and semantic answering, driven by Generative AI.
Generative AI will provide organisations with a way to access, distill, and create value out of that data – a task that may well be impossible to achieve in any other way.
#4 Gen AI Will Get Press Inches; ‘Traditional’ AI Will Do the Hard Work
While the use cases for Generative AI will continue to expand, the deployment models and architectures for enterprise Generative AI do not add up – yet.
Running Generative AI in organisations’ data centres is costly and using public models for all but the most obvious use cases is too risky. Most organisations opt for a “small target” strategy, implementing Generative AI in isolated use cases within specific processes, teams, or functions. Justifying investment in hardware, software, and services for an internal AI platform is challenging when the payback for each AI initiative is not substantial.
“Traditional AI/ML” will remain the workhorse, with a significant rise in use cases and deployments. Organisations are used to investing for AI by individual use cases. Managing process change and training is also more straightforward with traditional AI, as the changes are implemented in a system or platform, eliminating the need to retrain multiple knowledge workers.
#5 AI Will Pioneer a 21st Century BPM Renaissance
As we near the 25-year milestone of the 21st century, it becomes clear that many businesses are still operating with 20th-century practices and philosophies.
AI, however, represents more than a technological breakthrough; it offers a new perspective on how businesses operate and is akin to a modern interpretation of Business Process Management (BPM). This development carries substantial consequences for digital transformation strategies. To fully exploit the potential of AI, organisations need to commit to an extensive and ongoing process spanning the collection, organisation, and expansion of data, to integrating these insights at an application and workflow level.
The role of AI will transcend technological innovation, becoming a driving force for substantial business transformation. Sectors that specialise in workflow, data management, and organisational transformation are poised to see the most growth in 2024 because of this shift.
