A lot has been written and spoken about DeepSeek since the release of their R1 model in January. Soon after, Alibaba, Mistral AI, and Ai2 released their own updated models, and we have seen Manus AI being touted as the next big thing to follow.

DeepSeek’s lower-cost approach to creating its model – using reinforcement learning, the mixture-of-experts architecture, multi-token prediction, group relative policy optimisation, and other innovations – has driven down the cost of LLM development. These methods are likely to be adopted by other models and are already being used today. 

While the cost of AI is a challenge, it’s not the biggest for most organisations. In fact, few GenAI initiatives fail solely due to cost. 

The reality is that many hurdles still stand in the way of organisations’ GenAI initiatives, which need to be addressed before even considering the business case – and the cost – of the GenAI model. 

Real Barriers to GenAI 

• Data. The lifeblood of any AI model is the data it’s fed. Clean, well-managed data yields great results, while dirty, incomplete data leads to poor outcomes. Even with RAG, the quality of input data dictates the quality of results. Many organisations I work with are still discovering what data they have – let alone cleaning and classifying it. Only a handful in Australia can confidently say their data is fully managed, governed, and AI-ready. This doesn’t mean GenAI initiatives must wait for perfect data, but it does explain why Agentic AI is set to boom – focusing on single applications and defined datasets. 

• Infrastructure. Not every business can or will move data to the public cloud – many still require on-premises infrastructure optimised for AI. Some companies are building their own environments, but this often adds significant complexity. To address this, system manufacturers are offering easy-to-manage, pre-built private cloud AI solutions that reduce the effort of in-house AI infrastructure development. However, adoption will take time, and some solutions will need to be scaled down in cost and capacity to be viable for smaller enterprises in Asia Pacific. 

• Process Change. AI algorithms are designed to improve business outcomes – whether by increasing profitability, reducing customer churn, streamlining processes, cutting costs, or enhancing insights. However, once an algorithm is implemented, changes will be required. These can range from minor contact centre adjustments to major warehouse overhauls. Change is challenging – especially when pre-coded ERP or CRM processes need modification, which can take years. Companies like ServiceNow and SS&C Blue Prism are simplifying AI-driven process changes, but these updates still require documentation and training. 

• AI Skills. While IT teams are actively upskilling in data, analytics, development, security, and governance, AI opportunities are often identified by business units outside of IT. Organisations must improve their “AI Quotient” – a core understanding of AI’s benefits, opportunities, and best applications. Broad upskilling across leadership and the wider business will accelerate AI adoption and increase the success rate of AI pilots, ensuring the right people guide investments from the start. 

• AI Governance. Trust is the key to long-term AI adoption and success. Being able to use AI to do the “right things” for customers, employees, and the organisation will ultimately drive the success of GenAI initiatives. Many AI pilots fail due to user distrust – whether in the quality of the initial data or in AI-driven outcomes they perceive as unethical for certain stakeholders. For example, an AI model that pushes customers toward higher-priced products or services, regardless of their actual needs, may yield short-term financial gains but will ultimately lose to ethical competitors who prioritise customer trust and satisfaction. Some AI providers, like IBM and Microsoft, are prioritising AI ethics by offering tools and platforms that embed ethical principles into AI operations, ensuring long-term success for customers who adopt responsible AI practices. 

GenAI and Agentic AI initiatives are far from becoming standard business practice. Given the current economic and political uncertainty, many organisations will limit unbudgeted spending until markets stabilise. However, technology and business leaders should proactively address the key barriers slowing AI adoption within their organisations. As more AI platforms adopt the innovations that helped DeepSeek reduce model development costs, the economic hurdles to GenAI will become easier to overcome. 

During this time the global community has lurched through tech and property driven financial crisis. It has endured endemic terrorism, and a crippling pandemic. And it has been fragmented by the existential threats of energy and climate, world order dislocation, and challenges to once unshakable fiat money. The wonderfully efficient business models and supply chains enabled by W. Edwards Deming following World War II are fractured and broken. Despite all these challenges, the desire for a hopeful return to a golden age of global prosperity is clearly evident. Just maybe not as we know it.

The period between W. Edwards Deming and Dotcom (let’s say 1950-2000) ushered in ERP and the modern software revolution. Over decades, highly refined processes and perfected workflows shifted from paper and clipboards into mainframe environments – from conveyor belts to computing and from ledgers to LANs.

In the progression to slightly less monolithic server-based business applications, millions of lines of customised code are transferred into configurable data fields, coupled with ready-made workflow connections, and processes based on standards set by leading companies and their representative bodies. The standardisation of business systems lowered the entry point for new enterprises, spawned new industries, and ultimately allowed SaaS to proliferate.

ERP was a true revolution in automating process and quality management systems and building the modern world. Cloud was then a transformation for ERP. It was an innovation on an original idea, but it wasn’t the next revolution. In many ways, by standardising business systems, we went too far. The vendor market over-estimated what configuration over customisation could achieve and ultimately set unachievable expectations in relation to client outcomes. On the client side, end-user organisations seized on vanilla processes and workflows and got lazy about working out solutions to their own problems. In chasing out-of-the-box software they sought to expedite, and even outsource, the hard work. In doing so, the core driver of 20^th century post war economic prosperity was forgotten.

Looking Ahead

In business transformations there are no short cuts to results

One of the defining social drivers of the 21^st century is a move towards the concept of individualism. We see it everywhere. In the transformation of traditional marriage, family, and identity structures. In the migration away from the concept of houses and homes, in the rise of the gig economy, and even in the regulatory schemes of government, financial and insurance services. The individual sits at the centre of new globalisation economic design and is giving rise to the next business systems revolution. At ServiceNow Knowledge 2022 I was fortunate to hear Dr Catriona Wallace and the Hon Victor Dominello MP discuss it in the context of their recent research. Dr Wallace described the trend as, “know me and care about me”, and discussed the requirements to operate within a world of both hyper-personalisation and ethical restraint.

This time however the business systems revolution to support this change is not being driven by process efficiencies and quality management, though they remain important tools. It is being driven by the pursuit of Experiential Excellence. You’ve heard it many times before and once you’ve seen it you can’t unsee it – Customer Experience, Employee Experience, Digital Experience. These are all ambitions of populist organisational and service transformation agendas with Experiential Excellence at their core.

For business and technology leaders it requires a mental shift. Traditional ERP alone will not get us there. It means a new business systems methodology is required to accompany, and reflect the challenges of the modern world, not one created more than 70 years ago.

An Experiential Excellence platform isn’t just a new ERP. It’s a new type of system capable of operating at speed and with breakthrough power; but it is also capable of breaking the intellectual shackles of pre-configuration to help organisations recapture the essence of what Deming started so long ago and we somehow lost along the way: The ability to think about and solve any kind of complex, innovative and multi-objective, multi-stakeholder problem. And I think that ServiceNow, and the Now Platform, is the first company (and business system) to do it.

The sense of something special was clearly evident among ServiceNow staff and partners, at the event. But I don’t think they have yet nailed the messaging. And the reason is because there is still such a strong gravitational pull towards the old ERP model among end-user clients. This reinforces a need for ServiceNow to still define itself by the last 50 years of system technology rather than the next 50.

That needs to change. So, next time when a client asks, is ServiceNow an ERP or is it an RPA platform or something else, the answer is – it is neither, and both, and all, and sometimes at the same time. This wonderful superposition, the same quantum computing characteristic that allows a particle to be one thing, or either, or both, all at the same time, is the very essence of their opportunity – should they wish to take it.

To be a leader in the new quantum age of computing will mean taking the brave step of unshackling themselves from the 20^th century view of ERP and lead the redefinition of business systems for the quantum age. Let the revolution begin.  

5/5 (1)

Why do we use AI? The goal of a business in adding intelligence is to enhance business decision-making, and growing revenue and profit within the framework of its business model.

The problem many organisations face is that they understand their own core competence in their own industry, but they do not understand how to tweak and enhance business processes to make the business run better. For example, AI can help transform the way companies run their production lines, enabling greater efficiency by enhancing human capabilities, providing real-time insights, and facilitating design and product innovation. But first, one has to be able to understand and digest the data within the organisation that would allow that to happen.

Ecosystm research shows that AI adoption crosses the gambit of business processes (Figure 1), but not all firms are process optimised to achieve those goals internally.  

The initial landscape for AI services primarily focused on tech companies building AI products into their own solutions to power their own services. So, the likes of Amazon, Google and Apple were investing in people and processes for their own enhancements.

As the benefits of AI are more relevant in a post-pandemic world with staff and resource shortages, non-tech firms are becoming interested in applying those advantages to their own business processes.

AI for Decisions

Recent start-up ventures in AI are focusing on non-tech companies and offering services to get them to use AI within their own business models.  Peak AI says that their technology can help enterprises that work with physical products to make better, AI-based evaluations and decisions, and has recently closed a funding round of USD 21 million.

The relevance of this is around the terminology that Peak AI has introduced. They call what they offer “Decision Intelligence” and are crafting a market space around it. Peak’s basic premise was to build AI not as a business goal for itself but as a business service aided by a solution and limited to particular types of added value. The goal of Peak AI is to identify where Decision Intelligence can add value, and help the company build a business case that is both achievable and commercially viable.

For example, UK hard landscaping manufacturer Marshalls worked with Peak AI to streamline their bid process with contractors. This allows customers to get the answers they need in terms of bid decisions and quotes quickly and efficiently, significantly speeding up the sales cycle.

AI-as-a-Service is not a new concept. Canadian start-up Element AI tried to create an AI services business for non-tech companies to use as they might these days use consulting services. It never quite got there, though, and was acquired by ServiceNow last year. Peak AI is looking at specific elements such as sales, planning and supply chain for physical products in how decisions are made and where adding some level of automation in the decision is beneficial. The Peak AI solution, CODI (Connected Decision Intelligence) sits as a layer of intelligence that between the other systems, ingesting the data and aiding in its utilisation.

The added tool to create a data-ingestion layer for business decision-making is quite a trend right now. For example, IBM’s Causal Inference 360 Toolkit offers access to multiple tools that can move the decision-making processes from “best guess” to concrete answers based on data, aiding data scientists to apply and understand causal inference in their models.

Implications on Business Processes

The bigger problem is not the volume of data, but the interpretation of it.

Data warehouses and other ways of gathering data to a central or cloud-based location to digest is also not new. The real challenge lies with the interpretation of what the data means and what decisions can be fine-tuned with this data. This implies that data modelling and process engineers need to be involved. Not every company has thought through the possible options for their processes, nor are they necessarily ready to implement these new processes both in terms of resources and priorities. This also requires data harmonisation rules, consistent data quality and managed data operations.

Given the increasing flow of data in most organisations, external service providers for AI solution layers embedded in the infrastructure as data filters could be helpful in making sense of what exists. And they can perhaps suggest how the processes themselves can be readjusted to match the growth possibilities of the business itself. This is likely a great footprint for the likes of Accenture, KPMG and others as process wranglers.

5/5 (3)

Many years ago – back in 2003 – I spent some quality time with BMC at their global analyst event in Phoenix, Arizona and they introduced the concept of “Business Service Management” (BSM). I was immediately a convert – that businesses can focus their IT Service Management initiatives on the business and customer services that the technology supports. Businesses that use BSM can have an understanding of the impact and importance of technology systems and assets because there is a direct link between these assets and the systems they support. A router that supports a customer payment platform suddenly becomes a much higher priority than one that supports an employee expense platform.

But for most businesses, this promise was never delivered. Creating a BSM solution became a highly manual process – mapping processes, assets, and applications. Many businesses that undertook this challenge reported that by the time they had mapped their processes, the map was out of date – as processes had changed; assets had been retired, replaced, or upgraded; software had been moved to the cloud or new modules had been implemented; and architectures had changed. Effectively their BSM mapping was often a pointless task – sometimes only delivering value in the slow to change systems – back-end applications and infrastructure that delivers limited value and has a defined retirement date.

The Growth of Digital Business Strategies

Our technology systems are becoming more important than ever as digital business strategies are realised and digital interactions with customers, employees, and partners significantly increase. Many businesses expect their digital investments to remain strong well into 2022 (Figure 1). More than ever, we need to understand the link between our tech systems and the business and customer services they support.

I recently had the opportunity to attend a briefing by ServiceNow regarding their new “AI-Powered Service Operations” that highlighted their service-aware CMDB – adding machine learning to their service mapping capabilities. The upgraded offering has the ability to map entire environments in hours or minutes – not months or weeks. And as a machine learning capability, it is only likely to get smarter – to learn from their customers’ use of the service and begin to recognise what applications, systems, and infrastructure are likely to be supporting each business service.

This heralds a new era in service management – one where the actual business and customer impact of outages is known immediately; where the decision to delay an upgrade or fix to a known problem can be made with a full understanding of the impacts. At one of my previous employers, email went down for about a week. It was finally attributed to an upgrade to network equipment that sat between the email system and the corporate network and the internet. The tech teams were scratching their heads for days as there was no documented link between this piece of hardware and the email system. The impact of the outage was certainly felt by the business – but had it happened at the end of the financial year, it could have impacted perhaps 10-20% of the business bookings as many deals came in at that time.

Being able to understand the link between infrastructure, cloud services, applications, databases, middleware and business processes and services is of huge value to every business – particularly as the percentage of business through digital channels and touchpoints continues to accelerate.

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.