In this Insight, guest author Anirban Mukherjee lists out the key challenges of AI adoption in traditional organisations – and how best to mitigate these challenges. “I am by no means suggesting that traditional companies avoid or delay adopting AI. That would be akin to asking a factory to keep using only steam as power, even as electrification came in during early 20th century! But organisations need to have a pragmatic strategy around what will undoubtedly be a big, but necessary, transition.”
After years of evangelising digital adoption, I have more of a nuanced stance today – supporting a prudent strategy, especially where the organisation’s internal capabilities/technology maturity is in question. I still see many traditional organisations burning budgets in AI adoption programs with low success rates, simply because of poor choices driven by misplaced expectations. Without going into the obvious reasons for over-exuberance (media-hype, mis-selling, FOMO, irrational valuations – the list goes on), here are few patterns that can be detected in those organisations that have succeeded getting value – and gloriously so!
Data-driven decision-making is a cultural change. Most traditional organisations have a point person/role accountable for any important decision, whose “neck is on the line”. For these organisations to change over to trusting AI decisions (with its characteristic opacity, and stochastic nature of recommendations) is often a leap too far.
Work on your change management, but more crucially, strategically choose business/process decision points (aka use-cases) to acceptably AI-enable.
Technical choice of ML modeling needs business judgement too. The more flexible non-linear models that increase prediction accuracy, invariably suffer from lower interpretability – and may be a poor choice in many business contexts. Depending upon business data volumes and accuracy, model bias-variance tradeoffs need to be made. Assessing model accuracy and its thresholds (false-positive-false-negative trade-offs) are similarly nuanced. All this implies that organisation’s domain knowledge needs to merge well with data science design. A pragmatic approach would be to not try to be cutting-edge.
Look to use proven foundational model-platforms – such as those for NLP, visual analytics – for first use cases. Also note that not every problem needs AI; a lot can be sorted through traditional programming (“if-then automation”) and should be. The dirty secret of the industry is that the power of a lot of products marketed as “AI-powered” is mostly traditional logic, under the hood!
In getting results from AI, most often “better data trumps better models”. Practically, this means that organisations need to spend more on data engineering effort, than on data science effort. The CDO/CIO organisation needs to build the right balance of data competencies and tools.
Get the data readiness programs started – yesterday! While the focus of data scientists is often on training an AI model, deployment of the trained model online is a whole other level of technical challenge (particularly when it comes to IT-OT and real-time integrations).
It takes time to adopt AI in traditional organisations. Building up training data and model accuracy is a slow process. Organisational changes take time – and then you have to add considerations such as data standardisation; hygiene and integration programs; and the new attention required to build capabilities in AIOps, AI adoption and governance.
Typically plan for 3 years – monitor progress and steer every 6 months. Be ready to kill “zombie” projects along the way. Train the executive team – not to code, but to understand the technology’s capabilities and limitations. This will ensure better informed buyers/consumers and help drive adoption within the organisation.
I am by no means suggesting that traditional companies avoid or delay adopting AI. That would be akin to asking a factory to keep using only steam as power, even as electrification came in during early 20th century! But organisations need to have a pragmatic strategy around what will undoubtedly be a big, but necessary, transition.
These opinions are personal (and may change with time), but definitely informed through a decade of involvement in such journeys. It is not too early for any organisation to start – results are beginning to show for those who started earlier, and we know what they got right (and wrong).
I would love to hear your views, or even engage with you on your journey!
The views and opinions mentioned in the article are personal.
Anirban Mukherjee has more than 25 years of experience in operations excellence and technology consulting across the globe, having led transformations in Energy, Engineering, and Automotive majors. Over the last decade, he has focused on Smart Manufacturing/Industry 4.0 solutions that integrate cutting-edge digital into existing operations.
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.
Organisations have relied heavily on technology to survive and succeed over the last 2 years.
Many tech providers have led the way – showing by example how strategies and technologies have to be shaped. They have also worked at improving their product and services offerings, introduced newer features and acquired companies to support market needs and grow their market share.
What should they do differently in 2022 to continue to succeed?
Ecosystm analysts think that a mere focus on products and features will not help. This is the time to focus on softer aspects such as skills, alignment with customer priorities, and an overhaul of channel programs.
Here is what Tech Providers should focus on in 2022 for continued success:
- Build relationships with Business
- Increase Automation to curb the effects of the Great Resignation
- Syndicate Skills; not just Software
- Focus on Channel Partners – and Pricing
- Be Local and Industry-Specific
Read on to find out what Alan Hesketh, Darian Bird, Niloy Mukherjee, Peter Carr and Tim Sheedy have to say to Tech Providers.
Click here to download Growing your Market Share in 2022 as a PDF.
As the leader of the tech team, CIOs are working through many different strategies and initiatives to drive new digital initiatives and improve existing ones. They are often pulled into new initiatives by business leaders and have to make hard decisions on how to support a business that is increasingly digitalised.
But there are five initiatives that all CIOs should have on their list for 2022 as they will deliver impactful results quickly and will make future investments more manageable and reliable.
In 2022, these 3 technology investments will give you a fast start:
- AIOps. This will be an easy business case to build if you evaluate the benefits
- Hybrid Cloud Management. Even if your business is racing towards the public cloud, you will have some hybrid cloud services.
- Federated Data Management. Because your infrastructure and applications will be hybrid, your data needs to be too.
These strategic initiatives will also be crucial this year:
- Resolve technical debt. Improve architectures and increase agility.
- Improve Talent Recruitment and Employee Retention. Be aware that the “great resignation” is a reality
Click here to download 5 IT Initiatives to Jumpstart Your Digital Business in 2022 as a PDF.
On 4 November Kyndryl completed the spin-off from IBM and began trading as an independent company on the New York Stock Exchange. It is effectively a USD 19 Billion start-up, and the industry will be tracking its journey keenly. Kyndryl has the ability to disrupt markets as it reinvents its business to embrace growth areas and help clients through their tech-led transformations.
Ecosystm Analysts Darian Bird, Peter Carr, Sash Mukherjee, Tim Sheedy, Ullrich Loeffler, and Venu Reddy comment on Kyndryl’s strategy going forward and the associated opportunities.
To download this Vendorsphere as a pdf for offline use, please click here.
AI has become intrinsic to our personal lives – we are often completely unaware of technology’s influence on our daily lives. For enterprises too, tech solutions often come embedded with AI capabilities. Today, an organisation’s ability to automate processes and decisions is often dependent more on their desire and appetite for tech adoption, than the technology itself.
In 2022 the key focus for enterprises will be on being able to trust their Data & AI solutions. This will include trust in their IT infrastructure, architecture and AI services; and stretch to being able to participate in trusted data sharing models. Technology vendors will lead this discussion and showcase their solutions in the light of trust.
Read what Ecosystm analysts, Darian Bird, Niloy Mukherjee, Peter Carr and Tim Sheedy think will be the leading Data & AI trends in 2022.
Click here to download Ecosystm Predicts: The Top 5 Trends for Data & AI in 2022 as PDF
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.
Technologies to automate IT systems and relieve over-stretched IT operations teams have been moving into the mainstream over the last few years. Several factors, driven by the digital era, have made this necessary. Firstly, digital transformation is creating ever-larger IT environments and volumes of data that cannot be managed by manual processes. These distributed systems are also becoming more complex, incorporating IoT, mobile, multi-cloud, containers, and APIs. Moreover, for digital businesses, the financial impact of an outage makes time to resolution critical. Identifying and remediating issues before they affect the user is now paramount. AIOps provides intelligence to the IT operations team that allows them to proactively resolve events before they become outages.
Augmenting IT Operations with AIOps
AIOps allows IT operations teams to not only ensure observability of their systems and reduce noise but to also understand how events are interacting together to affect performance and take corrective action quickly. The primary features of AIOps are:
- Noise reduction. AIOps ingests systems data, surfaces priority anomalies and correlates them together. This brings the number of incidents to investigate back down to a human level. Rackspace recently announced that AIOps helped it reduce alert noise by 99% during the initial stage of its rollout. Successful vendor references typically cite similar figures between 95-99%.
- Root cause analysis. Once priority events have been correlated, AIOps identifies a root cause to enable the operations team to focus its efforts on a resolution. This is a task that proves challenging to perform at speed for a human operator considering the complexity of today’s systems.
- Proactive response. A range of responses is available with AIOps, from directing issues to the appropriate people, to recommending actions that can be taken by operators directly in a collaboration tool, to rules-based workflows performed automatically, such as spinning up additional AWS EC2 instances.
- Learning. By evaluating past failures and successes, AIOps can learn over time which events are likely to become critical and how to respond to them. This brings us closer to the dream of NoOps, where operations are completely automated.
The Impact of COVID-19 on IT Operations
The Ecosystm Digital Priorities in the New Normal study launched this month, asks technology users about how their digital priorities have shifted during the pandemic. Despite pressure to shift to digital delivery, almost 40% of participants reported that their organisations cut headcount in the IT department (Figure 1). Furthermore, over one third had been forced to cut their employees’ salaries. As we have seen in previous crises, IT operations teams are being asked to do more with less and will need automation to bridge the gaps.
As we begin to move into the next phase of the COVID-19 reality and businesses continue to open, we will see many launch digital services that were conceived of during the crisis. One of the greatest challenges that IT departments face will be scalability as digital businesses grow. AIOps will be a go-to tool for IT operations to ensure uptime and improve user experience. It is likely that the next 12-18 months will be a watershed moment for AIOps.
NLP and the Democratisation of Data
Natural Language Processing (NLP) will be the next string in the bow of AIOps. While the ultimate goal of IT operations is to identify and remediate situations before they have an impact on the user, oftentimes it is the service desk that generates the initial barrage of alerts. AIOps equipped with NLP can extract relevant data from user tickets, correlate them with other system events and potentially even suggest a resolution to the user. Here, ChatOps can help to reduce the workload on the service desk and bring relevant events to the attention of the operations team faster. NLP will also help democratise IT operations data within the organisation. As they digitalise, lines of business (LoBs) besides IT will need access to system health and user experience data but business managers may not have the necessary technical skills to extract them. Chatbots that can return these metrics to non-technical users will begin to proliferate.
AIOps Recommendations
Most IT departments would have discovered the limitations of their current systems during the upheaval caused by recent lockdowns. Only about 7% of organisations in our study reported that they were well-prepared across all areas of IT, to handle the COVID-19 crisis. For those organisations that have yet to invest in AIOps, we recommend starting now but starting small. Develop a topology map to understand where you have reliable data sources that could be analysed by AIOps. Then select a domain by assessing the present level of observability and automation, IT skills gap, frequency of outages, and business criticality. As you add additional domains and the system learns, the value you realise from AIOps will grow.
The power of collaborative AIOps tools would have been undeniable as the COVID-19 crisis began and IT departments were forced to work in a distributed manner. When evaluating a system, carefully consider how it will integrate into your organisation’s preferred collaboration suite, whether it be the AIOps vendor’s proprietary situation tool or a third-party provider like Slack or Microsoft Teams. The ability for operations teams to collaborate effectively reduces time to resolution.