Over the past year we have seen global systems integrators (SIs) – Accenture, IBM, Deloitte, Fujitsu, Capgemini and others – make many acquisitions, particularly in the public cloud, AI, cybersecurity and data space. Much of the growth in spending over the past few years have been driven by these categories: in 2020 if a software company was purely or mainly SaaS, they are likely to have witnessed strong growth. If they were on-premises software, they were lucky not to see declining revenues. While it is normal for the larger SIs and consultants to play catch up through acquisition, it is becoming harder for them to gain traction in these new areas.
Technology Shifts Drive Market Fragmentation
With every technology-driven business change new SIs, consultants, and managed services providers emerge. It happened with the move to big ERP systems, the move towards Business Intelligence, the emergence of SaaS etc. But I think we are now seeing something different. More than just the smaller players going after opportunities earlier, I believe we are seeing a changing buying behaviour from tech and business buyers – a greater willingness for larger enterprises to give their most important, business-critical strategies and implementations to smaller, less established players.
And I am not suggesting that the larger SIs are not performing well. Many are growing at 10-25% YoY – but at the same time, many are also growing at a slower rate than the markets they play in. The Ecosystm RNx for global IT services and consulting providers shows that the global providers continue to power ahead. But they need to adapt to changing market conditions.
New Cloud/AI Partners Winning Consulting and Implementation Deals
We have seen a new community of partners emerge with tech changes, such as the hyperscale cloud platforms and AI/machine learning tools. Traditionally, these companies would be good at one thing – and would learn slowly. For example, in the SAP ERP growth period, the projects were large and long. A single, mid-sized SI might only be working with 2-3 clients at a time. Therefore, the IP that they collected was limited – and they would find themselves with focused or niche skills. The large SIs had done many large, long projects across the globe and had much best-practice IP to call upon, giving them a broader and deeper knowledge of the technology and industries. Smaller providers had limited IP and industry experience.
But in this cloud and AI era, specialist providers work on hundreds of smaller projects with dozens or hundreds of clients. With the technology constantly evolving, the skills are constantly improving. While the global SIs are working on many cloud and AI engagements, they are often part of longer engagements – giving the consultants and tech teams less exposure to the new and evolving cloud platforms.
In a world where technology is changing at pace, the traditional global SI practice of “learning from peers across the globe” doesn’t happen at the pace the market requires. By the time your peers in the business have completed a project, documented it, and shared learnings, the market has moved on and technology has changed. Today it is easier and faster to learn directly from the tech vendors and cloud platform providers and their training partners. The network effect of knowledge in a team on the opposite side of the globe for a global SI is less valuable to clients. Often the smaller and mid-sized SIs have a deeper, broader knowledge of the technology platforms and toolsets than the larger providers – giving them a competitive advantage. For example, if you want the actual experience of moving SAP to Azure, or Oracle to AWS – you’ll often find the smaller providers have more experience. And this continues to play out. In many markets in the world, the top 5-10 SIs for cloud, AI and cybersecurity has a high proportion of local specialist providers.
Tech Buyers No Longer Look for Culturally Aligned Partners
Tech buyers themselves are changing too. In years gone by, the smaller tech partners would tell us that they felt they were included in bids to drive down the price from the global SIs. But today the story is different. Smaller partners are admired for their agility and innovation. Large enterprise customers will choose small providers because the small SI is NOT like them. In the past, they chose the global SI because they were just like them!
Because of this, the large SIs are mopping up their smaller competitors across the globe. Accenture has acquired 40 companies in the past 10-11 months, IBM has acquired over 10, Atos and Cognizant have also acquired many companies in the past 12 months. They are doing this for the skills as much as for the clients, along with getting a foothold in a new market or strengthening their position in geography. The challenge will be to hang on to the clients, culture, and the IP of the acquired business. Often these smaller competitors are growing at a significant pace – and the biggest risk is that the acquiring company takes their eyes off the prize.
Global SIs Still Own the Industry Play
Despite these challenges, one of the areas that the global SIs will continue to dominate is the industry play. I have discussed how as technologies mature, industry plays become more relevant.
Smaller and mid-sized SIs and consultants find it hard to create deep pools of expertise across multiple industries. While some may have a deep focus on a single or two industries, only the large players have broad and deep geography and industry experience. This puts many of the acquisitions into context – the global SIs will take these acquisitions and use that deep and broad technical and business knowledge and add it to their industry knowledge to create a more compelling offering.
Their challenge will still be one of cultural alignment. As discussed, many companies seek out tech partners who represent what they want to be, not what they are. The ability for the Global SIs to retain the culture, agility and innovation of the acquired business will determine their ability to continue to see similar or improved levels of growth from the acquired business. Using their IP in the context of industries will be the key to their ongoing success.
The Ecosystm RNx – Top 10 Global IT Services & Consulting Company Rankings is based on in-depth, quantified ratings from technology decision-makers on the Ecosystm platform.
If you are an End User, it is likely that you are looking to partner with the right services provider who can guide your transformation journey. This vendor ranking will help you evaluate your buying decisions based on key evaluation ratings by your peers across a number of key metrics and benchmarks, including customer experience, integration capabilities and strategy.
If you are an IT Services & Consulting Company, you operate in a competitive world with several global and regional players – this is an opportunity to understand how your customers rate you on capabilities and their overall customer experience.
Ecosystm RNx is an objective vendor ranking based on in-depth, quantified ratings from technology decision-makers on the Ecosystm platform. In this edition, we rank the Top 10 Global AI & Automation Vendors.
If you are an End-User, you are realising that the right investments in Data & AI now will be the key to your future success. This vendor ranking will help you evaluate your buying decisions based on key evaluation ratings by your peers across a number of key metrics and benchmarks, including customer experience.
If you are an AI & Automation Vendor, it’s an opportunity to understand how your customers rate you on capabilities and their overall customer experience.
AWS has been busy this year moving beyond its stronghold of public cloud to bring infrastructure closer to the enterprise and ultimately to where the end user needs computing most. The global availability of AWS Outposts, essentially AWS on prem, the launch of AWS Wavelength, edge computing embedded in 5G networks, and the extension of the AWS Snow Family of edge devices, have all combined to create a compelling hybrid cloud story. This evolution in AWS’ strategy has required a maturing of its partner ecosystem, building alliances with telcos, co-location providers, and integrators that are all still trying to cement their roles in the hybrid cloud space.
Outposts: The AWS Vision of Hybrid Could
Outposts launched late last year with availability extended to many mature countries in January 2020, in addition to India, Malaysia, New Zealand, Taiwan, Thailand, Israel, Brazil, and Mexico in June. The plug and play system delivers AWS compute and storage from the organisation’s own data centre with a rack that requires only power and network access. The system is managed with the same tools and APIs used in public AWS regions, providing a single hybrid cloud management console. Outposts is targeted primarily at the enterprise space, with the cheapest development and testing units coming in at $7-8k monthly or around $250-280k upfront, depending on the country. Other higher-end configurations include general purpose, compute optimised, graphics optimised, memory optimised, and storage optimised. Monthly installments attract a 10-15% premium over upfront payments.
The launch of hybrid cloud solutions by the major cloud providers and containerised services that allow workloads to be deployed in public and private environments will ensure enterprises are willing to continue their cloud journeys. Security concerns and data residency regulations have prevented many organisations from shifting sensitive workloads to the cloud. Moreover, as industries launch new customer-facing digital services or transform their manufacturing systems, latency will become a concern for some workloads. Hybrid cloud addresses each of these issues by employing either public or private resources depending on the data, location, or capacity needs.
AWS Outposts has two variants, namely Native AWS and VMware Cloud on AWS. Organisations already heavily invested in the AWS ecosystem will likely choose Native AWS and use Outposts as a means of migrating further workloads that require an on-prem environment over to a hybrid cloud environment. More traditional organisations, such as banks, may select the VMware Cloud on AWS variant as a means of retaining the same operational experience that they are accustomed to in their existing VMware environments today.
AWS will rely heavily on its network of enterprise partners for sales, management, and maintenance services for Outposts. AWS partners like Accenture, HCL, TCS, Deloitte, DXC, NTT Data, and Rackspace have all shifted in recent years to deliver the full stack from infrastructure to application services and now have a ready-made hybrid cloud platform to migrate on to. AWS is also in the process of recruiting co-location partners to serve Outposts from third-party data centres, providing another option that enterprises are familiar with. This will likely come as welcomed news for co-location providers that have been fighting uphill against AWS.
Wavelength: Embedding Cloud in 5G Networks
Another major announcement in AWS’s drive towards hybrid cloud and edge computing was the general availability of Wavelength in August. This service embeds AWS into the data centres of 5G network operators to reduce latency and bandwidth transmission. Data for applications residing in Wavelength Zones is not required to leave the 5G network. AWS is looking to attract mobile operators, who previously might have viewed it as a competitor while the public cloud space was more fragmented and open to telcos. These partnerships are another example of AWS expanding its ecosystem. Current Wavelength partners are Verizon, Vodafone Business, KDDI, and SK Telecom. With their own take on edge services, Microsoft has signed up the likes of Telstra and NTT Communications, while Google has enlisted AT&T and Telefónica. Edge computing in 5G networks will be the next battleground for cloud supremacy.
On a smaller scale, AWS has released new additions to its Snow Family of edge computing devices. AWS Snowcone is a compact, rugged computing device designed to process data on the network edge where cloud services may be insufficient. The processed data can then be uploaded to the cloud either through a network connection or by physically shipping the device to AWS. The convergence of IT and OT will drive the need for these edge devices in remote locations, such as mines and farms and in mobile environments for the healthcare and transportation industries.
Competitive Strategies
Openness will become a critical difference between how cloud platform providers approach hybrid cloud and edge computing. While AWS is certainly extending its ecosystem to include partners that it previously would have viewed as rivals, as the dominant player, it will be less compelled to open up to its largest competitors. If it can control the full system from ultraportable device, to $1M server rack, to cloud management console, it can potentially deliver a better experience for clients. Conversely, the likes of Microsoft, Google, and IBM, all need to be willing to provide whichever service the client desires, whether that is an end-to-end solution, management of a competitor’s cloud service, or an OEM’s hardware.
In our blog, Artificial Intelligence – Hype vs Reality, published last month we explored why the buzz around AI and machine learning have got senior management excited about future possibilities of what technology can do for their business. AI – starting with automation – is being evaluated by organisations across industries. Several functions within an organisation can leverage AI and the technology is set to become part of enterprise solutions in the next few years. AI is fast becoming the tool which empowers business leaders to transform their organisations. However, it also requires a rethink on data integration and analysis, and the use of the intelligence generated. For a successful AI implementation, an organisation will have to leverage other enabling technologies.
Technologies Enabling AI
IoT
Organisations have been evaluating IoT – especially for Industry 4.0 – for the better part of the last decade. Many organisations, however, have found IoT implementations daunting for various reasons – concerns around security, technology integration challenges, customisation to meet organisational and system requirements and so on. As the hype around what AI can do for the organisation increases, they are being forced to re-look at their IoT investments. AI algorithms derive intelligence from real-time data collected from sensors, remote inputs, connected things, and other sources. No surprise then that IoT Sensor Analytics is the AI solution that is seeing most uptake (Figure 1).
This is especially true for asset and logistics-driven industries such as Resource & Primary, Energy & Utilities, Manufacturing and Retail. Of the AI solutions, the biggest growth in 2020 will also come from IoT Analytics – with Healthcare and Transportation ramping up their IoT spend. And industries will also look at different ways they can leverage the IoT data for operational efficiency and improved customer experience (CX). For instance, in Transportation, AI can use IoT sensor data from a fleet to help improve time, cost and fuel efficiency – suggesting less congested routes with minimal stops through GPS systems, maintaining speeds with automated speed limiters – and also in predictive fleet maintenance.
IoT sensors are already creating – and will continue to create large amounts of data. As organisations look to AI-enabled IoT devices, there will be a shift from one-way transactions (i.e. collecting and analysing data) to bi-directional transactions (i.e. sensing and responding). Eventually, IoT as a separate technology will cease to exist and will become subsumed by AI.
Cloud
AI is changing the way organisations need to store, process and analyse the data to derive useful insights and decision-making practices. This is pushing the adoption of cloud, even in the most conservative organisations. Cloud is no longer only required for infrastructure and back-up – but actually improving business processes, by enabling real-time data and systems access.
Over the next decades, IoT devices will grow exponentially. Today, data is already going into the cloud and data centres on a real-time basis from sensors and automated devices. However, as these devices become bi-directional, decisions will need to be made in real-time as well. This has required cloud environments to evolve as the current cloud environments are unable to support this. Edge Computing will be essential in this intelligent and automated world. Tech vendors are building on their edge solutions and tech buyers are increasingly getting interested in the Edge allowing better decision-making through machine learning and AI. Not only will AI drive cloud adoption, but it will also drive cloud providers to evolve their offerings.
The global Ecosystm AI study finds that four of the top five vendors that organisations are using for their AI solutions (across data mining, computer vision, speech recognition and synthesis, and automation solutions) today, are also leading cloud platform providers (Figure 2).
The fact that intelligent solutions are often composed of multiple AI algorithms gives the major cloud platforms an edge – if they reside on the same cloud environment, they are more likely to work seamlessly and without much integration or security issues. Cloud platform providers are also working hard on their AI capabilities.
Cybersecurity & AI
The technology area that is getting impacted by AI most is arguably Cybersecurity. Security Teams are both struggling with cybersecurity initiatives as a result of AI projects – and at the same time are being empowered by AI to provide more secure solutions for their organisations.
The global Ecosystm Cybersecurity study finds that one of the key drivers that is forcing Security Teams to keep an eye on their cybersecurity measures is the organisations’ needs to handle security requirements for their Digital Transformation (DX) projects involving AI and IoT deployments (Figure 3).
While AI deployments keep challenging Security Teams, AI is also helping cybersecurity professionals. Many businesses and industries are increasingly leveraging AI in their Security Operations (SecOps) solutions. AI analyses the inflow and outflow of data in a system and analyses threats based on the learnings. The trained AI systems and algorithms help businesses to curate and fight thousands of daily breaches, unsafe codes and enable proactive security and quick incident response. As organisations focus their attention on Data Security, SecOps & Incident Response and Threat Analysis & Intelligence, they will evaluate solutions with embedded AI.
AI and the Experience Economy
AI has an immense role to play in improving CX and employee experience (EX) by giving access to real-time data and bringing better decision-making capabilities.
Enterprise mobility was a key area of focus when smartphones were introduced to the modern workplace. Since then enterprise mobility has evolved as business-as-usual for IT Teams. However, with the introduction of AI, organisations are being forced to re-evaluate and revamp their enterprise mobility solutions. As an example, it has made mobile app testing easier for tech teams. Mobile automation will help automate testing of a mobile app – across operating systems (Figure 4). While more organisations tend to outsource their app development functions today, mobile automation reduces the testing time cycle, allowing faster app deployments – both for internal apps (increasing employee productivity and agility) and for consumer apps (improving CX).
CX Teams within organisations are especially evaluating AI technologies. Visual and voice engagement technologies such as NLP, virtual assistants and chatbots enable efficient services, real-time delivery and better customer engagement. AI also allows organisations to offer personalised services to customers providing spot offers, self-service solutions and custom recommendations. Customer centres are re-evaluating their solutions to incorporate more AI-based solutions (Figure 5).
The buzz around AI is forcing tech teams to evaluate how AI can be leveraged in their enterprise solutions and at enabling technologies that will make AI adoption seamless. Has your organisation started re-evaluating other tech areas because of your AI requirements? Let us know in the comments below.