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.
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.
4.7/5 (3) 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
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.
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.
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