Software may be eating the world, but for every £1 spent on enterprise software, £3 is still spent on IT Services — consulting, system integration and outsourcing. To understand Artificial Intelligence (AI) — today’s most important enabling technology —we must, therefore, understand the tectonic shifts occurring in the market for AI-related IT Services. We’ve undertaken 52 calls with IT Service buyers and providers, and examined market data. Below, we highlight the seven dynamics shaping the AI Services market — from the ‘convergence’ of software and IT Service companies to the era of specialisation.
AI Service providers help mid-size buyers and enterprises with a variety of initiatives, from chatbot implementations to the provision of AI-powered analytics (‘AI Analytics’). Our analysis focuses on IT Service companies that enable AI Analytics — a core use case for AI.
AI Analytics refers to the application of machine learning techniques to enterprise data to derive insight. AI analytics is frequently applied to:
— enterprise resource data, such as inventory and order management information, to derive business intelligence; and:
— data from business functions, to improve performance – for example, analysis of marketing information to improve customer segmentation and churn prediction.
1. A nascent market
The market for AI Services is nascent — because adoption of AI itself is at an early stage within the enterprise.
There is a gulf between buyers’ awareness of AI and their understanding of the technology. Given media attention on AI and vendor marketing of the technology, awareness of AI technology among executives is high — an ‘8 out of 10’ — at mid-size ($200m-$1B revenue) and large ($1B+ revenue) companies. Buyers’ understanding of AI is low, however — a ‘3 out of 10’. AI technology principles, use cases and deployment methodologies are poorly understood.
“Even among CIOs, understanding of AI is extremely low.” (CXO, global consumer packaged goods company)
While understanding of AI is limited, there is considerable appetite for investment in AI as buyers seek to unlock value from data and avoid being left behind by competitors.
While positive about the potential for AI, many executives express some nervousness around undertaking AI initiatives given suppliers’ failure to articulate solutions to specific business problems, difficulty demonstrating ROI, over-promising by suppliers and the failure of some high-profile projects. In addition to this, many buyers are still implementing or consolidating previous investments in core data management, including data lakes and reporting tools. Many have significant data collection, consolidation and harmonisation challenges they seek to address before investing in advanced (AI-powered) analytics. The market for AI Services is early, therefore, with most buyers in a ‘test and learn’ phase. Almost every engagement begins with a proof-of-concept project. Speaking with CXOs reminds us how early we are in paradigm shift that will unfold within the enterprise during the coming decade and beyond.
“Buyers feel there’s value, but are nervous around making bets.” (CXO, global consumer products company)
To unlock value in the market, providers must deliver a tangible return on investment (ROI). Whether impacting direct drivers of revenue (uplift, conversion, yield or price) or reducing a company’s excess spend or resource requirements, a provider’s results will be assessed against the buyer’s existing process and key performance indicators. To their cost, AI Service providers sometimes offer ‘AI’ without articulating business value. In a market driven by measurable results, not perceived gains, companies that deliver tangible benefits will enjoy a competitive advantage.
2. A desire to outsource
As mid-size companies and enterprises experiment with AI, most plan to include at least an element of outsourcing to an AI Service provider to achieve their goals, powering growth in the AI Services market. Companies lack the AI-specific skills to ‘go it alone’, seek experts to deliver early wins during experimentation and test-and-learn cycles, and cannot re-deploy existing staff without slowing other initiatives.
“We have our core skill set…and then the things we outsource”. (Product manager, global equipment supplier)
During the next three years, we expect most companies to work with third parties in the data science initiatives they undertake. Small and mid-size companies are inclined to outsource entire AI initiatives, given resource constraints and risks associated with hiring in-house data science teams. Large enterprises, on the other hand, tend to adopt a ‘hybrid’ approach — engaging with outsourced providers while developing their in-house data science capabilities.
In the longer term, mid-size companies will remain particularly heavy users of third party AI capabilities. Given the difficulty and cost of hiring and integrating strong AI teams, and the capability of outsourcers offering best-in-class solutions for specific domains, AI Service companies will be valuable partners.
3. Rapid market growth
While today’s market for AI Services is nascent, we expect it to grow rapidly as buyers gain confidence and proofs-of-concept mature into broader deployments. Spend on general analytics services is vast — $60B annually — and growing rapidly at 20% per year to reach over $100B by 2020. Today, however, less than 5% of this is spent on AI-powered analytics services. By 2020 we expect this percentage to more than triple and establish a large, multi-billion dollar market as:
- clients increase, to over 40%, the proportion of their general analytics budgets they invest in advanced analytics, as their core deployments mature and they seek advanced capabilities for competitive advantage;
- use of AI for advanced analytics grows from a negligible base to comprise a large minority — perhaps 40% — of advanced analytic deployments, as AI becomes a cornerstone technology.
4. Convergence and consolidation
A powerful trend of ‘convergence’ is reshaping the market for AI Services. Effective software companies are strengthening their service capabilities to enable broader, more successful deployments. Meanwhile, service companies are developing and acquiring technology assets, from tools to full-blown applications, to access client opportunities and reduce the cost to serve.
“We’re 80% revenue from services, 20% from software licenses. But that won’t give you the full picture since the products are super-critical.” (CXO, AI Service company)
In addition to developing technical assets, companies are acquiring them. In 2015 alone, consulting behemoth McKinsey acquired advanced analytics companies 4Tree (price and promotion optimisation for consumer goods), VisualDoD (analytics for the defence industry) and QuantumBlack (analytics for organisational performance).
In this fragmented market, in which global consulting companies and system integrators are complemented by boutique and mid-size AI Service providers, we expect extensive consolidation in the years ahead. Boutique and mid-size vendors, with best-of-breed capabilities in specific business functions or sectors and high quality AI expertise, will be attractive targets to global vendors seeking stronger competencies and AI personnel. For boutiques and mid-size vendors this is a double-edged sword. Some will achieve attractive exits while others will be left behind, sub-scale, in a consolidating market.
5. The rise of managed services
The delivery model for AI Services is changing. Most large AI Service companies offer clients the option of either:
- a managed service deployment (a cloud service in which fees are paid monthly for ongoing access to a remotely hosted capability); or
- a time and materials model (a project of defined specification, cost and length — after which the engagement ends).
To date, demand for managed service deployments has been limited. We estimate that less than 25% of AI Service companies’ revenue is derived from managed service deployments, reflecting the preference of enterprise customers for time and materials engagements.
Demand for managed service deployments, however, is likely to double in the medium term, to comprise up to 50% of engagements. As with cloud adoption generally, mid-size companies lead the way. The lower up-front cost, increased flexibility, ongoing help, and perpetual technology updates from the managed service provider are highly valued by smaller buyers taking their first steps with the technology.
“I would prefer it on a subscription basis, certainly initially. In a subscription model, the tech always evolves.” (Product manager, global equipment supplier)
6. Fierce competition above the mid-market
Despite the early stage of the AI Services market, competition will be fierce for contracts of over £150,000 per year, as select global vendors position themselves for mid-size contracts and mid-size AI Service providers offer attractive value and specialisation.
Global system integrators, consulting companies and professional service firms that compete for the largest (£10m to £100m per year) general analytics services contracts today are repositioning for strength in AI. Companies including Accenture, Atos, CapGemini, Cognizant, Deloitte, EY, IBM, Infosys, KPMG, McKinsey, Palantir, PwC, TCS and Wipro have developed multi-billion dollar traditional analytics practices, typically with 5,000 to 15,000 in-house analytics professionals. On average, however, less than 8% of the companies’ analytics personnel are data scientists. Firms are investing heavily to increase the size of their data science teams but progress varies; we estimate an average of 1,200 data scientists per company, but some have as few as 100. Speed will be important; global vendors have the potential to collect customer data sets that dwarf the size of their smaller competitors and offer data network effects — more data delivers better algorithms, whose improved results attract more customers and data.
Significantly, while some large vendors focus on board level engagements and multi-year global transformation projects worth hundreds of millions of pounds, others engage with an enterprise’s IT group and, to an increasing extent, target smaller, project-based analytics engagements starting at £300,000 per year. As a result, some global vendors pose a growing competitive threat to mid-size providers.
Mid-size providers, including Mu Sigma, Fractal Analytics, Cartesian and Opera Solutions have significant revenue (typically £20m-£200m), sizeable workforces and a presence in multiple territories and sectors. By being early to market, offering strong machine learning expertise and developing powerful specialisations around specific sectors and/or business functions, mid-size providers compete effectively for AI service deals worth £150,000 to £1m per year. While well positioned to benefit from increasing adoption of AI Services, they face pressure from select global vendors moving down-market, and boutique vendors pressuring from below.
Given the early stage of AI adoption among buyers, there is an attractive opportunity for boutique vendors to capture smaller (<£150,000 per year) initial contracts with mid-size buyers, or with larger companies taking their first steps in AI Services. There are numerous boutique vendors globally, and while some are lifestyle businesses modest in scale, others have the ambition and capability to grow into mid-tier winners. One of the fast-growing companies we identified in our UK market map is Peak, which combines a machine learning technology platform with best-in-class data scientists to service the above need. Our discussions with mid-size AI Service buyers highlight their openness — often, their preference — to engage with smaller AI Service providers. Select boutiques offer buyers an accessible path to AI by delivering a more responsive relationship, best-of-breed specialisation in a business function, stronger machine learning expertise and lower cost than large vendors.
While the boutiques’ market for contracts under £150,000 per year is less contested, boutiques must contend with limited marketing budgets that can inhibit their presence in a fragmented market, may be left behind as competitors consolidate, and will face a materially more challenging competitive environment if they move up-market. Finally, as software companies from Google to Salesforce increase the AI they build into their software platforms and applications, the ‘on-ramp’ to AI will become ever-gentler — potentially lessening the need for small companies to engage third party help to take advantage of AI in select use cases.
Increasingly, AI Service providers are specialising — focusing their competencies on specific verticals (e.g. retail), business functions (e.g. marketing) or business sub-functions (e.g. customer segmentation).
Large buyers, in particular, value suppliers offering specialisation given the supplier’s expertise in the customer’s vertical or business challenge, their larger cross-customer data sets for optimising machine learning algorithms, and peer referenceability in the customer’s vertical. In the short term, specialisation is becoming a success factor to win market share among large enterprise customers. In the longer term, buyers will demand greater specialisation as buyers’ own in-house data science capabilities mature.
Smaller enterprise and mid-market customers that are experimenting with AI, seeking an ‘on-ramp’ to the technology, and have limited budgets, prioritise other selection criteria. Smaller buyers value vendors that rapidly addresses a tangible business problem to deliver initial ROI, deliver a high touch service to guide the buyer through the ‘test and learn’ phase, offer geographic proximity, provide flexible pricing for initial deployments, and have the ability to integrate easily with existing IT systems for data extraction and processing.
The end of the beginning
While early and modestly-sized today, the AI Services market is poised for rapid growth. As buyers seek value, through AI, from historic investments in data collection, AI Services will offer a multi-billion dollar opportunity by 2020. Competition is accelerating to match. For large deals, global service firms will compete by leveraging their data and data science personnel. Mid-size deals will represent a second battleground, with mid-tier vendors competing with one another, and facing pressure from above and below. Specialisation will be a weapon of choice. For smaller deals, select boutiques will offer buyers the right success factors — accessibility, flexibility and low cost — to achieve scale and mature into mid-size vendors.
At all levels, the line between service providers and software companies will blur; in the age of data, every company is enabled by technology assets and data management expertise.
By 2020, AI will permeate analytics and the service providers that support its adoption. Today, however, we are witnessing AI engagement in the enterprise spread from early adopters to the early mainstream. Today, we are witnessing the end of the beginning.