University-SME Platform

University-SME Platform
A two-sided market connecting expert
university researchers & solutions to
small and medium sized enterprises (SME) for
artificial intelligence (AI) projects and services

A report prepared by the AI Innovation Network, WBS
& Coefficiency Lab

Version 3.0

Reader note: this document is subject to continuous change and refinement. Please ensure
that you have the latest version before relying on information contained within it.

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Overview
Artificial intelligence (AI) represents a huge new revenue source for key service industries,
including Small and Medium sized Enterprises (SME). Despite this potential, AI technologies
are often difficult to understand and implement and remain largely within the realm of
academic research.
On the one hand, SME can improve their productivity and growth with AI, however evidence
suggests that, in the UK, these enterprises often fail to implement new technologies and
practices because they find it difficult to assess their value and cannot find good advice.
On the other hand, academic research on AI tends be faced by a “Lost in Translation”
problem, which refers to the fact that almost no managers turn to academic journals for
advice on how to improve their skills or practices; and a “Lost Before Translation” problem,
which refers to the tendency for academic researchers to design studies without input from
managers or employees.
In this report, we propose that the two challenges described above, one faced by SME and
one faced by university researchers, can be set to work against each other in a two-sided
market. A university-SME platform (U-SME) can act as an intermediary by helping to
efficiently match SME demand for AI project-based work to university researchers who can
supply such work at much lower cost than consultancy firms. The U-SME platform can
provide access to on-demand projects and hosted solutions over successive stages of service
delivery.
The benefits for both SME and university researchers provided by the U-SME platform will
be potentially huge. These include independent, objective business advice for SME, at lower
fees and with more transparency over pricing options; more opportunities to university
researchers to tap into this market to generate funding for more impactful research, while
also leading to the commercialisation of research.

This report provides a preliminary proposal as to how a university-SME platform can be set
up, while outlining the benefits to be gained for both SME and university researchers.

3
Table of Contents
Overview __________________________________________________________________ 2
Table of Contents ___________________________________________________________ 3
Introduction _______________________________________________________________ 4
The Challenge of Slow AI Adoption by SME __________________________________________ 4
Figure 1. Digital Adoption in UK Micro-Businesses 2012-2018 __________________________________ 4
Figure 2. Adoption of AI and Robotics by Business Size ________________________________________ 5
The Challenge of Achieving Impact by University Researchers ___________________________ 6
The Solution – A Two-Sided Market
_____________________________________________ 7
Figure 3. A Two-Sided Market between SME and University Researchers _________________________ 7
Phase 1 – On Demand Projects ____________________________________________________ 8
Figure 4. U-SME Process: Phase 1 - On Demand Projects
_______________________________________ 8
Phase 2 – Hosted Services ________________________________________________________ 9
Figure 5. U-SME Process: Phase 2 – Hosted Services _________________________________________ 10
Benefits __________________________________________________________________ 12
Benefits for Buyers - SME
________________________________________________________ 12
Benefits for Providers –University Researchers ______________________________________ 12
Next Steps ________________________________________________________________ 14
References ________________________________________________________________ 15

4
Introduction
The Challenge of Slow AI Adoption by SME
Artificial Intelligence (AI) has the potential to increase GDP by 30% over the next few
decades and will radically transform over 50% of human jobs [1]. The transformational
impact of AI technologies is much greater in comparison to earlier general-purpose
technologies [2]. Indeed, AI represents a huge new revenue source for key service
industries, including Small and Medium sized Enterprises (SME). Data shows that advances
in AI and automation can help transform SME, by improving their productivity and growth
[3,4].
SME are a vital part of the economy, accounting for 60 per cent of all private sector jobs and
47 percent of revenue [5]. Evidence suggests, however, that SME in the UK often fail to
improve because they do not know about new technologies and practices, find it difficult to
assess the value of new innovations and cannot find good advice [6]. Research by the
Behavioural Insights Team (BIT) for the Department of Business, Energy and Industrial
Strategy (BEIS) involving interviews with 60 SME indicates that a core challenge is “SME are
often overloaded” and do not have the time to understand, assess and access new
technologies [7]. Only a quarter of SME in the UK seek external advice every year [7],
despite good evidence that good business advice can improve SME performance [8].
As evident by a recent survey [9], there are currently 1.11 million micro-businesses (with 1-9
employees) in the UK, employing around 4.09 million people (17.6 per cent of the
workforce). This group of firms accounts for £552 billion in sales, 14.7 per cent of that by all
UK firms.
Figure 1. Digital Adoption in UK Micro-Businesses 2012-2018

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Source [9].
As evident from Figure 1 above, digital adoption among these businesses has increased
sharply in recent years with web-based accounting software and cloud computing used by
more than 40%. However, only 9% of these businesses use machine learning technologies,
and only 3% are using AI. Almost one in four firms (25.3%) use none of these digital
technologies.
Another report [10], presented results of a 2017 YouGov poll of 1,111 business leaders
about their adoption of AI and robotics. The results shown in Figure 2 below indicate that
only 14% of these business leaders said they had invested in or were about to invest in
these technologies [10]. It is also striking that small businesses are considerably less likely
than their larger counterparts to have embraced AI and robotics, with just 4% falling into
this category compared with 28% of large businesses [10].
Figure 2. Adoption of AI and Robotics by Business Size
Source [10].
As reported elsewhere, the key hurdles for overcoming this slow adoption of AI according to
the business leaders surveyed, are cost and a difficulty to assess the value of digital
technologies through trusted business advice.

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The Challenge of Achieving Impact by University Researchers
The impact that information systems and management research has on business practice is
a topic of ongoing debate [11-16]. The debate centers around the challenge to produce
knowledge that is both academically rigorous and relevant to practicing managers.
There are two problems that contribute to this challenge [16]. The first is the “Lost in
Translation” problem, which refers to the fact that almost no managers turn to academic
journals for advice on how to improve their skills or practices. Researchers have found that
managers tend to be unaware of research-supported management insights reported in
academic journals, and that such insights are typically excluded in practitioner-oriented
journals. The second is the “Lost Before Translation” problem, which refers to the tendency
for academic researchers to design studies without input from managers or employees —
the populations that their studies’ results are meant to help.
More recent commentary advocates for three main changes [17]. First, business schools
should not just measure impact from within academia but outside it as well. Second,
academics should focus on conducting research that positively impacts business and society.
Third, given that any research study is part of an ecosystem, it is incumbent upon all
stakeholders —researchers, funding agencies, government, and practicing managers — to
work together in a concerted way to encourage and reward impactful research. Potential
advantages of these changes include a broader set of consumers who use academic
research, including managers, employees, consumers, and policy makers; and an increased
likelihood that research topics and study designs will incorporate input from those same
populations.
In the UK, the four higher education funding bodies allocate about £2 billion per year of
research funding to UK universities on the basis of quality through a periodic exercise
known as the Research Excellence Framework (REF). The overall quality of UK universities is
derived from three elements – outputs, impact and environment. “Outputs are the product
of any form of research,” and include publications as well as outputs disseminated in other
ways such as designs, performances and exhibitions; “impact”, is defined as “any effect on,
change or benefit to the economy, society, culture, public policy or services, health, the
environment or quality of life, beyond academia”; and “environment refers to the strategy,
resources and infrastructure that support research” [18].
In order to overcome the challenge of producing knowledge that is both academically
rigorous and relevant to practicing managers, university research needs to focus not just on
outputs, but most importantly on impact. To enable such a shift of focus, the necessary
environment needs to be in place.

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The Solution – A Two-Sided Market
The two challenges described above, one faced by SME and one faced by university
researchers, can be set to work against each other in a two-sided market. Two-sided
markets are economic platforms having two distinct user groups that provide each other
with network benefits or network effects [19-21]. Network effects refer to the impact that
the number of users of a platform has on the value created for each user. Same side
network effects explain how an increase in usage by one side (e.g. SME) leads to a direct
increase in value for that side (e.g. more SME joining the platform and seeking business
advice). Cross-side network effects explain how increases in usage of one service by one
side (e.g. research consumed by SME) lead to increases in the value gained by the other side
(e.g. impactful research by university researchers), which can in turn increase the value of
the original. Value can be both tangible (e.g. financial gains) and intangible (e.g. access to
data and resources). The value gained by each group can exhibit increasing returns and
economies of scale: SME (i.e. buyers of services) will prefer using a platform with more
choice (i.e. price and service providers), while university researchers (i.e. providers of
services) will also prefer using a platform with more choice (i.e. types of AI project-based
work and buyers).
Figure 3. A Two-Sided Market between SME and University Researchers

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A university-SME platform (U-SME) can act as an intermediary by helping to efficiently
match SME demand for AI project-based work to university researchers who can supply
such work at much lower cost than consultancy firms. Typically, consultancy firms charge
between £1,000-£2,000 per day. The U-SME platform can minimize the overall cost of such
matching, by avoiding duplication, and minimizing search and transaction costs. The U-SME
platform can, on the one hand, address the challenge faced by SME regarding the slow
adoption of AI technologies due to the difficulty of assessing the value of such technologies
through trusted business advice. On the other hand, the U-SME platform can address the
challenge of university researchers producing research that is either not informed by
practitioners or not being relevant to those. The U-SME platform will be developed over two
phases, as described below.
Phase 1 – On Demand Projects

During Phase 1, the platform will enable both SME and university researchers to sign up,
create a profile and start interacting. The platform will moderate each step of those
interactions as shown in Figure 4.

Figure 4. U-SME Process: Phase 1 - On Demand Projects

SME will be able to browse for service providers, access use cases and research show cases,
define an AI project (such as predicting client interests, sales efficiency, social media
sentiment analysis, and others), review submitted proposals by providers, accept proposals

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and post payment, accept milestones achieved and trigger milestone payments, and provide
feedback and rating for providers.
SMEs could have a facility to crowd-source projects or services. This facility would enable a
number of SMEs to share the costs of a project or service. An SME could post a request and
make it available for other SMEs to join.
University researchers as providers will be able to upload their existing research and
expertise, review projects submitted by SME, make proposals including milestones for
staged payments, get notifications for proposal acceptance and sign contracts, complete a
series of project milestones, receive payments for milestones achieved, and provide
feedback and rating for SME.
Unlike other two-sided markets like Expert 360 and Freelancer, the U-SME platform will
perform an initial screening and vet both providers and SME based on a set of standards and
suitability criteria. This will help address issues of quality, as well as trust, both of which can
have a significant impact on the growth and sustainability of the platform.
In addition, the U-SME platform will analyze all requests and interests while ensuring the
confidentiality of both SME and providers, ensure all proposals follow a standard
methodology, provide a Machine Learning algorithm to assign most suitable projects to
most suitable providers, arrange for pre-payment of funds to escrow, manage the contract
process, payments and indemnity insurance, analyze lessons learnt, feedback and ratings,
issue resolutions, and provide chatbot and client-service support.

Phase 2 – Hosted Services

During Phase 2, the platform will develop research into hosted solutions for SME to easily
adopt. The platform will moderate each step of interactions between university researchers
and SME as shown in Figure 5.

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Figure 5. U-SME Process: Phase 2 – Hosted Services
SME will once again be able to browse for providers. During this phase, however, they will
also be able to browse for available apps similarly to browsing the Apple App Store or
Google Play. SME will able to review the terms and conditions of downloading and using the
chosen app and make a payment. They will then be able to leave feedback and rate the
providers of the app.
University researchers as providers will once again be able to upload their existing research
and expertise. During this phase, however, they will also be able to make the source code of
their apps available to the platform as a hosted service. They will be able to sign a contract
with the U-SME platform to upload their app and establish a revenue sharing relationship.
They will be able to receive payments for app usage and provide on-going support and
maintenance for the app.

The U-SME platform will once again vet both providers and SME based on a set of standards
and suitability criteria. During this phase, the U-SME platform will also screen apps and
make them available to SME as a hosted service, after having established a revenue sharing
relationship with university providers, as well as ensuring that apps comply with platform
standards, as in the case of apps on the Apple App Store. The U-SME platform will be able to
track app usage, arrange for pre-payment of funds to escrow, manage the contract process,
payments and indemnity insurance, analyze lessons learnt, feedback and ratings, issue
resolutions, and provide chatbot and client-service support.

To enable uptake of the platform by SME, an initial set of popular services will be defined
and promoted, such as:

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• Customer service chat bot
• Recommendation engine to drive client growth
• Benchmarking services
• Price optimization service
• Order or invoice automation and analysis
• Voice recognition system
• Document analysis and checking for standard terms
• News or market analysis and summary
• Social media sentiment analysis
• Credit limit checker
• Compliance or AML monitoring service

The U-SME platform will provide education materials for registered SME users to help them
understand what services might be available, how they could be implemented and used for
business benefit.

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Benefits
Benefits for Buyers - SME

The benefits for SME provided by the U-SME platform will be potentially huge. First, the U-
SME platform will provide one place to go to find AI-based expertise and solutions provided
by leading universities, thus addressing the current challenge for SME to find trusted advice
for growth. Unlike consultancy firms, universities offer independent, objective advice that is
usually driven by cutting edge research. The fees for contracting university researchers are
also much lower than the fees charged by consultancy firms, thus contributing positively to
the resource constraints faced by SME.

Second, and related to the first, the U-SME platform can provide transparency over pricing
options for business advice.

Third, the U-SME platform can facilitate the engagement process for expertise and solutions
through standardized processes of contract and IP management.

Fourth, the U-SME can provide access to proven solutions and use cases to increase
awareness and adoption of AI technologies, which are often presented with much hype but
less clarity about their applicability in SME contexts.

Fifth, SME can gain access to practical resources, including project management, theoretical
frameworks, designs and models to aid decision making and implementation of AI
technologies in their work practices.

Finally, SME can access value for money hosted solutions, which will become available after
research and analysis of already applied solutions to other SME contexts.

One additional benefit that could be gained is access to crowdfunding research and
solutions that could be implemented on the U-SME platform in the near future (e.g. link to
CrowdCube).
Benefits for Providers –University Researchers
The benefits for university researchers provided by the U-SME platform will also be
potentially huge. First, according to a report by BIT in 2018, the annual value of the business
advice market is between £2 and £4 billion, and only 1% of SME seek advice from
universities [22]. The U-SME platform offers opportunities to university researchers to tap
into this market to generate funding for more impactful research.

Second, and related to the first, the U-SME platform offers opportunities to university
researchers to engage with a broader set of consumers who will use academic research. This
will increase the likelihood that research topics and study designs will incorporate input
from that broader set of consumers, thus, producing more impactful research.

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Third, the U-SME platform will provide one place to go to find interesting projects, data, and
sponsorship from SME that will facilitate university research.

Finally, the U-SME platform could potentially lead to the commercialisation of research,
while providing possibilities that a high percentage of the profits generated will be re-
invested in research.

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Next Steps

In order to assess the available demand for the proposed U-SME platform, we intend to run
a survey across both university researchers and SME. Using our immediate networks, we
will send out a Qualtrics-supported survey1
to assess the type of AI-based projects currently
on demand by SME, as well as the AI skills of university researchers. We will also assess time
and pricing demands for each type of AI-based project.

Following this survey and depending on our findings, we will organize and host an event at
the London Shard campus of the Warwick Business School to gauge further interest and
feedback from SME and university researchers, but also potential investors. We expect this
event to take place in the Spring 2019, to allow time to build an initial prototype of the U-
SME platform which can be showcased to event participants.

1
Qualtrics is the leading web-based software for conducting surveys and is a licensed software of the
University of Warwick. https://www.qualtrics.com/academic-solutions/warwick-university/

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References
[1]. WEF (2016) The Future of Jobs: Employment, Skills and Workforce Strategy for the
Fourth Industrial Revolution. World Economic Forum. Available from
http://www3.weforum.org/docs/WEF_FOJ_Executive_Summary_Jobs.pdf

[2]. McAfee, A., and Brynjolfsson, E. (2017) The Business of Artificial Intelligence. Harvard
Business Review. July 18, 2017. Available from https://hbr.org/cover-story/2017/07/the-
business-of-artificial-intelligence

[3]. McKinsey (2017) Artificial intelligence: The next digital frontier? Available from
https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%2
0Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20co
mpanies/MGI-Artificial-Intelligence-Discussion-paper.ashx

[4]. Accenture (2016) Why Artificial Intelligence is the Future of Growth. Available from
https://www.accenture.com/t20170927T080049Z__w__/us-en/_acnmedia/PDF-
33/Accenture-Why-AI-is-the-Future-of-Growth.PDFla=en#zoom=50

[5]. Rhodes, C. (2017). Business Statistics. House of Commons Library Briefing Paper.
Available from
http://researchbriefings.files.parliament.uk/documents/SN06152/SN06152.pdf

[6]. Mole, K., North, D., & Baldock, R. (2017). Which SMEs seek external support? Business
characteristics, management behaviour and external influences in a contingency approach.
Environment and Planning C: Politics and Space, 35(3), 476-499.

[7]. BEIS. (2017). Small Business Survey 2016: panel report. Available from
https://www.gov.uk/government/publications/small-business-survey-2016-panel-report

[8]. Overman H. G. (2016) What Works Centre for Local Economic Growth: Business Advice.
Available from http://www.whatworksgrowth.org/public/files/Policy_Reviews/16-06-
15_Business_Advice_Updated.pdf

[9]. Entreprise Research Centre (2018) State of Small Business Britain Report. Available from
https://www.enterpriseresearch.ac.uk/wp-content/uploads/2018/06/SSBB-Report-2018-
final.pdf

[10]. Dellot, B., and Wallace-Stephens, F. (2017) The Age of Automation Artificial
intelligence, robotics and the future of low-skilled work. The Royal Society for the
encouragement of Arts, Manufactures and Commerce. Available from
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automation?utm_medium=social&utm_source=medium&utm_campaign=age-of-
automation&utm_content=report

[11]. Benbasat, I., and Zmud, B. (1999) “Empirical Research in Information Systems: the
Practice of Relevance,” MIS Quarterly (23:1), pp. 3-16.

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[12]. Davenport, T., and Markus, M. (1999) “Rigor vs. Relevance Revisited: Response to
Benbasat and Zmud,” MIS Quarterly (23:1), pp. 19-23

[13]. Flyvbjerg, B. (2001) Making Social Science Matter: Why Social Inquiry Fails and How It
Can Succeed Again, translated by Steven Sampson, Cambridge: Cambridge University Press.

[14]. Constantinides, P., Chiasson, M., Introna, L. (2012) “The Ends of Information Systems
Research: a Pragmatic Framework” MIS Quarterly, 36(1), pp. 1-10

[15]. Rynes, S. L., Bartunek, J. M., & Daft, R. L. (2001). Across the great divide: Knowledge
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[16]. Shapiro, D.L., Kirkman, B.L. and Courtney, H.G. (2007). Perceived causes and solutions
of th6e translation problem in management research. Academy of Management Journal,
50(2), pp.249-266.

[17]. Shapiro, D.L., Kirkman, B.L. (2018) It’s Time to Make Business School Research More
Relevant. Harvard Business Review, July 19, 2018. Available from
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relevant?utm_source=twitter&utm_medium=social&utm_campaign=hbr

[18]. REF (2014) REF Brief Guide. Research Excellence Framework. Available from
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[19]. Parker, G.G. and Van Alstyne, M.W., 2005. Two-sided network effects: A theory of
information product design. Management Science, 51(10), pp.1494-1504.

[20]. Rochet, J.C. and Tirole, J., 2006. Two-sided markets: a progress report. The RAND
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[21]. Constantinides, P., Henfridsson, O., and Parker, G. (2018) Platforms and Infrastructures
in the Digital Age. Information Systems Research, 29,2, pp. 381-400.

[22]. BIT (2018) Evidence Report: Improving Functioning of the Business Advice Market and
Increasing the Take-Up of High-Quality Business Advice amongst SME. The Behavioural
Insights Team.

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