SCALING AI STARTUPS THROUGH PRODUCTIZATION AND INTERNATIONALIZATION
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The document explores challenges and success factors for AI startups aiming for scalability. Through interviews with AI startup founders, it develops a framework to classify AI startups based on their value driver and offering. The main findings are: 1) AI startups improve scalability by applying lean startup and customer development principles to systematically develop their business model and products in a market-oriented way. 2) Startups need to establish organizational structures, standardize processes, and specialize their technology to develop productized businesses. 3) International expansion can help scale startups but requires fully productized solutions; initial expansion occurs through clients and partners while eventual local presence is needed for customization.
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SCALING AI STARTUPS THROUGH PRODUCTIZATION AND INTERNATIONALIZATION
- 1. SCALABILITY IN ARTIFICIAL INTELLIGENCE STARTUPS – AN EXPLORATORY STUDY Sophia Frisbie, 2019 MCI Management Center Innsbruck
- 2. 2 1. Introduction 2. Questions & Synthesis from theories 3. Design & Findings 4. Recommend ations & Summary CONTENT
- 3. Note: Worldwide. Source: Statista 2018. 3,221.8 4,819.11 7,345.35 11,283.76 17,267.75 25,995.68 37,987.17 53,231.4 70,971.51 89,847.26 2016 2017* 2018* 2019* 2020* 2021* 2022* 2023* 2024* 2025* Market in million U.S.D. AI +43% revenues p.a. 3 x3 AI startup funding (2016-17) 1
- 4. 4 Expectations Time AI HYPE HYPE CYCLE FOR EMERGING TECHNOLOGIES (GARTNER, 2016) X AI today disillusionment Plateau will be reached in 2-5 years (Enterprise AI maturity) peak of inflated expectations plateau of productivity 1
- 5. CHALLENGES & SUCCESS FACTORS FOR AI STARTUPS THAT AIM FOR SCALABILITY 5 2
- 6. AI startups Startup growth QI: How do startup- growth principles apply to AI startups ? Productization Q2: How can AI startups develop into a productized business? Inter - nationalization Q3: What role does internationalizatio n play in AI startups? 6 RESEARCH AREAS 2
- 7. SYNTHESIS – AI HYPE CAN KILL THE FOUNDATION Startup sales growth Expectations Time Hype Cycle (Gartner, 2016) vs. Lifecycle of an Entrepreneurial firm (Picken, 2017) 1. Defining business model & analyzing the market1 2. Setting a foundation, productizing & specializing2 Expectations Sales growth THEORETIC CONCEPTS 7 AI Startup sales growth Expectations Time AI in the Hype Cycle vs. Future AI startup growth scenarios today (2019) Possible growth scenarios** **Unknown trajectory post-AI- hype AI STARTUP GROWTH IN A HYPED ENVIRONMENT Expectations Sales growth* *During AI hype 1) Theoretic concepts include: Lean Startup Theory, Customer Development Model 2) Theoretic concepts include: Software productization, Service productization, Hedgehog concept, Transitioning stage in Lifecycle 3) Theoretic concepts include: OLI framework, lean global startup 3. Scaling & internationalisation3 2
- 8. 8 Method Semi-structured interviews Approach Theory testing Theory building Participants 7 experts 9 AI startups Geogr. Coverage Globally Globally Analysis results Developed categories Tested, refined categories Outcome High-level framework High-level success factors Low-level framework Challenges Success factors RESEARCH DESIGN Phase 1 Phase 2 3
- 9. Research objective EXPLORE CHALLENGES AND SUCCESS FACTORS FOR AI STARTUPS THAT AIM FOR SCALABILITY Research questions Research areas Theoretic concepts Analysis categories 1. APPROACH TO BUSINESS MODEL DEVELOPMENT 2. MARKET- ORIENTATION 3. SETTING & MAINTAINING DIRECTION 4. ORGANIZATIO N BUILDING 5. STANDARD- IZATION 6. INTERNATION ALIZATION & PRODUCTIZAT ION I. How do startup- growth principles apply to AI startups 1. Startup growth 1.1 Lean startup x 1.2 Customer Development x x 1.3 Transitioning stage in lifecycle x x II. How can AI startups develop into a productized business? 2. Productizat ion 2.1 Software productization x 2.2 Service productization x x 2.3 Hedgehog concept x III. What role does internationaliz ation play in AI startups; 3. Inter- nationalizat ion 3.1 Internationaliza tion in tech x 3.2 Entry modes 3
- 10. Commercial- ization Research Advisory Standardized products TYPE II: AI consulting (limited scalability) TYPE I: Diffused, no clear focus (not scalable) TYPE IV: Scaling a product (high scalability) TYPE III: Focused tech innovation (creating IP) Value driver Customer-specific solutions Offering AI STARTUPS CLASSIFICATION FRAMEWORK 10 4
- 11. Commercial- ization Research Advisory Standardized products TYPE II: AI consulting (limited scalability) TYPE I: Diffused, no clear focus (not scalable) TYPE IV: Scaling a product (high scalability) TYPE III: Focused tech innovation (creating IP) Value driver Customer-specific solutions Offering AI STARTUPS CLASSIFICATION FRAMEWORK – APPLIED 11 S8 (’08) S6 (’12) S9 (’14) S5 (’16) S1 (’17) S7 (’18) S2 (’17) S3 (’14) 4
- 12. Commercial- ization Research Advisory Standardized products TYPE II: AI consulting (limited scalability) TYPE I: Diffused, no clear focus (not scalable) TYPE IV: Scaling a product (high scalability) TYPE III: Focused tech innovation (creating IP) Value driver Customer-specific solutions Offering RECOMMENDATIONS 12 Hypothesis-driven, market- oriented, maintain direction, organizational structures, standardization Hype-driven short- term growth; no clear direction, no standardized processes. 4
- 13. 13 SUMMARY Question Finding 1. How do startup- growth principles apply to AI startups? AI startups improve scalability, if they apply a systematic approach to business model development (Lean Startup), market-oriented product development (Customer Development), and set & maintain a direction (Lifecycle – Transitioning stage) 2. How can AI startups develop into a productized business? Organization building: structures, delegation (Lifecycle – Transitioning stage) Standardization, modularization for repeatability and scalability (productization) Specialization within data-types and data-processes (Hedgehog concept) 3. What role does internationalization play in AI startups, and how does internationalization link to the productization of the business? Market-seeking for scale – only viable when fully productized, to gain ownership-advantage (OLI) Initial expansion via clients and partners (Lean Global Startup) Ultimately local presence due to need for customization (Software Startup) 4
Editor's Notes
- In the following 15 min I will provide you an overview on th scalability of AI startups
- my presentation will cover an introduction to the topic, my research questions and literature findings, the research design, findings, and recommendations. A summary is provided for discussion.
- AI major transformative force of this century; Founder of coursera put it, AI is the new electricity; it has the same level of impact on industries, as electricity had centuries ago. Th reflected in fast growing revenues, the AI industry revenues are growing 43% p.a. Also, in just one year, AI startup funding tripled. But what happens to the many emerging startups down the line?
- The hype cycle of emerging technologies provides a view. public expectations towards a new tech get hyped through media. Businesses often lack knowledge on the tech and therefore become disappointed when they realise the promises of the new tech are not fulfilled easily. Only at a later stage will expectations and capabilities align. AI is currently at the peak of inflated expectations. While AI customers have a strong desire to use the tech, they primarily need strategic advise on where to start. AI in enterprise is expected to reach a mature stage, meaning it will be more common and known, in 5 years from now.
- So, what does all of this mean for AI startups; we know that only few startups survive, and even fewer achieve, what is called scalability, the ability to grow revenues faster than costs. Therefore, this research explored the challenges and success factors for AI startups that aim to scale.
- Break down into research questions, …., I explored 3 key areas that help break down the reserch objective and inform the research questions. Gaps How it applies to AI startups Various growth curves – diverting from Picken Productisation – what activities? Software startups – how to Internationalization Lean startup by Ries Cust dev by Blank Lifecycle by Picken Software productization by Artz et al Service productization by Ritala et al Hedgehog concept by Collins Internationalization Motives: Bailetti, Onnetti, Blank Entry modes OLI: Dunning Burgel and Murray: customization Rasmussen and Tanev : lean global startup, partnering
- But what will happen after the AI hype? Without investing in business model development, standardization, processes and organization building, the startup is likely to experience a long-term decline. Falling into the trap of "hype-driven business", such an AI startup experiences success on the back of customers' lack of knowledge. Revenues are growing as long as enterprises require AI advisory and custom-made solutions. However, once the majority of enterprises have reached a mature stage in applying AI, these startups will struggle to scale. In the long-run, such an AI startup is unlikely to compete with providers that are focused on specific markets or domains and offer standardized products in a cost- and time-efficient manner.
- 1-high level, how the theory applies to AI startups. For example, focus was here on how to transform from being consulting focused towards creating scale, Approach to business model development 2- test and develop theory, ie input data and data processes to specialise market focus Setting & maintaining direction Organization building Market-orientation Processes, standardization & modularization Internationalization
- Lean startup by Ries Cust dev by Blank Lifecycle by Picken Software productization by Artz et al Service productization by Ritala et al Hedgehog concept by Collins Internationalization Motives: Bailetti, Onnetti, Blank Entry modes OLI: Dunning Burgel and Murray: customization Rasmussen and Tanev : lean global startup, partnering
- 8 out of 9 AI startups have one foot in AI consulting (type 1); this highlights the high market demand for advisory and custom-made solutions. Majority is cluttered between type 1 and type 2, indicating that they are gradually moving from 1 to 2 1 Startup is pure type 2, with a clear product (MVP), emphasized how difficult it is to stay focused 2 AI startups are at risk of getting trapped in the AI Hype, they have developed a product vision but are heavily investing resources in delivering customer-specific solutions and advisory
- The lean startup theory …Experts suggest AI startups lack hypothesis-driven approach when they get pulled into various directions due to the hyped environment. Those AI startups that apply a systematic, hypothesis-driven approach, scaled sooner than the rest of the sample. Take example type 2 and 4 product to scale, address the specific needs of a clearly defined target market T2: value, data, and receptiveness. trade-off blue-chip customers and standardizable product. AI consulting cannot replace … getting diffused. set and maintain direction: narrow down on key customers, product differentiation, and profitable project delivery. ... hyped environment. …consulting, they are at risk of losing focus. …agility and flexibility vs direction organization building: technical teams along with their products, modules, or industries. Delegation, increased effectiveness and efficiency. industry experts to manage customer engagements. …external funding–the funded AI startups have effective structures and processes in place Standardization for repeatability and scalability. customers’ input data. …specialize. Process, re-use models …AI consulting - service productization...’ unrealistic expectations, Internationalization: market seeking, .. AI startups are different from common tech startups when it comes to internationalization. Even though they initially seek channel partners just as other tech startups, they ultimately setup a local presence due to the high level of customization required for AI products… less productized , less efficiently internationally.-ownership-advantage- fully productized. Lean startup by Ries Cust dev by Blank Lifecycle by Picken Software productization by Artz et al Service productization by Ritala et al Hedgehog concept by Collins Internationalization Motives: Bailetti, Onnetti, Blank Entry modes OLI: Dunning Burgel and Murray: customization Rasmussen and Tanev : lean global startup, partnering