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Navigating Emerging Technologies in Business
A framework for evaluating and implementing new technologies, with case studies on high-profile successes and failures.
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Navigating Emerging Technologies in Business
- 1. ARIK FLETCHER NAVIGATING EMERGINGTECHNOLOGIES IN BUSINESS B E Y O N D T H E H Y P E : H O W T O E VA L U AT E , I M P L E M E N T, A N D S U C C E E D W I T H N E W T E C H N O L O G I E S
- 2. Agenda • I nt r o d u c t i o n • T h e I n n o v a t i o n L i f e c y c l e • S t r u c t u r e d A p p r o a c h t o Te c h n o l o g y E x p l o r a t i o n • S u c c e s s S t o r i e s a n d C a u t i o n a r y Ta l e s • B u i l d i n g a n I n n o v a t i o n C u l t u r e • C o n c l u s i o n
- 3. INTRODUCTION Techie Brunch Club– Meet, Greet, Eat, Repeat Greyface Games (@Greyface_Games) / X Greyface Guild - YouTube South Devon Sound
- 4. Objectives • Focus onmethodology over specific technologies, exploring success stories and cautionary tales • Maintain a consistent approach to evaluating and implementing technologies despite rapid changes • Learn a practical framework for assessing emerging technologies in your future careers and roles • Develop critical thinking about technology adoption, not just awareness of what's trending
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- 6. G a rt n e r R e s e a r c h " U n d e r s t a n d i n g G a r t n e r ' s H y p e C y c l e s " The Gartner Hype Cycle A visual representation of how technologies typically evolve from initial interest through early success (and failure) to waning interest, and finally into mainstream adoption
- 8. Hype Cycle inAction - AI Evolution • AI has gone through multiple hype cycles since the 1950s • Early expert systems created initial excitement but couldn't deliver on promises • The 'AI winter' of the 1990s represented a classic trough of disillusionment • Machine learning's practical applications in the 2010s moved AI up the slope of enlightenment • Today's generative AI is creating another peak of inflated expectations Understanding where a technology sits in this cycle helps manage expectations and investment decisions H a r v a r d B u s i n e s s R e v i e w " T h e B u s i n e s s o f A r t i f i c i a l I n t e l l i g e n c e "
- 9. Hype Cycle inAction - Cloud Computing • Early cloud services (2006-2008) created excitement but faced skepticism about security • By 2010-2012, many early adopters were disillusioned by migration challenges and unexpected costs • From 2013-2017, best practices emerged, making benefits more achievable • Today, cloud computing has reached the plateau of productivity as a mainstream business approach This evolution took over 15 years - typical for truly transformative technologies M I T S l o a n M a n a g e m e n t R e v i e w " C l o u d C o m p u t i n g E v o l u t i o n : F r o m D i s r u p t i o n t o M a i n s t r e a m "
- 10. Why Understanding theCycle Matters N e t f l i x Te c h n o l o g y B l o g " N e t f l i x ' s J o u r n e y t o t h e C l o u d " • Timing your adoption is critical - early adoption brings competitive advantage but higher risk • Late adoption is safer but may mean losing market position • The right timing depends on your industry, company culture, and risk tolerance" • Example: Netflix's early cloud adoption gave them scalability advantages over competitors • Counter-example: Many banks deliberately adopted cloud later but with more mature approaches"
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- 12. M c Ki n s e y D i g i t a l “ W h y D i g i t a l S t r a t e g i e s F a i l ” A Framework for Technology Assessment • Random technology adoption leads to wasted resources and missed opportunities • A structured approach ensures technology serves business needs • Develop a framework to help scale and match to an organisation’s size and resources • Provide a common language for business and technology stakeholders • Use the framework to filter hype from reality Business Problem Identification Research Methodologies Assessment Criteria Development Proof of Concept Design Evaluation Protocols
- 13. Business Problem Identification Ha r v a r d B u s i n e s s S c h o o l C a s e S t u d y " I K E A ' s D i g i t a l Tr a n s f o r m a t i o n " • Always start with the business challenge, not the technology • Clearly articulate: What problem are we trying to solve? • Quantify the impact of the current problem (cost, time, customer satisfaction) • Identify stakeholders affected by the problem Example: IKEA identified that inventory management was costing 15% in lost sales and excess stock before considering any technology solution
- 14. Research Methodologies • Sourcesmatter - seek vendor- neutral information first • Industry analyst reports (Gartner, Forrester) provide broad perspective • Peer networks offer real-world implementation insights • Academic research gives depth on emerging technologies • Create a balanced research approach combining multiple sources • Document research methodically to support decision-making O E C D “ Te c h n o l o g y a s s e s s m e n t f o r e m e r g i n g t e c h n o l o g y ”
- 15. D e lo i t t e I n s i g h t s “ A n e w l a n g u a g e f o r d i g i t a l t r a n s f o r m a t i o n ” Assessment Criteria Development • Develop consistent criteria before evaluating specific solutions • Business alignment: How does this support strategic objectives? • ROI potential: Quantifiable benefits vs. total cost of ownership • Implementation complexity: Resources, timeline, organisational change • Risk profile: Security, compliance, vendor stability • Future flexibility: Ability to scale, adapt, or pivot
- 16. Proof of ConceptDesign S i e m e n s D i g i t a l I n d u s t r i e s S o f t w a r e " P r o o f o f C o n c e p t M e t h o d o l o g y " • Design limited-scope experiments to test assumptions • Define clear success metrics before starting • Involve end-users early in the process • Allocate dedicated resources rather than adding to existing workloads • Plan for both success and failure scenarios Example: Siemens testing IoT sensors on just one production line
- 17. Evaluation Protocols • Createa formal evaluation process to avoid confirmation bias • Compare results against pre- established criteria • Gather feedback from all stakeholder groups • Document unexpected outcomes (positive and negative) • Make go/no-go decisions based on evidence, not enthusiasm • For 'go' decisions, develop scaling strategy • For 'no-go' decisions, capture lessons learned" C e n t e r f o r S t u d y o f S c i e n c e , Te c h n o l o g y a n d P o l i c y ( C S T E P ) “ Te c h n o l o g y A s s e s s m e n t F r a m e w o r k 2 . 0 : M e t h o d o l o g y N o t e ”
- 18. S a nt a n d e r I n n o V e n t u r e s “ T h e F i n t e c h 2 . 0 P a p e r : R e b o o t i n g F i n a n c i a l S e r v i c e s ” Case Study: Fintech Blockchain Approach • Santander identified settlement time as a competitive disadvantage • They researched blockchain beyond the cryptocurrency hype • Assessment criteria included regulatory compliance, interoperability, and cost • Their POC involved a single non- critical transaction type with existing clients • Evaluation showed 80% reduction in settlement time but implementation complexity • Decision: Phase 1 implementation for specific transaction types only • Key lesson: Targeted implementation rather than wholesale adoption"
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- 20. Data Analytics inRetail - Zara A c a d e m i a “ Z a r a I T M I S C a s e S t u d y " • Zara struggled with $2M annual losses from inventory management issues • Traditional forecasting based on historical sales was failing due to changing consumer patterns • They implemented advanced analytics in three phases • Results: 30% reduction in excess inventory, 22% reduction in stockouts • ROI achieved in 14 months despite initial implementation challenges • Phase 1: Data cleansing and warehouse implementation • Phase 2: Predictive algorithms for demand forecasting • Phase 3: Integration with supply chain systems
- 21. Digital Twins inManufacturing - Rolls-Royce R o l l s - R o y c e " D e l i v e r i n g F a s t e r, M o r e C h e a p l y a n d M o r e E f f i c i e n t l y t h r o u g h D i g i t a l I n n o v a t i o n " • Rolls-Royce faced unplanned downtime costing $50,000 per hour in their jet engine manufacturing • Traditional maintenance schedules were inefficient - either too early or too late • They created digital twins of critical equipment using IoT sensors and simulation software • Initial proof of concept on one production line reduced downtime by 35% • Full implementation across three plants achieved 45% reduction in unplanned maintenance • Additional unexpected benefit: 15% energy consumption reduction • Key success factor: Starting with clear metrics tied to business value
- 22. AI in CustomerService - Vodafone V o d a f o n e “ A n n u a l Te c h n o l o g y R e p o r t 2 0 2 0 " • Vodafone struggled with customer satisfaction and retention • They implemented AI-powered customer service in a phased approach • Results: 40% faster resolution times, 23% improvement in customer satisfaction • Critical success factor: They designed for human-machine collaboration rather than replacement • Phase 1: Chatbots for simple queries only, with clear escalation paths • Phase 2: Natural language processing for customer sentiment analysis • Phase 3: Predictive models for churn prevention"
- 23. B o st o n C o n s u l t i n g G r o u p “ F l i p p i n g t h e O d d s o f D i g i t a l Tr a n s f o r m a t i o n S u c c e s s ” Common Success Factors • Clear business problems with quantifiable impact • Phased implementation approach rather than 'big bang’ • Strong executive sponsorship but also frontline involvement • Focus on measuring outcomes, not just technology deployment • Willingness to adjust course based on early results • Integration with existing systems and processes • Attention to the human factors - training, change management, and communication
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- 25. Google Glass –Solution Without a Problem R e s e a r c h G a t e “ G o o g l e G l a s s : A C a s e S t u d y " • Google Glass launched in 2013 with enormous hype and a $1,500 price tag • The technology was innovative but lacked a clear use case for consumers • It created social friction - people didn't want to be recorded unknowingly • Epilogue: Google Glass found success in enterprise applications where the use case was clear
- 26. Quibi's $1.75B Failure Wa l l S t r e e t J o u r n a l " Q u i b i I s S h u t t i n g D o w n B a r e l y S i x M o n t h s A f t e r G o i n g L i v e " • Launched in 2020 with $1.75 billion in funding and major Hollywood talent, focused on short- form premium mobile content. • Solved a problem consumers didn't have, and launched during pandemic when people were home with TVs • Technology investment can't overcome fundamental business model flaws • Even experienced executives and massive funding can't guarantee success
- 27. Healthcare AI Failures- IBM Watson H e n r i c o D o l p h i n g “ T h e $ 4 B i l l i o n A I F a i l u r e o f I B M W a t s o n " • IBM Watson Health partnerships with MD Anderson Cancer Center and other major hospitals failed to deliver promised outcomes • Training data bias led to inappropriate recommendations • Regulatory and ethical considerations were underestimated • Integration with existing clinical workflows was insufficient • The stakes in healthcare required more rigorous validation than was performed • The technology wasn't mature enough for the complexity of the use case
- 28. H a rv a r d B u s i n e s s R e v i e w “ W h y S o M a n y H i g h - P r o f i l e D i g i t a l Tr a n s f o r m a t i o n s F a i l ” Common Innovation Pitfalls • Technology for technology's sake - implementing without clear business objectives • Underestimating implementation complexity and total cost of ownership • Inadequate stakeholder engagement and change management • Neglecting security and compliance implications • Failing to account for integration with existing systems • Unrealistic timelines driven by hype rather than practical considerations • Lack of measurement framework to determine success • No defined exit strategy if technology doesn't deliver expected value
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- 30. Creating a BalancedApproach • Innovation must balance opportunity with operational stability • Create a portfolio approach: 70% core improvements, 20% adjacent innovation, 10% transformative • Establish an innovation governance framework with clear decision rights • Dedicate resources rather than making innovation 'extra work'" • Set expectations that not all initiatives will succeed Example: Amazon's approach to innovation accepts failure as a cost of experimentation Te c h t r e n d G r o u p “ T h e I n n o v a t i o n P a r a d o x : N a v i g a t i n g t h e B a l a n c e B e t w e e n R i s k a n d R e w a r d ”
- 31. M c Ki n s e y D i g i t a l “ S u p e r a g e n c y i n t h e w o r k p l a c e : E m p o w e r i n g p e o p l e t o u n l o c k A I ’ s f u l l p o t e n t i a l ” The Future of Work • Develop critical evaluation skills when reading about new technologies Build 'T-shaped' knowledge: Deep expertise in one area plus broad awareness • Practice explaining technology in business terms • Create your own assessment framework and refine it over time • Seek mentors who successfully bridge technology and business • Remember that technology implementation is ultimately about people and processes, not just the technology itself
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- 33. Key Takeaways • Startwith business problems, not technology solutions • Use a structured, methodical approach to technology assessment • Implement with clear metrics and evaluation criteria • Learn from both successes and failures • Build a culture that balances innovation with pragmatism • Remember that technology adoption is a means to an end, not an end itself • The most valuable skill is discernment - knowing which technologies matter for your specific business context D e l o i t t e D i g i t a l Tr a n s f o r m a t i o n E x e c u t i v e S u r v e y " K e y s t o D i g i t a l Tr a n s f o r m a t i o n S u c c e s s "
- 34. QUESTIONS? A r ik F l e t c h e r h t t p s : / / a b o u t . m e / a r i k f l e t c h e r a r i k f @ i s e e t e c h . c o . u k