Exponential data, meet exponential thinking. The ... · Systems Era Programmable systems enabled major accomplishments such as space exploration and the development of the internet

Exponential data, meet exponential thinking

The intelligence is artificial. The implications are very real

Exponential data, meet exponential

thinking. The intelligence is artificial.

The implications are very real.

Copyright © 2019 Deloitte Development LLC. All rights reserved. 2Welcome to the Age of With

Headline Verdana BoldWelcome to the Age of WithFAE | 3º Congresso de Gestores PortuguesesNovember, 2019

4Copyright © 2019 Deloitte Development LLC. All rights reserved. Welcome to the Age of With

The pace of technology change is outstripping human ability to adopt it

1900s

Tabulating System Era

Mechanical devices helped us with tasks such as organizing data and making calculations (Hollerith Tabulating Machine).

1950s

Programmable Systems Era

Programmable systems enabled major accomplishments such as space exploration and the development of the internet. The programmable systems era will continue indefinitely, and will underpin the era of computing that we are in now (Windows desktops/ ERP systems).

2000s

CognitiveEra

Cognitive capabilities augment and amplify human intelligence. It is critical to recognize and know when and how to automate and augment to achieve the competitive advantage.

2020s

Amplified Intelligence Era

In the future, systems will be able to augment human intelligence and replicate human interactions.

More than a decade after the concept of big data became part of the lexicon, only a

minority of companies have become insight-driven organizations—and culture may be

the culprit.


“This has to be a human system we live in.” Sandy Pentland

“Those who learned to collaborate and improvise most effectively have prevailed.” Charles Darwin

withTime to shift your thinking from versus to with


Where we focus

Intelligence with advantage

AutomationSpeed with precisionWith the advantage of robotic & cognitive automation (R&CA) technologies, we can transform business processes, augmenting human labor with a digital workforce to drive better outcomes.

InsightsIntellect with purposeWith the advantage of advanced pattern recognition and machine learning, we can find previously unrecognized patterns in data, continuously learn from those patterns, and execute actions and report on them. What’s more, the entire system is able to get smarter and improve over time.

EngagementConnection with meaningWith the advantage of intelligent technologies such as natural language processing with speech recognition, as well as advanced engagement principles, such as predictive, proactive, and real-time interactions, we can deliver mass personalization at scale.


Path to Enterprise AI

Deloitte’s AI Maturity Model

Level 1

AI aware

Level 2

AI localized

Level 3

AI aspiration

Level 4

AI company

Level 5

AI competitor

Aware of AI but little to no infrastructure in place and undefined analytics

strategy.

Adopting AI, building capability, and

articulating an AI strategy in silos.

Expanding ad hoc AI capabilities beyond silos

and into mainstream business functions.

Industrializing AI to combine data from broad sources into

meaningful content and new ideas.

Depend on AI to manage performance, preserve

value, create new products and sources of

value, while keeping pace with rapid

technology change.


Path to Enterprise AI

Deloitte’s AI Maturity Model

+ Speed-up team’s learning

+ Demonstrate AI business value

+ Experiment in Data Labs environments

+ Supported by existing budgets

+ Data platform for quick-wins at department level

+ Deploy & Integrate AI in production

+ Collaboration between IT and business teams

+ Define dedicated AI budgets

+ Centralized Governed Data platform for self-service analytics

+ Automated ML exploration and deployment to production

+ AI strategy in place (short-to-medium term)

+ Increased and strategic dedicated AI budget

+ Central AI platform with corporate-wide standards

+ AI strategy based on a long-term roadmap

+ AI drives digital transformation and shapes new services and capabilities

The insights trifecta: Data and tools, talent and

culture

A data-driven culture is the most

difficult to establish

Level 1

AI aware

Level 2

AI localized

Level 3

AI aspiration

Level 4

AI company

Level 5

AI competitor


An integrated and optimized environment for engaging end users

How it works

Traditional Channels (Phone, E-mail, In-person)

Talent

Processes

Operations

Core Technology Layer(e.g., SAP, Salesforce)

Technology Infrastructure / On-Premise / Cloud

Intelligent Technology Layer(e.g., NLP, Speech Recognition, Machine

Learning, Automation)

Applications

Data Management, Analytics, and Security

Intelligent Channels

Automated Augmented

Business Transformation Strategy


Deloitte’s full-stack iterative approach

Approach for building a leading AI Practice

Proof of Value

Deploy ML

WorkflowDiscovery

Choose the right use case and derive business value

Train & coach

Upskill permanent team

ML Roadmap and Governance

Deploy and integrate ML workflow in production

Syn

thesis

Choose the right use case and derive business value

Upskill permanent team

Deploy and integrate ML workflow in production

Deliver ML to production UpskillCoE

Blueprint


AI-fueled journey presents CIOs with an

opportunity to redefine their own role,

from chief information officer to

Chief Insight Officer


Inflection points and benefits

Boost customer/user/citizen engagement

24x7 support

Multilingual

Increase focus on mission-critical tasks

Transform costreduction

Reallocate of talent(“Future of Work”)

Improveuser experience

Improve compliance and regulatory consistency

Increase efficiencyand speed

Wait time (customers, citizens, users) Human

Resource constraintsand Human error

Budget constraints

Common pain points Potential benefits


Our lessons learned regarding AI adoption

AI considerations

01Choose the right use casesThe first use case is key-enabler. The decision of use case’s roadmap should be based in cultural acceptance, financial potential and data availability & richness.

02Automate ML exploration and deploy to productionML experimentation phase should be automated to continuously explore better models. The deployment of ML workflows is key to obtain business value.

03Bring the right skillset in-houseThe technology landscape of AI is large and innovates quickly. Choosing the right set of tools helps focusing and upskilling AI teams.

04Data and ML GovernanceGovernance of data and ML workflows, including data and models quality controls, are mandatory to achieve a robust Enterprise AI practice.

Nobody likes to be left behind when the next big thing comes along and business leaders are no exception.

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Exponential data, meet exponential thinking. The ... · Systems Era Programmable systems enabled major accomplishments such as space exploration and the development of the internet