ITCamp 2019 - Andy Cross - Business Outcomes from AI

@ITCAMPRO #ITCAMP19Community Conference for IT Professionals
BUSINESS OUTCOMES FROM AI
Andy Cross
Director / Elastacloud / @andyelastacloud / Microsoft Regional Director /
Azure MVP

Many thanks to our sponsors & partners!
GOLD
SILVER
PARTNERS
PLATINUM
POWERED BY
BUSINESS OUTCOMES FROM AI

Brexit update
• Hogwash
• It’s like watching people trying to fix a broken down
car using poetry. - @IanDunt

• Success through modern analytics:
– Business Understanding
• Mental models
– Data Understanding
– Sustainable Business Processes
– Value Proposition
• The value of a service is greater than the cost of purchasing it and building
it oneself
Agenda

• Locking business and technology together
–New roles such as CDO, CInnovO
• IT 3.0 – profit centre not cost centre
–Innovate and generate through novel products;
–Beware the bear traps; CIOs are easily sold on “new
business models” but easily lost if this turns to vapour
when they try to explore it
Top Business Trends

The AI Project Stages
• Opportunity discovery
– What can we do? What are our success criteria?
• Implementation
– How do we do it?
• Adoption
– How do I get my company to use this?
• Sustainability
– How do we control costs and/or build a viable price point?
• Measurement
– How do we know it’s generating value?
Build
Use
Profit

OPPORTUNITY DISCOVERY
Fitting AI to a Business

• Innovating customer service and experience
– Reaching further
– Better products
– Self-healing and self-improving
• Improving operational processes and resource
consumption
– Faster Time to Market
– Lighter Bill of Materials
• New business models
– Revenue from Insights in data
– Different pricing models
The end goals

Customer Satisfaction
1. Targeting customers more accurately through MarkTech
1. Defining clusters of users by their behaviour -> Maximise Revenue
2. Recommending a customers next best action -> Maximise Revenue
3. Fulfilling a duty of care and responsibility where actions may have a
poor outcome for the customer -> CSR
2. Intervening in Predicting Churn
1. Measure engagement with features -> Product Quality and
Maximise Revenue
2. Target discounts and offers -> Minimise Sales Discounting
3. Omni-channel engagement -> Target investment
4. Acquisition/Loss decision support -> Target investment

Operational Efficiency
1. Predictive Maintenance
1. Conditional Maintenance
2. Dynamic Service Regimes
3. JIT Repair and Supply Chain
2. Enterprise Resource Management
1. Asset utilisation management
2. Enterprise Warehouse Optimization
3. Variable and complex cost modelling
1. Trend identification in system costs
2. Identification of waste
3. Resource Planning
4. Cost related to people
1. Churn prediction
2. Progression of most capable staff

New Lines of Revenue
1. Data Brokerage
1. Feeds of predicted outputs / yields
2. Common costs in market predictions
3. Historic weight of data
2. Digitalisation of Services
1. Replace administrative services with digital
2. Translation and NLP services
3. Automation of visual inspection
3. Establishment into new market segments
1. Fuel Optimisation algorithm and JIT refuelling via machine vendor
2. Disruption of supply chain and internal bidding mechanisms

Similarity or Uniqueness
• Business problems that are similar and non-unique i.e. facial recognition
– Vendors will focus on these repeatable problems to maximise their return
– If you’re not a software company, you must focus on the others
• A rich vein of unique problems remains
– Businesses compete on different aspects of their similar products, whether
service, quality, style, sophistication, each with can be augmented with AI
• An AI built from the data of a business is a unique model of that
business
– The atomic base of a Brand Intelligence is Data

• Respect their core values
– Shipping reliability
– Stock availability
– Customer satisfaction
– Product quality
• These are the key KPIs of strategic success
– The language of the business
– Not generalisms such as:
• Growing Revenues
• Controlling Costs
• Reducing Risks
– The Success Criteria of a project are general metrics that affect core values
Know your customer

Examples

MENTAL MODELS
In order to envision the transformation possibilities, consider these mental models; key
benefits of AI with a real world twist

One Million Clerical Workers
What is achievable with a million trained office
workers?

Billy the Kid Reaction Times
What if all workers can
react and form a course of
action in 1 millisecond?

Auditable Actions
What if all actions and their impact were
measurable and quantified, before and
after the fact?
Great companies have high cultures of accountability, it
comes with this culture of criticism […], and I think our
culture is strong on that.
Steve Ballmer
Challenge the culture of
“We made a bad decision”
with
“We acted on a 70% chance and lost”

Cement the approach to the unchanging
What if the decision making process proven hyper-successful for
static domains was automated and eternally actionable?

All tools are Smart Tools
What if every tool used by a business was
a cognitive member of the team?
A forklift could tell you the context of a crate was the
wrong weight.
A machine could tell you it was feeling unwell.
A room could tell you someone had left a dangerous
tool in an unsafe state.

Own a Unique Selling Point
A system that takes in all available data about
a market can become a complete model for
that market and can lock out competitors.
Owning the most accurate model of power generation, of shoe
purchasing dynamics, of stock values - prevents competitors making
an entry to provide slim-services (services that complete with
established businesses without the overheads of those businesses).
Becoming a Data Brokerage is about earliness of market entry

PRIORITISING AI PROJECTS
How do you sort opportunities you’ve discovered?

Priority Criteria and Success Probability Markers
• Clearly articulated objective aligned with strategic targets of
business
– With buy in of senior stakeholders
• A measurable financial benefit to the business
– And owned by whichever division we’re engaging – it is direct to our
stakeholders
• Acquired knowledge of the objective
– we’ve heard the anecdotes
• Technically measured complexity
– A Disco has occurred
• Success Criteria established
– Reference £6 energy charge threshold

Technical Resources for Prioritisation
• Availability and ownership of roles
– Data Liaison
– Data Steward
• Has the disco sufficiently catalogued data sources?
• What complexity is there in acquisition?
• What is the availability, quality, timeliness and completeness
of data?
– No data, no model
– Poor data, poor model
– Out of date data, out of date model
– Incomplete data, incomplete model

IMPLEMENTATION
Building a team and delivering an AI

Some observations
• Most approaches have solid foundation in 1960s
• Availability of cheap compute and distribution frameworks
– AKA Cloud
• Separation of storage and compute
– Differentiation of econcomy; costly training, cheap model
storage
• Advent of operationalisation demands
– VMs -> Hadoop -> K8S -> Spark -> Databricks

GETTING THINGS DONE
Productivity is key

How to get things done in corporations?
• Put the team into the swimlanes of the business
• Don’t put things into a separate hived off business
• Subject Matter Expert is the most important thing

The Agile Process

Crisp-DM

Lean Analytics; continuous iterative analytics
Define Predictive model Improve model
Individual Approach fits into a sprint
Improve model

ROLES IN A DATA PROJECT

Data projects span specialties
• Business
– Owns the problem, owns the success criteria, will likely enact the
decisions that the AI performs decision support for
• Business -> Data Liaison
– Owns the anecdotes and acts as a Subject Matter Expert, is likely a
classic analyst who supports senior management
• IT BG -> Data Steward
– Owns the collection, storage, classification and understanding of data,
is likely a classic data professional, possibly with a DBA history
• Data Project Workers
– A team of Data Engineers and Data Scientists who deliver the AI

Division of Labour in Data Workers
• Engineering should build data acquisition, integration
and pipelining strategies
• Data Scientists should focus on getting better and
better predictions from data
• Engineering needs to be involved all the way through
a project because deployment and pipelining
methodologies need to be productionised
• Engineers need to be involved in conversations about
which technologies can scale

Value of Data Scientists

When is a model good enough?
• When it matches the initially defined success criteria
• Continuous Improvement of Platform
– Software continually evolves
– Better algorithms
• Continuous Improvement of Model
– Models continually evolve with new data
– Trends, changes in behaviour need to be understood
– and feature engineering needed continually

When is data good enough?
• Testing and Training
• Training and Scoring
• No data, no model
• Incomplete data, incomplete model
• Out of date data, out of date model
• Bad data, bad model

Do I have a problem that requires Data Science?
• Is my problem clearly defined?
• Can I see whether it’s been done before (Google search)?
• Speak to the BI team first and see whether it’s a problem
that’s been solved inside the business
• If this is clearly a problem which requires a Data Scientist,
spike it out before committing
• Colleague of mine says, most organisations can’t do BI
properly but now they’re moving onto AI
• Get your data strategy right!

SUMMARY
recap

• AI isn’t taking over the world, it’s just tooling to help
us – Tim Huckerby
• Business Understanding
• Data Understanding
• Agile delivery of software.
Summary

Q & A

ITCamp 2019 - Andy Cross - Business Outcomes from AI

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