An expert proposes using AI, machine learning, and robotic process automation to automate the entire strategic planning process for organizations. Currently, strategic planning is often done poorly on an annual basis with limited and outdated data, resulting in mediocre outcomes. The proposed solution would use algorithms to continuously import, analyze, and interpret large amounts of internal and external data to identify opportunities and recommend strategic responses in real-time. This could provide organizations with faster, more data-driven decision making and a sustainable competitive advantage.

1. AI-Driven Strategic Planning
ODSC East 2022, Open Data Science
Conference
April 19 - 21, 2021

2. Traditional Organization Chart
CEO
Board of
Directors
Marketing Finance Production
Human
Resources
Strategy

3. Define Strategic Planning
 Tactical/operational vs strategic
 Multi-step process
 Well-studied and “improved”
 Concept is many decades old
 Carried out by most organizations
 Frequently supported by consultants

4. Purpose of Strategic Planning
 Prepare for the future – 3 to 5 to 10 years
 Achieve long-term objectives
 Better serve customers
 Anticipate trends and future conditions
 Realize future vision for the organization
 Align organization activities
 Gain a “competitive advantage”

5. Current AI Use in Marketing and
Financial Management
 Identify real-time patterns in financial markets
 Build predictive models for fraudulent CC
transactions, lending, blockchain, wealth mgmt
 Software robots to process transactions, monitor
compliance, as conduct audits
 Personalized product/content recommendations
 Use previous interactions to predict future
reactions

6. Steps in Traditional SP Process
 What is the business capable of doing? DATA
 Who is the business serving? DATA
 Who is it competing against? DATA
 What is the world in which it operates? DATA
 Define a future direction.
 Set strategic objectives.
 Formulate and implement strategies.
 Monitor progress and success.

7. Data Types in Strategic Planning
 Internal operations
 External markets and customers
 External industries and competitors
 External environment (economic, legal,
demographic, technologic)

8. Data Sources in Strategic Planning
 Internal records and processes
 Public government records, filings, & reports
 Free privately gathered data
 Commercial privately gathered data
 News media articles
 Academic research findings
 Competitive intelligence

9. Data Warehouse vs Data Lake
 Data warehouse – stores data extracted from
online transactions and datasets to support
business analytics queries for specific business
purposes
 Data lakes – receive relational and non-relational
from IoT devices, social media, mobile apps,
and some corporate apps
 AI-driven strategic planning utilizes data
warehouses

10. Historic Strategic Planning Practices
 Annual planning retreat
 Staff gather some relevant data in 4 domains
 Report findings to assembled executives
 Executives select strategies based on experience,
judgment, and intuition
 Compiled into a bound plan
 Return to day-to-day work duties till next year

11. Executive Satisfaction With
Planning Results
 Only one third of 300 global executives said that
their strategies met vital criteria (Bain & Co.)
 Few strategic planning processes are well-
supported across the organization. (Bain & Co.)
 45% of 800 executives surveyed were satisfied
with their strategic planning process (McKinsey)
 Only 23% of strategy decisions are made within
that process (McKinsey)

12. Problems to Be Addressed
 Infrequent planning exercises
 Delayed planning decisions
 Inadequate volumes of data
 Incomprehensive data
 Out-of-date, incomplete, obsolete data
 Data not integrated or correlated
 Data are unscientific, biased, inaccurate

13. More Problems to Be Addressed
 Decisions made on instinct, intuition, and
experience
 Resulting decisions are unscientific, misguided,
and irrational
 Strategy decisions do not work for the business
 Planning assumptions are rarely revisited
 Progress on strategies is rarely monitored
 Competitive Advantage is sacrificed

14. End Goal is “Competitive Advantage”
 Single feature essential to success in industry or
marketplace
 Advantage: If only one business possesses that
feature
 Sustainable: If no other business can copy the
feature

15. Our Solution to the Problems
Use Robotic Process Automation, Artificial
Intelligence, and Machine Learning to automate
the entire strategic planning process for every
organization in the world.

16. Solution Details
 System based entirely in the cloud
 Clients subscribe and access it remotely
 System imports and aggregates data from
various strategy-related sources
 System cleans, sorts, & analyzes the data
 Algorithms and data reside in the cloud
 Algorithm calculations take place in the cloud

17. More Solution Details
 Algorithms reach conclusions & report them to
management
 Managers can make real-time, action-oriented
strategy decisions
 Some components marketable as free-standing
products
 System tailor made for each organization

18. Animated Description

19. Benefits from Using This System
 Access to larger volume & diversity of data
 Much faster access to data
 More comprehensive data, checked for accuracy
 More precise, reliable conclusions from data
 Greater insights into markets & competitors

20. More Benefits from System
 Minimize bias in managerial strategic decisions
 More timely, up-to-date decisions
 Lightning-fast strategic decisions
 Strategic moves faster & better informed than
the competition
 Leading to a major competitive advantage

21. Some AL/ML Applications in
Strategic Planning
 Identify meaningful clusters in customer markets
 Identify product feature preferences for the
business and its competitors
 Predictive models to forecast future behavior of
competitor executives
 Predict implications of environmental events
and competitor actions
 Project historical trends into the future

22. Challenges and Difficulties
 Customer validation
 Develop system component to prove concept
and earn revenue
 Gaining access to relevant data
 Paying for necessary proprietary data
 Transforming internal data to make it useful

23. More Challenges and Difficulties
 Persuade executives to surrender some strategic
decision-making authority
 Recruit business data managers
 Recruit AI design engineers
 Measure and prove ROI for investments in the
system
 Secure substantial funding to develop full system

24. Questions?
www.ai-driven-strategy.com
George B Moseley III, MBA JD
gmoseley@post.harvard.edu
gmoseley3@gmail.com

25. Founder and CEO
George B Moseley III
 MBA (Harvard), JD (Michigan), BS (Ohio State),
Engineering (Cornell)
 Venture capital director, small-scale startups
 Strategic Planning instructor at graduate level
(Harvard, Strathmore)
 Author of second-best selling textbook on
strategic planning in health care

28. Strategic Planning
A business function through which companies …
 Prepare for the long-term future
 Respond to environment changes
 Build on resources and competencies
 Adjust to markets and consumers
 React to competitors
 Gain a sustainable competitive advantage

29. The Problem
 When strategic planning is performed well, it
can give a business a powerful competitive
advantage.
 Most businesses perform it poorly, leading to
mediocre results and a loss of management
confidence.

30. Strategic Planning Flaws
 Perform planning poorly or not at all
 Once a year, with incomplete, outdated data
 Poor judgments lead to missed opportunities
 Plan is rarely monitored or adjusted
 Distraction from operational duties
 Lack confidence in planning skills
 Fail to see its value

31. Leading to poor results

32. Solution
We propose to use Robotic Process
Automation1, Artificial Intelligence2, and
Machine Learning3 to automate the entire
strategic planning process for every organization
in the world.
1RPA Software tools that automate and standardize business processes that are manual, rule-
based, and repetitive.
2AI Smart machines able to learn from experience, adjust to new inputs, and perform tasks that
typically require human intelligence.
3ML Algorithms and statistical models used to perform a specific task without using explicit
instructions, relying on patterns and inference instead. It is seen as a subset of artificial
intelligence.

33. Initial Solution
(addressing data-intensive part of planning process)
Use AI algorithms to automate import of data on:
• resources & competencies
• markets & customers
• industries & competitors
• regulations, economics, demographics, technology
 Import maximum amounts of relevant data
 Ensure accuracy and relevance of data
 Automatic data interpretation and analysis
 Recommend strategic responses

34. Ultimate Solution
(addressing the entire planning process)
 Algorithms applied to every step in the process
 Automatic continuous strategic planning 24/7
 Planning process free of human involvement
 Prompt for human intervention
 Offer informed choices to decision makers
 Automatic monitoring of implementation

35. How It Starts
An executive enters her office at 7:30 am on a
Monday morning, turns on her desktop computer,
and a voice says:
“Something happened over the weekend that I
think you need to pay attention to.”
(merger of two competitors, change in currency
rates, new technology announced, changes in key
demographics, primary customer’s CEO retires.

36. That desktop computer is a connected to a cloud
system, which in turn is connected to data sources
which are cleaned, sorted, and analyzed, which
then reach and report conclusions.

37. The user organization identifies strategically
relevant resources and competencies, gathers data
on each of these, and generates new data if
necessary.

38. The user organization identifies strategically
relevant customers and markets, gathers data on
each of these, and generates new data if necessary.

39. The user organization identifies strategically
relevant competitors, gathers data on each of
them, and generates new data if necessary.

40. The user organization identifies strategically
relevant environmental factors (economic
conditions, laws and court decisions, demographic
reports, new technologies), gathers data on each,
and generates new data if necessary.

41. Cloud-based system aggregates data from various
strategically related sources, and translates them
into action-oriented, real-time conclusions and
recommendations.

42. Benefits of AI-Driven Strategy
 The much larger volume and diversity of data
accumulated and used in the planning process.
 The much higher accuracy of data accumulated
and used in the planning process.
 The much greater speed with which data are
assimilated, aggregated, cleaned, and analyzed.
 Management’s ability to make lightning-fast
strategic decisions.

43. Leading to a sustainable competitive
advantage

44. Why Now
 Long-standing need for a system to make
competent planning available to all
organizations
 AI science, cloud technologies, and computing
power have progressed to the point where
such a system is possible

45. Market Size
 Literally, every for-profit and non-profit
organization in the world
 Estimate over 100,000 businesses with more
than $100M in revenue
 Different types and sizes of AI-driven system
will be available for different organizations

46. Competition
 Organizations that try to plan on their own
 Strategy management consultants
 Marketing & sales products using AI
 Financial planning & analysis products using AI

47. Product/Service (A)
 Planning process divided into a system of
interlocking components
 Algorithms analyze data, deduce implications,
and recommend decisions
 Algorithms, data, and calculations reside in
the cloud
 Some components are marketable as free-
standing products

48. Product/Service (B)
 Customers subscribe to components they
want
 No single standard version of the system or its
components
 Services to tailor systems to organizations
 Services to help customers adapt their
organizations

49. Business Model - Revenues
 Subscription fees for system and components
to access the cloud
 Charges for customer product customization
 Charges for optimizing customer operations

50. Business Model - Expenses
 Salary of operations manager
 Salary of data engineers and scientists
 Salary of subject matter specialists
 Costs of data for training and testing
 Costs of support services
 Costs of the cloud

51. Product Development Plan
 Phase I: Proof of concept, an MVP, and a
working model (9 months)
 Phase II: First marketable freestanding
component product (12 months)
 Phase III: Complete pilot system for a real-
world organization (24 months)
 Phase IV: Full production of systems and
components for real-world organizations

52. Financial Projections
 Two freestanding component products
generating revenue within one year
 Within two years, full production of complete
systems will generate increasing revenues

53. Financial Resources
 Initial financing of $300,000 to support Phase I
demonstration of concept and development
of one minimum viable product (MVP)

54. Founder and CEO
George B Moseley III
 MBA (Harvard), JD (Michigan), BS (Ohio State),
Engineering (Cornell)
 Venture capital director, small-scale startups
 Strategic Planning instructor at graduate level
(Harvard, Strathmore)
 Author of second-best selling textbook on
strategic planning in health care

55. Our Business Website
www.ai-driven-strategy.com