Synopsis
Want to stay ahead of the game with AI innovations? As automation takes hold of more job functions, businesses need to develop the right tools and skill sets to stay efficient and competitive. This AI Strategy presentation reviews Upskilling Opportunities, Corporate Use Cases, AI-Integrated Product Mix, AI-Enabled Customer Journey, Intelligent CRM, end-to-end automation capabilities, innovation hype cycle, and many more tools that can be downloaded and customized to various business needs. Let's review how these tools work, and how each one can turn ambitious ideas into reliable execution.
Upskilling opportunities
Many job functions will, with enough time, be automated. As this happens, it's important that your employees can continue putting their skills to use. Use this Upskilling Opportunities table to brainstorm how to repurpose the talent pool and continue to offer career growth.
On the left side, identify the role's current labor supply and demand and automation potential. Keep tab of any existing skills that can be transferred to future roles. On the right, list the possible upskilling options. It'd also be helpful to show how much each option matches the role's existing capabilities.
Corporate use cases
This Corporate Use Cases breakdown visualizes how AI tech and tools can be integrated into departments and their specific job functions. In this Marimekko chart, the bigger the box, the greater the AI use case. When it comes to AI corporate use cases, think of it as a meeting of process improvement and digital transformation.
For example, for a risk and compliance department, tasks like scenario analysis and threat detection could be very suitable for AI-integrated solutions. List your organization's departments and their functions on this Merimekko chart.
As an alternative visualization, this slide uses tables to itemize the business value and feasibility of various use cases. You can score each area on a scale of "none" to "very high" or use a numerical scoring system for more precision.
AI-integrated product mix
While AI can optimize an organization's internal workflow, its impact can also be made on the public-facing end. How would you integrate AI into your products or services to delight customers? And how would you visualize the degree to which each old or new product is powered by AI?
On this product wheel, the products or features listed on the outer edges tend to be more AI-driven. Level 1 is for offerings that reliably generate revenue for the time being, and level 4 includes experiments that could potentially be the big billion-dollar ideas. Take Google as an example. Many of its products already have some degree of AI capacity. For one, Google Maps uses AI for its Driving Mode feature. With this tool, communicate with your team members, stakeholders, or even customers on the exciting possibilities ahead.
AI-enabled customer journey
Realistically, not every business is ready to tackle full-on AI products just yet. But even with the existing customer base, parts of the customer journey can be automated — think chatbots for customer queries or virtual shopping assistant. Using this AI-Enabled Customer Journey tool, see how automated your service currently is, and plan how much more you can do in the coming years. Say you run an ecommerce business. You may already use a chatbot for customer queries. But could you use AI to better predict customer preferences and offer new product suggestions?
Intelligent customer engagement (ICE)
Staying on the theme of customer experience, the Intelligent Customer Engagement tool — or ICE — compares two stages: the use of AI today, and the use of AI tomorrow. In this hypothetical scenario, we have a business that currently uses up a lot of resources on call centers. But the plan going forward is to automate that area and cut its spending by half.
End-to-end automation capabilities
So far, we've talked about how AI can be leveraged on both the enterprise level and the customer level. Now, let's bring them all together into an End-to-End Automation blueprint. (Note that every AI use case will be applicable to your business goals over time, so add or take out any of them based on your focus areas.)
- On the left, consider the in's and out's of your product offerings and how they impact the customers.
- On the right, plot the ways that your organization's internal processes can be upgraded to meet higher market expectations and demands.
- Last but not least, understand that AI integration still needs to be complemented by an organic, human dimension for it to be a truly helpful and synergetic system.
Hype cycle
Many exciting new technologies go through the journey of a Hype Cycle. Here, the AI Development Hype Cycle graphs out the general expectations about the technology over time, from skyrocketing growth to an eventual plateau. The graph's X-axis represents time. The Y axis represents expectations about AI. In other words, how excited people are about it as a force for change.
Plot the current AI strategic focus of your organization on this graph and where they fall on the curve. This is indicated by the purple points. Additionally, you can plot the strategic focus of your competitors. Are they reaching for the peak that gets all the public love and attention, or have they already moved into the trough of disillusionment?
- Physical & Manual Skills: Decreasing significantly by -14% (dropping from 203 to 174 hours).
- Basic Cognitive Skills: Decreasing the most by -15% (dropping from 115 to 97 hours).
- Higher Cognitive Skills: Growing steadily by +8% (rising from 120 to 140 hours).
- Social & Emotional Skills: Growing strongly by +24% (rising from 119 to 148 hours).
- Technological Skills: Experiencing the highest surge at +55% (rising from 73 to 113 hours).
Overall, the chart highlights a clear automation-driven trend: traditional, repetitive, and manual tasks are declining, while human-centric skills (social/emotional) and technical expertise are becoming highly critical for the future economy.
- Job Functions (Y-Axis): Rows cover various business areas including Finance, HR, Marketing & Sales, Operations, Product Development, Risk, Strategy, and Supply-Chain Management.
- Techniques (X-Axis): Divided into two main groups:
- Focus of Report: Advanced AI methods like Reinforcement Learning, Feedforward Networks, Recurrent Neural Networks, Convolutional Neural Networks, and GANs.
- Traditional Analytics Techniques: Methods like Tree-based Ensemble Learning, Dimensionality Reduction, Classifiers, Clustering, Regression Analysis, Statistical Inference, Monte Carlo, and Markov Processes.
- Heatmap Legend: Bright cyan blocks represent a high number of use cases, while dark blue blocks indicate low adoption.
- Marketing & Sales and Supply Chain lead AI adoption: These two business functions show the highest concentration of high-volume use cases (bright cyan blocks) across both advanced neural networks and traditional analytical models.
- Heavy reliance on Traditional Analytics and Tree Models: Techniques like Feedforward networks, Tree-based Ensemble learning, and Regression Analysis remain the workhorses of business optimization, showing widespread, high-density implementation across multiple core functions.
- Niche application areas: Advanced techniques like Recurrent and Convolutional Neural Networks are highly concentrated in specific areas (likely Product Development and Supply Chain/Manufacturing for computer vision or time-series tracking), rather than being universally distributed.
- Risk Management heavily favors specific mathematical modeling: The Risk function bypasses most deep learning models in favor of targeted Regression Analysis and Monte Carlo simulations to project and mitigate vulnerabilities.
- X-Axis (Job Functions): Lists nine distinct organizational functions, ranging from high-impact areas like Marketing & Sales to administrative areas like HR.
- Y-Axis (Economic Value): Visualized through dual-colored peaks showing two tiers of value realization for each function (measured in trillions of dollars).
- Legend Context: The legend specifies two distinct data layers, tracking total potential value generated by advanced AI/deep learning versus broader analytical techniques.
- Marketing & Sales holds the highest economic potential: This function represents the single largest area of value creation, reaching an estimated potential of 3.3 to 6.0 trillion dollars at its upper bounds.
- Supply Chain and Manufacturing is the secondary driver: Coming closely behind Marketing, this operational domain holds a massive potential value of 3.6 to 5.6 trillion dollars, solidifying front-office and supply operations as the primary areas for AI investments.
- Massive drop-off in back-office and support functions: Administrative and strategic functions—such as Risk, Product Development, Strategy, Finance, and HR—hold comparatively minimal value potential (averaging between 0.1 to 0.9 trillion dollars each).
- Concentrated economic impact: The chart proves that AI's commercial value is heavily uneven; unlocking the vast majority of the projected trillion-dollar global impact relies almost entirely on optimizing customer-facing interactions and supply chain logistics.
According to the data, Marketing & Sales and Supply-Chain Management & Manufacturing stand out as the two dominant pillars of economic impact, dwarfing all other corporate functions combined. Marketing & Sales captures the single highest potential value, estimated to unlock between $3.3 and $6.0 trillion globally by driving optimized customer interactions, predictive demand generation, and personalized engagement. Closely following is Supply-Chain Management & Manufacturing, which commands a massive value potential of $3.6 to $5.6 trillion through automated logistics, predictive maintenance, and optimized inventory workflows. Together, these two operational engines represent the core front-office and back-end pillars where AI implementation yields the highest immediate financial returns and scalability.
- Work Activity Categories (X-Axis): Divided into seven core activity types, ranked on a spectrum from "Less Automatable" to "More Automatable." Each category displays a percentage reflecting its share of overall time spent across the studied work environment.
- Organizational Units (Y-Axis): Rows isolate Department A, Department B, and Department C.
- Technical Feasibility Legend: A color-coded scale ranging from 0% (grey/dark purple) to 100% (bright cyan) that signifies the percentage of time spent on activities that can be automated by adapting currently demonstrated technology. The size of the blocks also corresponds to the volume of time allocation.
- Predictable Physical Work offers the highest immediate automation potential: Accounting for 31% of overall time spent, this activity category shows the highest technical feasibility for automation, particularly within Department A and Department B, where the bright cyan indicators signify nearly 100% feasibility.
- Department A is highly exposed to structured automation: Department A features large, high-feasibility blocks concentrated under predictable physical work, data processing, and data collection, making it the prime target for an immediate automation rollout.
- Unpredictable Physical Work heavily impacts Department C: While unpredictable physical tasks account for 21% of the general baseline, Department C has an exceptionally large concentration of time dedicated to this category. However, the purple shading indicates a lower overall technical feasibility for automation compared to structured physical work.
- Human-centric leadership and specialized expertise remain highly resistant to automation: Activities involving Managing Others (3%) and Applying Expertise (8%) show consistently low technical feasibility across all three departments, highlighting that strategic, interpersonal, and highly specialized creative roles face minimal threat from automation technology.
- The Decline of Physical and Routine Labor: The high automation feasibility (up to 100%) seen in Predictable Physical Work (31%), Data Collection (14%), and Data Processing (10%) perfectly explains the projected -14% drop in Physical/Manual Skills and the -15% drop in Basic Cognitive Skills. Routine tasks in departments like Department A are being rapidly systemicized by modern software and robotics.
- The Insulation of High-Cognitive Leadership: Interpersonal and specialized categories like Managing Others (3%) and Applying Expertise (8%) show negligible automation potential across all business units. This directly underpins the surging market demand for Social & Emotional Skills (+24%) and Higher Cognitive Skills (+8%), emphasizing that strategic decision-making and empathetic leadership cannot be replicated by automated systems.
- The Operational Imperative: The uneven distribution of risk—such as Department A's extreme exposure to structured automation versus Department C's heavy reliance on highly variable, unpredictable physical labor—means that workforce reskilling cannot be uniform. Organizations must aggressively transition routine personnel into technological oversight or client-facing roles.
- Data-Science Workflow (X-Axis): Outlines the six chronological stages of a typical data project: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment.
- Effort Allocation (Y-Axis): Compares the human resource investment between Effort Today and Effort Tomorrow.
- Role Types: Uses color-coded icons to differentiate between Data-Science Expertise (blue figures) and Business Domain Knowledge (grey figures).
- The Disruption Zone: Highlights a central bracket spanning from Data Understanding through Evaluation, explicitly labeled as "Heavily Affected By AutoML".
- AutoML drastically collapses intermediate workflow stages: The technical core of data science—specifically Data Preparation, Modelling, and Evaluation—is projected to experience an extreme reduction in human labor. Automation technologies handle the heavy lifting, leading to a massive drop in required workforce effort tomorrow.
- Human effort shifts heavily to the extremes of the workflow: While the middle processing stages shrink, the initial stage (Business Understanding) and the final stage (Deployment) remain completely untouched by automation. They retain identical, high levels of human effort tomorrow as they do today.
- Business domain knowledge becomes relatively more critical: In the future workflow ("Effort Tomorrow"), the blend of pure data science technical expertise shrinks across four out of the six stages. The remaining un-automated stages heavily rely on grey figures, proving that cross-functional communication, identifying corporate use cases, and strategic context (Business Understanding) cannot be automated out of the cycle.
- Operational deployment is highly resilient: The actual integration of machine learning models into live production environments ("Deployment") remains highly complex and human-dependent, requiring a sustained, balanced effort of both data scientists and domain experts.
- Technical Automation Potential (Top Banner): Establishes baseline capabilities of current technologies, highlighting that ~50% of current work activities are technically automatable, and 6 out of 10 current occupations have more than 30% of activities that can be automated.
- Impact of Adoption by 2030 (Bottom Grid): Breaks down potential labor market displacement across three adoption velocity scenarios (Slowest, Midpoint, Fastest) using two distinct metrics:
- Work potentially displaced: Measured by the percentage and raw volume of Full-Time Equivalents (FTEs).
- Workforce needing to change occupational categories: Showing the scale of necessary population reskilling.
- Massive scaling variance based on adoption speed: The economic impact is highly volatile depending on global implementation rates. Under a slow scenario, displacement is negligible (10 million workers), whereas a fast adoption scenario triggers a massive displacement of up to 800 million workers (30% of the global workforce).
- Widespread but partial occupational exposure: Because 60% of all jobs have at least 30% of their day-to-day tasks automatable, automation will radically transform how most people work rather than completely eliminating their job titles immediately.
- A historic global reskilling crisis is possible: In the fastest adoption scenario, up to 375 million workers (14% of the global workforce) will be forced to completely transition out of their current occupational categories into entirely new fields. This highlights an urgent macro-economic need for large-scale corporate and governmental retraining programs.
- The "Midpoint" serves as a conservative corporate baseline: Even under a balanced, moderate adoption trajectory, 400 million workers (15%) face displacement, and 75 million workers will need to pivot careers, signaling that workforce disruption is inevitable regardless of market friction.
- The Return Matrix (Center Circle): Compares the yield between two investment strategies across two core metrics: Hours Freed and Potential Savings.
- High Investment (Top Half): Assumes tasks speed up by 200%.
- Low Investment (Bottom Half): Assumes tasks speed up by 20%.
- Resource Optimization Chart (Right Side): Tracks "Person-hour per year for task" over a two-year horizon (Year 1 vs. Year 2), illustrating the decline in labor requirements as optimization takes effect.
- High upfront investment yields disproportionately massive returns: Transitioning from a low-investment strategy to an aggressive, high-investment strategy scales the financial return by more than 12x (jumping from $3.3B to $41.1B in potential savings).
- Extreme operational capacity is unlocked through aggressive automation: A high-investment strategy frees up a staggering 1.2 Billion hours of workforce capacity globally, compared to a modest 96.7 Million hours under a low-investment blueprint. This massive pool of freed time provides the exact fuel needed to satisfy the surging demand for higher cognitive and social skills discussed in previous slides.
- Consistent year-over-year operational efficiency: The bar chart on the right indicates that targeted optimization creates compounding multi-year efficiency, driving down necessary person-hours per task continuously between Year 1 and Year 2.
- Incrementalism leads to missed financial opportunities: A low-investment approach (resulting in a minor 20% task speed up) nets only $3.3B. For massive enterprises, an incremental, timid rollout creates an immense opportunity cost compared to an all-in technological transformation.
- Functional Streams (X-Axis): Evaluates eight corporate pillars: Capital Allocation, Research, Chip Design, Manufacturing, Procurement, Sales & Operations Planning, Sales and Pricing, and EBIT from Increasing Revenue.
- Maturity Horizons (Legend): Breaks down value realization into three distinct stages: Current Gains (bright cyan), Near-term Potential (medium cyan), and Long-Term Potential (dark blue).
- Macro Impact Callouts (Bottom Banners): Quantifies the resulting overall potential of AI into three financial buckets: a 28-32% reduction in research & design costs, a 13-17% reduction in cost of goods sold (COGS), and a 10-14% reduction in SG&A spending.
- Manufacturing is the overwhelming driver of long-term AI value: Manufacturing stands out as the single largest beneficiary, displaying a massive vertical bar dominated by long-term potential. This category underpins the projected 13-17% reduction in COGS.
- High-tech design processes are ripe for disruption: AI delivers a dramatic optimization wave to technical engineering, leading to a massive 28-32% reduction in research & design costs, fueled directly by the significant growth potential shown in the Chip Design and Research pillars.
- Significant commercial runway for revenue generation: The "EBIT from Increasing Revenue" bar shows that while current baseline gains are modest, the near- and long-term financial growth derived from AI-driven top-line expansion is highly substantial, ranking second only to manufacturing.
- Pervasive operational cost deflation: The data proves that AI acts as a deflationary force across the entire corporate structure, simultaneously lowering technical R&D, structural supply chains/manufacturing costs, and back-office SG&A expenses.
- Axes: The Y-axis tracks operational Cost (scaled from 0 to 90,000), while the X-axis tracks time in Years (from Year 1 to Year 10).
- The Performance Lines:
- Conventional (Top Purple Line): Illustrates the baseline cost trajectory under traditional operational models.
- AI (Middle Cyan Line): Shows the reduced operating cost achieved by implementing artificial intelligence.
- Cost Saving Conventional-AI (Bottom Blue Line): Tracks the widening financial net difference (savings gap) between the Conventional and AI baselines over time.
- Current Stage Overlay: A vertical highlighted banner isolates the transition period between Year 6 and Year 7, marking the company's current position along the timeline.
- Compounding efficiency over time: While both conventional and AI operational costs scale upward as the business grows, the cost curve for AI grows at a significantly slower rate. This demonstrates that AI delivers compounding structural efficiencies rather than just a one-time fixed cost reduction.
- The savings gap is accelerating: The bottom net savings line climbs steadily year over year. The financial advantage of AI implementation expands from a minor delta in Year 1 to its highest point in Year 10, proving that long-term commitment yields the most aggressive return on investment (ROI).
- Critical inflection point at the "Current Stage": Positioned at Year 6–7, the organization is currently entering the steep part of the curve where the cost paths diverge aggressively. Moving forward past this milestone, maintaining conventional systems becomes exponentially more expensive than running AI-driven operations.
- Scalability advantage: The widening gap between the purple and cyan lines indicates that AI systems possess vastly superior operational leverage, allowing the business to handle increased volume or complexity without a linear surge in overhead costs.
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