Turning Generative AI Into Real Advantage

Generative AI is moving fast. Organizations aren’t. That gap is where most initiatives stumble.

Four kinds of AI

Leaders don’t need a PhD to choose the right approach. Use this simple map:

1) Rule-based systems (expert systems)

• What they are: If/then logic codified from domain experts.

• Strengths: Fast, repeatable, explainable; great for narrow decisioning (eligibility checks, policy compliance).

• Limits: Don’t adapt well; brittle outside specified rules.

• Use when: You need consistent, auditable answers on well-understood problems.

2) Econometrics / traditional statistics

• What it is: Regression, classification on structured data (think spreadsheets).

• Strengths: Cheap to build, explainable, repeatable, strong with numeric outcomes and trends.

• Limits: Needs structured, quality data and a reasonable functional form.

• Use when: You need forecasting, scoring, or causal inference on well-defined datasets.

3) Deep learning (traditional ML at scale)

• What it is: Neural nets trained on labeled data to recognize patterns (images, speech, sensor data).

• Strengths: Superb on perception tasks; learns features you can’t easily hand-code.

• Limits: Opaque (“black box”), data-hungry, bias-sensitive, compute-intensive.

• Use when: You need high-accuracy pattern recognition and can tolerate limited explainability.

4) Generative AI (LLMs and beyond)

• What it is: Predicts the “next best token,” producing text, code, images, audio, video.

• Strengths: Creates, summarizes, translates, drafts; accelerates coding; boosts ideation.

• Limits: Probabilistic, not deterministic; hallucinates; non-repeatable outputs unless constrained.

• Use when: You want speed, creativity, and flexible language interfaces, and can add guardrails.

Decision cues for leaders

• Accuracy required & cost of being wrong: High-stakes medical or driving? Favor explainable and deterministic systems. Low-stakes marketing copy? GenAI is fine with review.

• Explainability: Needed by regulators or internal audit? Use rule-based/statistical methods or add model-explanation layers.

• Repeatability: If answers must be identical every time, avoid unconstrained GenAI.

• Data truth and bias: Check class balance (gender, age, region). If your history is skewed (e.g., past hires), models will be too.

Where GenAI helps right now

Think of GenAI as the next stage of digital transformation—same principles, more power.

1) Customer experience

• Personalized, conversational storefronts and guided selling.

• Real-time service assistants that listen and coach (e.g., prompts for de-escalation, better explanations).

2) Operations

• Document intake → structured data → automated routing.

• Warehouse notes → optimized pick paths and substitutions using natural language tools.

• Call summaries, case notes, and auto-documentation.

3) Business model tweaks

• Turning products into services with info layers (usage tips, proactive alerts).

• Content localization at scale (policies, manuals, training in any language).

4) Employee experience

• Copilots for coding, analysis, writing, and meeting notes.

• Role-specific tutors and onboarding guides.

Real-world patterns

• Lemonade (insurance): ~98% of policy issuance and first-notice-of-loss automated; ~50% of claims handled automatically. Humans take the hard cases.

• Sysco (foodservice logistics): Dozens of AI use cases across sales, planning, routing, and customer interactions—traditional AI + GenAI + standard IT.

GenAI’s double edge: creativity vs. hallucination

LLMs can draft brilliant copy—and invent citations. That’s not a showstopper if you design controls like you do for people:

• Human-in-the-loop for higher-risk tasks.

• Source grounding (RAG) to tie answers to trusted documents.

• Guardrails: prompt templates, policy checks, PII filters, and domain-restricted knowledge bases.

• Evaluation: test suites for accuracy, bias, and safety before production.

People aren’t perfect either. Expect errors, contain them, and learn fast.

Governance: centralize risk, decentralize discovery

You have two extremes—and a pragmatic middle.

Centralized (safe, slower)

• Tight review, common platforms, standard guardrails.

• Example: Société Générale collected 700 use cases, built shared components (chat agents, programming aids), then let teams build on top.

Decentralized (fast, riskier)

• Business units experiment under broad rules (“don’t break the law; don’t leak data”).

• Risk: duplication, compliance gaps, fragmented learning.

The hybrid that works

• Shared rails: identity, security, data governance, model catalog, prompt libraries, evaluation harnesses.

• Local autonomy: business teams launch within those rails.

• Portfolio logic: buy first, then rules/statistics, then traditional ML, then GenAI only if needed (Sysco’s approach).

Culture and careers: reduce fear, raise capability

GenAI will reshape tasks across ~46% of jobs (with roughly half the tasks affected). Don’t let that paralyze adoption.

• Message the win: offload drudgery; free time for creative, complex work.

• Invest in learning: communities of practice, office hours, internal promptathons.

• Codify and share what works: pattern libraries of prompts, flows, and playbooks.

• Target “creative confidence”: use GenAI to brainstorm, storyboard, and draft. Encourage divergent thinking and iteration.

• Support mobility: map new skill ladders (prompting, data literacy, model oversight, human-factors design).

Case in point: At Dentsu Creative, GenAI now drafts proposals and produces first-cut visuals in minutes, enabling live iteration with clients. The firm introduced tools with training and peer sharing, turning initial skepticism into widespread pull.

Climb the capability ladder: small “t” to big “T” transformation

Don’t wait for a moonshot. Build momentum in stages:

Level 1 — Individual productivity (low risk, fast ROI)

• Secure LLM access (vendor or private) with logging and content filters.

• Use cases: meeting notes, email rewrites, summarization, knowledge retrieval, translation, slide outlines.

• KPI ideas: time saved per task, adoption rates, satisfaction.

Level 2 — Role/task transformation (moderate risk)

• Coding copilots, service agents with human in the loop, sales call coaching, claims triage.

• KPI ideas: handle time, first-contact resolution, quality/defect rates, conversion lift.

Level 3 — Direct customer engagement (higher visibility)

• Conversational shopping, personalized onboarding, tier-1 support bots grounded in your docs.

• KPI ideas: NPS/CSAT, AOV, self-serve containment, deflection with satisfaction.

Level 4 — End-to-end process redesign (highest payoff, most change)

• Intake → decision → fulfillment with combinatorial AI: GenAI for unstructured intake, traditional AI for scoring/routing, and rule engines for policy enforcement.

• KPI ideas: cycle time, cost per transaction, exception rates, compliance findings.

Think “lug-nut pattern,” not “one bolt to 100%.” Tighten a little across multiple parts, learn, then tighten again. Each win funds the next.

A simple operating model for GenAI

Use this as a one-page blueprint:

1) Strategy & pipeline

• Define your north-star business outcomes (e.g., 15% faster claims, 10-point CSAT lift).

• Source use cases bottom-up and top-down. Score by value, risk, effort, and data readiness.

• Maintain a rolling 90-day portfolio with clear owners and KPIs.

2) Platform & guardrails

• Central platform for model access (vendor + private), identity, logging, prompt/mask libraries, retrieval (RAG), and evaluation.

• Data governance: approved sources, lineage, PII controls, retention.

• Pre-production safety tests: accuracy, bias, jailbreak/PII, policy compliance.

3) Product teams

• Cross-functional pods: product owner, designer, SME, data/ML engineer, platform engineer, risk partner.

• Design for human-in-the-loop by default; automate when evidence supports it.

• Ship small; measure relentlessly; iterate.

4) People & change

• Learning paths by role (operator, analyst, engineer, leader).

• Communities of practice; internal showcases; pattern libraries.

• Role redesign with clear expectations and advancement routes.

5) Risk & compliance

• Model registry with owners and purpose.

• Documentation: prompts, data sources, evaluation results, change logs.

• Monitoring: drift, toxicity, leakage, answer quality; kill-switch playbooks.

KPIs that matter (by layer)

• Adoption: weekly active users, use per user, team penetration.

• Efficiency: time saved, tasks automated, cycle time, error rates.

• Effectiveness: conversion lift, resolution rates, quality scores, revenue per rep/agent.

• Experience: CSAT/NPS, employee satisfaction, rework/escapes.

• Risk: incident counts, policy violations, hallucination rate in audit samples.

• Economics: ROI per use case, payback period, platform cost per outcome.

Tie each metric to an owner and a review cadence.

Practical prompts and patterns to standardize

• RAG grounding: “Answer using only the attached policy. If the policy doesn’t cover it, say you don’t know and escalate.”

• Tone controls: “Rewrite in a clear, friendly tone for a non-technical audience in under 120 words.”

• Decision support: “Summarize the customer’s last three interactions and propose the next best action with rationale and confidence.”

• Code assist: “Refactor this function to our performance guide and add docstrings and unit tests.”

• Coaching: “Listen to this call transcript. Identify confusion points and suggest three phrases the agent could have used to clarify.”

Store proven patterns in a shared library with examples.

Leader checklist

1. Name an executive owner for AI value creation (not just “AI adoption”).

2. Publish your policy (what’s in/out of bounds) in plain language.

3. Stand up a small platform team to provide safe model access, RAG, and evaluation.

4. Pick five low-risk use cases (1–2 per function) and ship inside 60–90 days.

5. Instrument everything: define success upfront and measure weekly.

6. Launch an internal guild (office hours, demos, pattern sharing).

7. Create a lightweight model registry with owners, data sources, and tests.

8. Plan the next rung up (one role transformation, one direct customer pilot).

9. Align incentives so teams get credit for time saved and quality improved.

10. Communicate progress to the whole org; celebrate real outcomes, not model counts.

The bottom line

• Be intelligent about “artificial intelligence.” Expect errors; design guardrails; learn fast.

• Start with the problem, not the model. Many wins come from combinations of GenAI, traditional AI, rules, and boring but essential IT.

• Climb the risk slope deliberately. Move from individual productivity to role transformation, then customer engagement, and finally end-to-end process redesign.

• Lead the culture. Reduce fear, amplify learning, and show employees how AI makes their work better—not smaller.

Transformation is a leadership job. Get the rails in place, give teams room to run, and keep turning the lug-nuts—one thoughtful quarter at a time.