Webinar: The AI-first workflow redesigning for growth

Who/what this is about

• Stefan Ohran (Board of Innovation) presents an approach to AI transformation centered on redesigning knowledge work, using an analogy to how factories re-engineered workflows for robots.
• Board of Innovation positions itself as an AI transformation studio focused on growth, with three topic areas:
  • Value creation (how AI changes products/services/business models)
  • Work redesign (today’s focus): redesigning knowledge work for AI capability
  • Operating model (how to organize to scale opportunities)

---

Core concept: AI-enabled vs AI-first (two transformation paths)

AI-enabled (retrofit)

• AI is added onto the existing human-driven workflow
• Gains are often limited to task-level efficiencies
• Does not scale well over time; “missed opportunities”

AI-first (redesign)

• Work is redesigned around AI capabilities
• The engine shifts:
  • AI-enabled: human = engine, AI accelerates steps
  • AI-first: AI = engine, human intervenes at key moments
• Requires changing growth, operations, and competition with AI “at the core”

---

Concrete example: RFP response process redesign

Traditional (AI-enabled retrofit)

• Linear, SME-heavy workflow:
  1. RFP intake (inbox or active BD)
  2. Context gathering
  3. Qualification (capabilities/credentials/selection criteria)
  4. Compliance checks
  5. Bid/no-bid decision (senior meeting)
  6. Proposal definition: pricing, drafting sections, QA, submission
• Headcount scales similarly to the old model
• Example impact mentioned: cycle time could improve from ~2 weeks to ~1 week (marginal gains vs structural redesign)

AI-first workflow (three phases; throughput ~3 hours total)

1. Instant intake (fully autonomous) ~1 hour

   • Parse RFP, extract requirements
   • Cross-reference CRM + past wins for context
   • Generate scorecards/elements needed for bid/no-bid
   • Run compliance checks, etc.

2. Parallel generation (AI + human in the loop) ~1 hour

   • Start proposal drafting early (cheap to iterate)
   • Draft all sections; build pricing model from past data

3. Review & submit (human driving + AI assistance) ~1+ hour

   • Human reviews for fit/comfort and prepares final submission
   • AI performs post-facto consistency audits

---

Frameworks / playbooks highlighted

“Factory lessons” translated to knowledge work

1. Value stream mapping

   • Map flow between “workstations” → in knowledge work, map decisions and information dependencies
   • Identify what decisions are waiting for (reveals real waste)

2. Task allocation

   • Decide for each task: human vs machine
   • Framed as a multi-dimensional allocation problem

3. Jidoka (Toyota principle) / escalation logic

   • Machine handles the norm, human handles exceptions
   • Build triggers for anomalies so AI stops/escalates when needed

---

6-step methodology to move from workflow → AI-first workflow

Phase 0: Prioritize

• Step 0: Set priorities for which workflow to redesign first
• Use a 2x2 matrix:
  • Strategic leverage vs Readiness
• Target: high readiness + high leverage opportunities (argued to build a competitive “moat”)

Phase 1: Map decisions

• Step 1: Map the decision flow
  • Trace decision inputs/outputs and transitions (not just activities)
  • For RFP: bid/no-bid, pricing, quality, sign-off—each waits on specific information

Phase 2: Allocate + govern

• Step 2: Define constraints + allocate work to one of three archetypes:

  1. Autonomous AI (AI executes alone)
  2. AI + human in the loop (AI drives; human provides feedback)
  3. Human + AI assistance (human drives; AI assists)

• Allocation is guided by questions:

  • Determinism: how specifiable is the task, and what’s the cost of error?
  • Judgment: does it require tacit knowledge / judgment?
  • Recurrence: is it recurrent vs non-recurrent?

• Step 3: Define escalation logic (Jidoka)

  • Examples of escalation triggers in RFP compliance:
    • Unknown requirement
    • Borderline match (e.g., coverage limit 4.8M vs requirement 5M)
    • Contradictory clauses in the RFP

Phase 3: Design human role + close the loop

• Step 4: Design the human role (upgrade, not demotion)

  • Use a Toyota-inspired guardrail:
    • Automation needs to be < ~8% for certain key processes (to keep human engagement/expertise)
  • Human responsibilities shift toward:
    • Maintaining domain expertise
    • Concentrating on judgment-intensive work
    • Providing feedback loops to improve the system

• Step 5: Measure the process and reallocate continuously

  • Metrics remain similar; observability improves
  • As models improve, the boundary of AI autonomy shifts, so allocations must be updated

---

Metrics / KPIs mentioned

No explicit numeric targets were provided, but the talk emphasizes central metric types (especially for RFP):

• Win rates
• Gross margin
• Pricing quality / pricing decisions

Also implied in task allocation:

• Risk/cost of error (used as part of the cost model, not just time savings)

---

Key takeaways (business implications)

• AI isn’t just workflow optimization—it’s workflow redesign (decision-making and system design).
• The real gains come from allocation: assigning decisions to the right agent (human vs AI) to maximize quality vs cost (including risk).
• Competitive advantage comes from system design, not merely adopting newer tools.

---

Actionable “tomorrow morning” recommendations

• Pick one workflow and run an AI-first redesign thought experiment:
  • Does it shrink drastically and become end-to-end AI-handled?
  • Is the human role elevated or minimized/removed for some segments?
  • Does the workflow change form (e.g., shift to AI-aided exception triage)?
  • Are there new workflows now possible because AI can act as a cognitive agent?

---

Presenters / sources

• Presenter: Stefan Ohran, Managing Director, Board of Innovation (AI transformation studio)