Three Strategic Options for AI-Agent-First Management: CEO, Talent Development, or M&A — Which Path to Choose [2026 Edition]

Hello, this is Hamamoto from TIMEWELL.

Over the past six months, I have had back-to-back AI strategy conversations with executives ranging from listed-company board members to startup founders. The substance of those conversations is remarkably similar. They tell their teams to "use AI," yet somehow they do not feel their company's competitive edge improving. There is no sign of productivity rising by an order of magnitude. The investment line just keeps swelling. So they panic and try to hire — only to freeze when they see the going market rate.

I have spent the last several months trying to pin down the source of this discomfort. The conclusion is clear. Most companies are stuck at the stage of "introducing AI as a tool," and have not advanced to the stage of "rebuilding the management structure itself on the assumption of AI agents." This piece is the first in TIMEWELL's new series, "AI-Agent-First Management." It lays out the three strategic options available to executives and how to combine them for each stage of corporate growth.

"Using AI as a tool" and "AI-agent-first management" are different things

Let me draw a clean line here. Using AI as a tool means each employee opens ChatGPT or Claude to draft emails or summarize meetings. It is convenient, and I use it daily. But based on my experience, this alone barely changes a company. At best, it saves one or two hours per person per week and never translates into organizational competitiveness.

AI-agent-first management is two levels above that. A single employee runs ten AI agents in parallel; an executive runs a hundred. They drive research, analysis, implementation, internal coordination, and sales prep around the clock without stopping. Humans then concentrate on "final judgment" and "interpersonal communication that actually matters." This becomes the new unit of work.

This change is no longer a fantasy. At Cloud Next in April 2026, Google announced that 75% of new code is now generated by AI and approved by engineers[^1]. It was 25% in October 2024, 50% in fall 2025, and 75% by April 2026. That is roughly 15 points of acceleration every six months. They also reported that complex code migrations using their internal tool "Antigravity" finished six times faster than they did a year earlier with engineers alone. This is no longer "AI assists." We have entered the stage of "AI does the heavy lifting and humans approve."

According to KPMG's Global AI Pulse Q1 2026, companies operationally running AI agents in their core business have reached 54%, up steeply from 11% two years ago and 33% in mid-2024[^2]. 80% already report tangible economic impact. BCG's AI Radar 2026 finds 82% of CEOs are more optimistic than a year ago, and AI investment is expected to nearly double from 0.8% to 1.7% of revenue in 2026[^3]. The market has moved beyond "should we use it" to "how many agents are we running, and who runs them."

From here, the executive's options split into three: become the command center yourself, develop internal talent, or acquire capability externally. Let's go through each.

Option 1: The executive becomes the command center for AI agents

This is the one I push hardest. The reason is simple — the leverage on the executive themselves is the largest. The impact of one employee working twice as effectively with AI is dwarfed by the executive making decisions ten times faster, conducting external negotiations with ten times the density, and validating business hypotheses with ten times the granularity. The orders of magnitude are different.

Looking back at a recent day of mine, I am running 5 to 10 sessions of Claude Code at any given time, and during peak hours more than 20 agents in parallel. Spec discussions for our own services, research for column articles, due diligence on hiring candidates, identification of contract review angles, simulations for new business lines. Had I delegated these to staff, the coordination overhead alone would take a week. I parallel-process them overnight by myself and lay them out as decision-making material at the morning executive meeting. The number of moves available to the executive physically changes.

The point here is not "everyone should work the way I do." When the executive can use AI agents as their own hands and feet, the resolution of the company-wide AI strategy improves by an order of magnitude. BCG's research shows that roughly 75% of CEOs are now the top AI decision-makers in their company, and half also feel "my job is at risk if AI doesn't deliver"[^3]. Making decisions of that weight in a domain where you have no firsthand sense is, frankly, unworkable. Investment levels and talent strategies should be set only after the executive themselves has run agents and felt, "this work no longer needs a human."

How do you actually do this. First, lock in 20% of the executive's working hours as "time to run AI agents." If you work 40 hours a week, that is 8 hours. Whether it is Claude Code, Gemini, or OpenAI's Codex, what matters is having an agent runtime at your fingertips. With Anthropic's Managed Agents, announced in April 2026, you can leave runtime orchestration, sandboxing, and credential management to the platform and just write the business logic[^4]. Early adopters like Notion, Rakuten, Asana, and Allianz are already deploying it. Pricing is the standard Claude model usage plus 0.08 dollars per agent execution hour. Even running 500 hours a month comes out to about 6,000 yen. If that lifts the executive's decision speed by 10%, it is an absurdly cheap investment.

On top of that, share the prompts you ran and the agents you built with the organization as "Claude Skills." With the major Claude Cowork update in February 2026, Anthropic rolled out the ability to deploy Skills company-wide via organizational directories[^5]. If the executive distills their decision-making patterns into Skills, those become "the strongest replicas of the CEO" for the rest of the company. This is the launching point for the talent development of Option 2.

Option 2: Build the organization by developing AI-savvy internal talent

The second path is to seriously develop AI-savvy talent inside the company. Even if only the executive can use AI, the organization will not move. Performance only changes once frontline doers and middle managers reach the state of "half my work is already running on agents." Many executives give up here, but I think giving up is premature.

McKinsey's State of AI 2025 reports that 88% of companies now use AI somewhere in their operations regularly[^6]. However, only about 30% answered that they were "scaling it organization-wide" — the majority remain stuck in pilot mode. Conversely, the companies that break out of this pull cleanly ahead. KPMG's research lists "integration with existing systems" as the biggest hurdle for 46% of companies[^2], and that is an organizational issue, not a technical one.

What I have found effective with clients is a "two-stage rocket" design that runs top-down and bottom-up simultaneously. On the top-down side, the CEO and executives share a common library of Skills and prompts and meet weekly to share "this is the result I produced with this agent." On the bottom-up side, give your top frontline employees Claude Code, Anthropic Managed Agents, and where appropriate the GraphRAG platform of ZEROCK, and grant them discretion to agentify their own work.

When this happens, if no one can see who inside the company is building what kind of agent, you end up reinventing similar wheels over and over. This is where Claude Skills' organizational sharing functionality and the knowledge control features of ZEROCK come into play. ZEROCK is an enterprise AI built on AWS domestic servers that organizes past meeting notes, contracts, internal approvals, regulations, and case histories into a structured GraphRAG. Whether your AI agents can move while understanding "the history of our company" significantly affects how efficiently you can develop talent. If new hires can pull veteran tacit knowledge through agents, ramp-up can be shortened by months.

To be candid, talent development takes time. In my experience, even when you go all-in, you need to commit for half a year to a year. BCG also positions 2026 as "the year to build organizational capability"[^3], and you should not over-expect immediate financial impact. That is exactly why the framing becomes: run Option 1 with the CEO leading from the front, prepare Option 2 in the background, and fill the gaps with Option 3. WARP provides exactly this design — an executive-led, monthly-updated companion-style consulting engagement.

Option 3: Buying time through external acquisition (M&A and hiring)

The third option is acquiring capability from outside. This includes both hiring battle-ready talent and acquiring AI-native companies. It is the move of "buying time with money," and unlike Options 1 and 2, which are long games, it is the fastest way to lift your company's AI implementation capability.

Let's start with the reality of the talent market. The average equity compensation for OpenAI's roughly 4,000 employees has reached the equivalent of 150 million yen per year as of 2025[^7], and top researchers are in the 1-billion-yen-plus range. In June 2025, Meta reportedly offered compensation packages totaling around 10 billion yen, including signing bonuses, to key OpenAI researchers[^8]. Sam Altman's remark that "if you can build a model for a billion dollars, paying one engineer a billion yen is relatively cheap" captures the Silicon Valley reality with brutal clarity.

The number of companies in Japan that can write checks at this level is limited, but the logic is the same. A person who can design and operate 100 to 1,000 AI agents and extract a billion yen of profit contribution from them is fully a rational investment at 100 to 200 million yen of compensation. I think the very idea of pricing AI talent against "market rates" should be abandoned. The only benchmark is how much they will earn for your company by running agent fleets. If you back-solve from the return, the parochial debate about "in our company, we can only go up to division-head pay" loses meaning.

The same logic applies to M&A. Already in 2026, Apple acquired Israel's Q.ai in January for an estimated 1.5 to 2 billion dollars, securing roughly 100 AI talents[^9]. Meta acqui-hired the entire team of agentic AI startup Dreamer in March[^10]. OpenAI bought Hiro Finance in April[^11]. The aim is partly the technology, but more importantly to absorb a packaged amount of AI implementation capability in a single move. In Japan too, cases where acquiring a mid-sized startup growing on AI agents is three years faster than building from scratch will only multiply.

That said, let me note the trap when you choose this path. If externally acquired talent or the acquired company does not stick, they vanish in three years. If the acquiring side's executive and management lack the resolution of agent operation built up through Option 1, top talent will judge "this place can't make full use of me" and walk away. So Option 3 only pays off on top of the foundation built by Option 1. This is the same rut as the previous DX talent hiring boom. To avoid "buy and forget, hire and forget," the executive on the receiving side has to keep running agents themselves.

How to combine the three options (recommendation by company stage)

For those wondering "okay, which one should I actually do," let me share my field-tested weighting by company stage.

For startups and small companies with 30 employees or fewer, you should go overwhelmingly with Option 1. Just by having the CEO and a handful of co-founders master agent operations, you can move three times faster than competitors. For employee development, simply handing out the Skills the executive made is enough at first — it is not yet the stage to build out a training program. External acquisition is enough at the level of bringing in freelance AI engineers on a contract basis for specific needs. Keeping fixed costs down is the highest priority.

For mid-sized companies with 100 to 300 employees, the realistic answer is running Options 1 and 2 in parallel. The CEO and 5 to 10 executives go after Option 1 while you give 20 to 30 frontline key people the discretion to operate agents — over six months to a year, develop these 30 into "employees who have agentified half of their own work." Bringing in a knowledge platform like ZEROCK at this point and structuring the tacit knowledge scattered around the company elevates training efficiency another notch. For Option 3, the natural shape is hiring only for key positions like CTO-level and CAIO-level from outside.

For enterprises with 1,000+ employees, you have to run all three in parallel. Executive education (Option 1), a company-wide talent development program (Option 2), and M&A of agent-focused startups (Option 3) form a trinity. Anthropic's Managed Agents and Claude Skills' organizational deployment features really come into their own at this scale. Allianz's announcement of a company-wide rollout of custom agents tailored to the insurance industry is symbolic[^4]. From here on, large enterprises that own "industry-optimized agent fleets for their own business" — accepting the vendor lock-in risk — will be the ones widening the gap with competitors.

What I want to say to companies at every stage is that the order of these three options matters. Skipping Option 1 and jumping straight to Option 2 or 3 will always cause you to stall somewhere. Do not have the courage to invest people and money in a domain where you, the executive, have no firsthand resolution. Conversely, stopping at Option 1 alone leaves you with a company that depends on the executive's individual technique and cannot be handed over or scaled horizontally. With Option 1 the executive develops their gut feel; with Option 2 that gut feel is diffused into the organization; with Option 3 you buy time. When this order breaks down, AI investment starts spinning in the air.

Summary: Three steps to start moving in 90 days

Let me close by translating all of the above into a 90-day plan you can start tomorrow. It is essentially what I have my WARP clients do in their first 90 days.

The first 30 days should be spent building the executive's own agent environment. Actually subscribe to Claude Code, Anthropic Managed Agents, and where needed Gemini's Antigravity, and pick one of your decision-making tasks to agentify. The first one can be anything. In my case I started with a due-diligence agent for new business partners. Once one task moves, that prompt and Skill become an organizational asset.

In the next 30 days, pick three to five key people inside the company and hand them the Skills the executive built. In parallel, start structuring the company's knowledge. Get meeting notes, contracts, regulations, and past case reports into a form agents can reference. If you are deploying an enterprise AI platform like ZEROCK, starting the implementation design at this timing means you can enter operation in the back half of the 30 days.

In the final 30 days, decide whether external acquisition is necessary. If your internal key people are growing at the expected pace, the extension of Option 2 is enough. If you sense their development is not keeping up with the business plan, do not hesitate to trigger Option 3. Take CTO-class and CAIO-class hiring, or M&A of an AI-native company, seriously. Saying "from next year" almost always ends with a competitor taking the prize.

Lastly, this is something I tell myself as a CEO. AI-agent-first management is not a question of technology — it is a question of executive resolve. Which of the three options you take first will determine where your company is three years from now. In future installments of this series, we will dig into each option and write down the operational design at field level. If you find yourself wanting to discuss "how should we design this for our company," feel free to reach out to TIMEWELL's WARP. As your CEO-dedicated companion, we will run the first 90 days alongside you.

Related articles you may find useful.

• Google Cloud Next 2026 and the AI-Agent-First Enterprise
• A Guide to Running Multiple Agents as a Team with Claude Code
• Superpowers: How to Leverage the Claude Code Plugin
• Sharing Organizational Knowledge with Claude Code Skills

References

[^1]: Fast Company, "Google CEO Sundar Pichai says 75% of the company's code is AI-generated" https://www.fastcompany.com/91531519/google-ceo-says-75-of-the-companys-code-is-ai-generated [^2]: KPMG, "Global AI Quarterly Pulse Survey: Q1 2026" https://kpmg.com/xx/en/our-insights/ai-and-technology/ai-pulse.html [^3]: BCG, "As AI Investments Surge, CEOs Take the Lead" (2026) https://www.bcg.com/publications/2026/as-ai-investments-surge-ceos-take-the-lead [^4]: VentureBeat, "Anthropic's Claude Managed Agents gives enterprises a new one-stop shop" https://venturebeat.com/orchestration/anthropics-claude-managed-agents-gives-enterprises-a-new-one-stop-shop-but [^5]: Anthropic, "Provision and manage Skills for your organization" https://support.claude.com/en/articles/13119606-provision-and-manage-skills-for-your-organization [^6]: McKinsey, "The State of AI: Global Survey 2025" https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai [^7]: Fortune, "OpenAI is paying workers $1.5 million in stock-based compensation on average" (2026) https://fortune.com/2026/02/18/openai-chatgpt-creator-record-million-dollar-equity-compensation-ai-tech-talent-war-career-retention-sam-altman-millionaire-staff/ [^8]: Fortune, "Meta's $100m signing bonuses for OpenAI staff are just the latest sign of extreme AI talent war" https://fortune.com/2025/06/18/metas-100-million-signing-bonuses-openai-staff-extreme-ai-talent-war/ [^9]: Founders Forum, "AI Acquihires: How Microsoft, Google, & Meta Acquire for Hire in the Talent Wars" https://ff.co/ai-acquihires/ [^10]: Creati.ai, "Meta Acqui-Hires Entire Team of Agentic AI Startup Dreamer" (March 2026) https://creati.ai/ai-news/2026-03-24/meta-acqui-hires-dreamer-ai-startup-agentic-ai-agents/ [^11]: TechCrunch, "OpenAI has bought AI personal finance startup Hiro" (April 2026) https://techcrunch.com/2026/04/13/openai-has-bought-ai-personal-finance-startup-hiro/