The IBM CEO on Transforming Your Operating Model for AI: A Practical Guide for Japanese Companies in the Philippines

The IBM CEO on "Transforming Your Operating Model for the AI Era" — A Practical Guide for Philippine Sites

This guide explains, with a hands-on focus for Japanese companies at Philippine sites, the "transformation of the operating model for the AI era" advocated by IBM CEO Arvind Krishna. You'll learn a phased approach to adoption and the notes specific to the local context.

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Part 1: Why This Matters

Step 1: The Philippine Business Context (3 min)

IBM CEO Arvind Krishna said that to get the greatest returns from AI investment, simply adopting the technology isn't enough — you need to fundamentally rebuild the way work itself gets done. This idea is especially important for Japanese companies with operations in the Philippines. In the Philippines, much work — call centers, accounting shared services, IT help desks, and more — has been built on "the quantity of manpower." Just placing AI on top of existing work as is won't raise productivity as much as you'd hoped.

For a Japanese company to maintain its competitiveness in the Philippines, it must review how employees move and how departments connect to one another, rather than merely signing up for an AI tool. At Manila and Cebu sites, because local staff, Japanese expatriates, and the head office are all involved, rebuilding the work is harder than at a Japanese head office — but if it succeeds, it leads to differentiation.

At the Manila office, Tanaka, the IT lead, speaks to Maria, the local manager. "The IBM CEO was saying that to get results from AI, you need to rebuild the way work gets done. For our team too, before we bring in AI, shall we first write out who's doing what?" Maria replies, "I agree. If we just put AI on top of the way we work now, it'll end up just doing the same work a little faster."

Step 2: Key Points from the Source Article (5 min)

Here are the facts from the source article, organized so that people working at a Philippine site can grasp them in a short time.

Source — Wall Street Journal "IBM CEO Says AI Triggers Need for New Operating Models" (May 5, 2026)

This table was created for learning purposes based on facts in publicly available information. For details, please check the source article at the link above.

Related: see How AI Helps Philippine SMEs Build a Practical Adoption Roadmap.

Step 3: Comprehension Check (5 min)

Q1. What is cited in the source article as the key to maximizing the returns on AI investment?

Hint: Recall whether the weight is placed on "adopting the technology" or on "the way work gets done."

Q2. State, in four stages, the order in which AI use spreads within a company.

Hint: It starts from the smallest unit and finally reaches the whole organization.

Q3. State the title and the company of the speaker, Arvind Krishna.

Hint: It's a long-established U.S. IT company, often called by a three-letter acronym.

Q4. What in-house work area did Krishna cite as an example of an operating model that spreads AI at a company-wide scale?

Hint: It's the department in charge of hiring, staffing, and the like.

Q5. If you sum up the source article's argument in one phrase, "the returns from AI are decided by the renewal of what?"

Hint: The answer includes the long-standing "way work gets done" and "the way people connect with one another."

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Related: see How AI Strategy Helps Philippine SMEs Avoid Costly Adoption Failures.

Part 2: Putting It into Practice

Step 4: Steps for Adoption in the Philippines (10 min)

Here are the steps for advancing AI use together with a rebuild of the operating model at a Philippine site.

Step 5: Common Mistakes and How to Avoid Them (5 min)

Failure pattern 1: "Being satisfied just by signing up for an AI tool"

Bad example: Management considers "adoption complete" the moment they sign the AI contract, and puts off briefing local staff and reviewing the work. As a result, the tool goes almost unused while the monthly cost keeps accruing.

Good example: At the same time as the contract, work with the Manila IT lead to create a model way of using it that fits the local work. In team meetings, explain while showing concrete examples, and always set aside time for questions at the end.

Failure pattern 2: "Stalling at individual trials and not spreading to the team"

Bad example: Only some outstanding staff master the AI, and their results aren't shared with other members. Individual productivity rises, but it never connects to results for the whole organization.

Good example: Once a month, hold a short meeting to share how AI is used. Have members bring both examples that worked and examples that didn't, so the whole team can use the same model.

Failure pattern 3: "Bringing the Japanese head office's way of doing things over as is"

Bad example: You hand the AI-use approach made in Japan to the Manila team without checking the local work flow. It doesn't fit work premised on Philippine business customs and verbal agreement, and the team is thrown into confusion.

Good example: Work with the local manager to design a way of using it that fits the Philippine work from scratch. Keep the Japanese head office's way as a reference only, and rebuild it into a form suited to local culture and law.

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Part 3: Going Deeper

Step 6: Related Technical Terms (5 min)

The operating model (the framework for how work gets done) is the company-wide mechanism that defines who does what, in what order, and how. At a Philippine site, rather than using the Japanese head office's framework as is, rebuilding it to fit local business customs greatly changes the effect of AI adoption.

A workflow (the flow of work) represents the order of tasks from when one job starts to when it ends. At a Manila call center, writing out the flow from receiving a customer's call to resolving the problem makes it easier to see the parts that can be automated with AI.

Cross-functional (collaboration across departments) refers to members from different departments — sales, accounting, IT, and so on — forming a single team to advance work. At a Philippine site, creating a forum where Japanese expatriates and local staff gather across departments broadens the scope of AI use.

Enterprise scale (rollout at the company-wide scale) means spreading an initiative across the entire company and operating it. When you spread a way of using AI that succeeded at one Manila site to your Cebu or Davao sites, adjustments suited to each location's circumstances become necessary.

Human resources (HR work) refers to all work involving people, such as hiring, training, and evaluation. It was cited in the source article as the area where IBM rebuilt the way work gets done, and at Philippine sites too, a move to review how local staff are developed using AI is expected to spread going forward.

Step 7: Thinking About How to Apply This at Your Company (10 min)

Write out your work flow and look for places AI can change

Prompt to consider: In your everyday work, isn't there repetitive work that someone does by hand? Write out five tasks you repeat the same way every week — creating reports, replying to customers, transcribing data, and so on.

Next action: Schedule a 30-minute meeting with your local team leader next Monday and create the list of repetitive tasks together.

Confirm whether AI use has stalled at the "individual" level

Prompt to consider: At your Philippine site, who has mastered AI? Is that person's knowledge shared with other members? Check whether you're relying entirely on one outstanding staff member.

Next action: This month, plan a short internal showcase to share how AI is used, and have three staff members present their cases.

Check whether you're bringing the Japanese head office's approach over as is

Prompt to consider: Are the manuals and work flows you operate locally just direct translations of the Japanese head office's? Or have they been rebuilt to fit Philippine law and business customs?

Next action: Pick three of your main work manuals and create an opportunity for the local manager to check "whether there are parts that don't fit the Japanese way."

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Part 4: FAQ

Q1. How much budget should we anticipate when starting AI adoption at a Philippine site?

A1. If it's the stage of individual trials, you can start from a generative AI service of about 1,000–1,500 pesos per person a month. Even when spreading it to a 10-person team, you can experiment from around 15,000 pesos a month. Building it into a full-fledged business system can range from hundreds of thousands to millions of pesos depending on scale, so we recommend first testing small, seeing the results, and then deciding.

Q2. How can we get local staff to accept changes in how work gets done?

A2. In the Philippines, a culture of "saving face" is valued, and unilaterally announcing sudden changes can invite pushback. It's effective to set up a forum to carefully explain the reasons for the change, and to consult the local manager first before communicating it to everyone. Because explaining only "because the Japanese head office decided so" makes it hard for the ground to move, show the benefits to the local site concretely.

Q3. I'm worried about letting the AI learn from our company data. How should we deal with this?

A3. Many enterprise AI services have a setting that prevents the data you input from being used for training. Before contracting, always confirm whether that setting is enabled by default. In the Philippines, there's the data-protection law of the NPC (National Privacy Commission), and care is needed in how customer information is handled when sending it to a third party's AI. Document your in-house operating rules while consulting your legal staff.

Q4. Is it OK to set differences between the Japanese head office and the Philippine site in how AI is used?

A4. You should set differences, in fact. In Japan, agreement in writing is central, whereas in the Philippines there are many situations that value verbal agreement and the human relationship with one's superior. Even when using the same AI tool, rebuilding the operating rules and manuals to fit local business customs helps it take root. The important stance is to reference the head office's way while deciding the final form locally.

Q5. How long does rebuilding the operating model take?

A5. As a rough guide, individual and small-team trials take 1–3 months, cross-department rollout about six months, and a company-wide operating-model change 1–2 years. For a Philippine site, because developing local staff and coordinating with the head office take time, estimating a bit longer than within Japan is reassuring. Taking the stages without rushing is, in the end, the shortcut to success.

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Tips for Getting the Most Out of It (3 Tips)

First, try writing out your current work on a single sheet of paper. Before thinking about adopting AI, the starting point is to put into visible form who is doing which task. Once you write it out, you'll start to see work that can be automated and work that wasn't needed in the first place. At a Philippine site, where a lot of work runs on verbal agreement, this exercise is especially valuable.

Create a forum, once a month, to share with the team the ways of using it that have stalled at the "individual" level. The knowledge of a person who has mastered AI doesn't become the organization's strength unless it's shared. Just setting up a casual forum once a month — a short showcase or a lunchtime study session — widens the circle of use. Because the Philippines has a culture that enjoys talking, these sharing forums liven up naturally.

Set aside time to review the Japanese head office's approach together with the local manager. Importing the head office's procedures as is won't work locally. Once a quarter, check the main manuals together with the local manager and revise them to fit Philippine business customs and law. Continuing this becomes the foundation for rebuilding the operating model.

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Bonus: How to Work with PH AI Works

PH AI Works supports Japanese companies that want to use AI and technology in the Philippines. On the theme of this material — "AI adoption accompanied by a rebuild of the operating model" — specialists familiar with local business customs and law provide support tailored to the circumstances of Japanese companies.

The topics you can consult us on as a next step are as follows.

• Making the work flow at your Philippine site visible, and identifying places that can be made more efficient with AI
• Designing AI-use training for local staff, and coordinating operations with the Japanese head office
• Support for establishing AI operating rules with attention to the data-protection law of the NPC (National Privacy Commission)

The consultation is free. Please feel free to get in touch.

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Citations and References

• Wall Street Journal — "IBM CEO Says AI Triggers Need for New Operating Models" (2026-05-05)

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References and Sources

• Wall Street Journal — "IBM CEO Says AI Triggers Need for New Operating Models" (2026-05-05)
• IBM
• National Privacy Commission (NPC), Philippines