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Timo Vitikainen retweeted
Before an AI agent negotiates for your business, decide which tactics it may use.
Anthropic's Project Swap found agents using time pressure, appeals to duty, product positioning and trade coordination in a controlled employee book exchange. Its examples make an abstract delegation question concrete.
A discount obtained through pressure can still conflict with how a company wants to treat a long-term partner. An agent may also become helpful to another participant in ways its own principal never authorised.
The experiment does not establish how agents would behave in enterprise procurement. It does expose a useful design question: what behaviour counts as acceptable representation?
A commercial mandate should specify permitted disclosures, concession limits, commitments requiring approval and when the agent must stop. Evaluation then needs to examine the conversation as well as the final deal.
A good purchase price cannot, by itself, tell you whether the agent represented the business appropriately.
Source: Anthropic, “Project Swap: What happens when agents trade for us?”
@RLDI_Lamy @sulefati7 @ILoveBooks786 @Corix_JC @FrRonconii @COSTESLionelEr @Nicochan33 @faryus88 @ramonvidall @ozsilverfox @jameslhbartlett @dinisguarda @ricardo_ik_ahau @timo_vi @violetainsights @DioOmega @TravlTechDog @66Iot @anton_chuvakin @OperatingByJohn @quepasachico @PatrickCFOD
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Timo Vitikainen retweeted
Creativity doesn't always need more inspiration. Sometimes, it just needs more space.
I've learned that the best ideas don't always come when I'm sitting at my desk.
That's why I've built a few simple tech rituals into my creative routine.
Follow @LindaGrass0 for more insights 💡
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Timo Vitikainen retweeted
An expansion forecast can look profitable because it carries the home market's cost assumptions into a different country.
A local partner may bring introductions while still requiring training, marketing investment and support. Customers may need adaptations that change delivery costs. A longer sales cycle can delay the point at which the operation supports itself.
Before increasing the budget, test the route from first customer contact to a delivered, supported sale.
Can the channel reach your target buyers? Will those buyers commit at a viable price? Can your team fulfil the promise with the people and systems available?
Use the pilot to establish the cost to serve and the evidence needed for the next investment decision. Agree the review date and spending limit before the test starts.
#InternationalBusiness #MarketExpansion
Sources: International Trade Administration, “Develop an Export Plan”; “Conducting Market Research”; “Pricing Strategy”.
@RLDI_Lamy @sulefati7 @ILoveBooks786 @Corix_JC @FrRonconii @COSTESLionelEr @Nicochan33 @faryus88 @ramonvidall @ozsilverfox @jameslhbartlett @dinisguarda @ricardo_ik_ahau @timo_vi @violetainsights @DioOmega @TravlTechDog @66Iot @OperatingByJohn @BCAgroup @Eli_Krumova @PatrickCFOD
ALT Infographic by Syed Ijlal Hussain: Before you enter a new market. Six checks for an expansion plan you can execute: 1. Buyer demand: a specific segment, buying trigger and buyer feedback; country growth alone is insufficient. 2. Customer access: a tested channel and sales ownership; a partner's contacts are not a sales plan. 3. Reason to switch: feedback on differentiation and switching barriers; home-market success alone is insufficient. 4. Local economics: include acquisition, delivery, support and partner costs instead of reusing domestic margins. 5. Delivery readiness: check capacity, service coverage and local requirements. 6. Commitment limits: agree a budget, accountable owner and review criteria. Test one customer segment through one route to market in a bounded pilot. Review buyer commitment, cost to serve and delivery reliability. Planning references: International Trade Administration, Trade.gov.
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Timo Vitikainen retweeted
Interest in science and tech is far from uniform, even across major economies. For companies, this matters: you cannot assume the same curiosity toward technology in every market or workforce.
Digital change starts from different points.
Source @StatistaCharts via @antgrasso
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Timo Vitikainen retweeted
A pricing win can disappear in the promotional calendar.
L.E.K.'s CPG framework connects revenue-growth levers rather than assessing each in isolation.
If a list-price increase is followed by deeper promotional support, separate teams may hit their targets while the combined economics barely improve.
Review the scenario across price, promotion, mix and trade spend. AI can make that comparison faster; it cannot choose the commercial trade-off on management's behalf.
@RLDI_Lamy @sulefati7 @ILoveBooks786 @Corix_JC @FrRonconii @COSTESLionelEr @Nicochan33 @faryus88 @ramonvidall @ozsilverfox @jameslhbartlett @dinisguarda @ricardo_ik_ahau @timo_vi @violetainsights @DioOmega @TravlTechDog @66Iot @PatrickCFOD @OperatingByJohn @TysonLester @BCAgroup
Source: L.E.K. Consulting, “RGM Needs More Influence, Not Just Better Analytics”
ALT L.E.K.'s six enablers of coordinated revenue growth management include global standards with local flexibility, analytics hubs, integrated platforms, workflow tools, scenario modelling and cross-lever optimisation.
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Timo Vitikainen retweeted
Expanding sales coverage in emerging markets has traditionally meant hiring more people, opening new territories and adding distribution partners.
AI is introducing another growth lever: improving the commercial value of every sales visit.
BCG reports that a multinational consumer goods company using an AI sales companion achieved 25% more customer-facing time and 8% more product lines sold per sales call.
The system supported route optimization, personalized recommendations and sales execution.
For commercial leaders, the opportunity extends beyond salesforce productivity.
Better territory decisions and store-level recommendations could make existing distribution networks more productive before companies commit to costly geographic expansion.
That changes how market growth should be evaluated. The next investment may be in better sales intelligence rather than additional sales capacity.
@RLDI_Lamy @sulefati7 @ILoveBooks786 @Corix_JC @FrRonconii @COSTESLionelEr @Nicochan33 @faryus88 @ramonvidall @ozsilverfox @jameslhbartlett @dinisguarda @ricardo_ik_ahau @timo_vi @violetainsights @DioOmega @TravlTechDog @66Iot @OperatingByJohn @BCAgroup @Eli_Krumova @PatrickCFOD
Source: BCG, “How AI Can Reshape Distributed Sales Channels in Emerging Markets”
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Timo Vitikainen retweeted
Bessemer's 2025 AI-startup comparison pairs very different growth trajectories with roughly 25% and 60% gross margins.
The study covered 20 selected high-growth companies. These are neither market-wide averages nor targets every founder should adopt.
That is precisely why the comparison matters: a revenue trajectory comes with a business model and an economic profile.
Before copying another company's target, test what must be true about acquisition, customer retention and delivery costs. A growth benchmark is useful when it exposes those assumptions, rather than replacing them.
@RLDI_Lamy @sulefati7 @ILoveBooks786 @Corix_JC @FrRonconii @COSTESLionelEr @Nicochan33 @faryus88 @ramonvidall @ozsilverfox @jameslhbartlett @dinisguarda @ricardo_ik_ahau @timo_vi @violetainsights @DioOmega @TravlTechDog @66Iot @OperatingByJohn @BCAgroup @Eli_Krumova @PatrickCFOD
Source: Bessemer Venture Partners, “The State of AI 2025”
ALT Bessemer's 2025 selected-startup comparison shows different revenue trajectories and gross margins for AI Supernovas and Shooting Stars.
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Timo Vitikainen retweeted
An HR chatbot can resolve an employee query faster. It cannot decide whether the HR function is organised around the right work.
Gartner's AI-era operating model separates digital HR delivery, strategic talent leadership and HR product design.
The distinction matters because routine service and consequential workforce decisions need different capabilities.
A digital service may handle a policy query. A business facing a difficult capability or workforce choice still needs someone who understands its commercial priorities, people and organisational constraints.
Reducing administrative work should create a reason to reconsider where expertise belongs and how it reaches the business.
Faster answers alone do not establish that HR can make better talent decisions. Leadership still has to design that capability.
@RLDI_Lamy @sulefati7 @ILoveBooks786 @Corix_JC @FrRonconii @COSTESLionelEr @Nicochan33 @faryus88 @ramonvidall @ozsilverfox @jameslhbartlett @dinisguarda @ricardo_ik_ahau @timo_vi @violetainsights @DioOmega @TravlTechDog @66Iot @TysonLester @TalentedLearn @JohnLeh @Eli_Krumova
Source: Gartner, “Build An HR Operating Model That Succeeds In The AI Era”
ALT Gartner diagram of an AI-infused HR operating model, combining an HR innovation command centre with talent leadership, product design and digital HR delivery.
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Timo Vitikainen retweeted
An AI agent can initiate blockchain transactions and perform DAO operations, provided it has an authorized wallet and the required permissions.
An agent can go beyond recommending an action and carry it out. People still decide the limits of that authority.
Microblog @antgrasso
Working with a blockchain is different from updating a conventional database. An application with database access can change records, but it cannot simply change a blockchain's shared state. Transactions must meet the rules checked by the network.
The AI agent itself usually runs off-chain.
It analyzes information and decides which action to request, while the blockchain validates submitted transactions and executes any relevant smart-contract code. The AI model does not perform its reasoning on the blockchain.
A decentralized autonomous organization (DAO) allows a group to coordinate decisions and manage shared resources through blockchain-based governance.
Despite its name, a DAO does not need AI to operate.
But it can delegate specific rights or tasks to an AI agent.
An agent could therefore cast a vote or initiate a treasury payment if the DAO's rules permit it. This does not mean giving the agent unlimited authority.
The organization can define which actions are allowed and which still require approval from its members. Smart contracts can enforce programmed limits, although they cannot determine whether the agent's judgment is sound.
The agent can act autonomously within its assigned role, without taking control of the organization's governance.
#AIAgents #Blockchain #DAO
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Timo Vitikainen retweeted
A profitable-looking growth contract can require cash the business has not planned to provide.
L.E.K.'s matrix separates working-capital impact from the finance team's control. Customer terms, service levels and inventory commitments are part of that picture.
Extra stock may be needed before delivery; payment may arrive much later. A stronger collections process cannot eliminate the funding gap built into those terms.
Evaluate it while shaping the offer. Price, billing milestones and service commitments belong in the same commercial discussion.
@RLDI_Lamy @sulefati7 @ILoveBooks786 @Corix_JC @FrRonconii @COSTESLionelEr @Nicochan33 @faryus88 @ramonvidall @ozsilverfox @jameslhbartlett @dinisguarda @ricardo_ik_ahau @timo_vi @violetainsights @DioOmega @TravlTechDog @66Iot @anton_chuvakin @Transform_Sec @quepasachico @PatrickCFOD
Source: L.E.K. Consulting, “Working Capital Reimagined: How CFOs Unlock Value Beyond Core Finance Levers”
ALT L.E.K.'s matrix maps working-capital levers by financial impact and the finance function's control, including customer terms, service levels, inventory and billing.
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Timo Vitikainen retweeted
Before commissioning bespoke AI, check whether the business has made full use of what already works.
The OECD's SME taxonomy separates breadth of AI use from technical complexity. Extending a proven application into another useful workflow and building a customised system are different investment decisions.
The first may depend on coordination, training and process adoption. The second also requires a case for technical development, maintenance and ongoing support.
For an SME, the starting point should be the commercial constraint. Is useful work failing to spread, or does the business need a capability that existing tools cannot provide?
The underlying survey is non-representative. Use the framework to structure the decision, rather than rank all SMEs or assume that greater complexity means better business performance.
@RLDI_Lamy @sulefati7 @ILoveBooks786 @Corix_JC @FrRonconii @COSTESLionelEr @Nicochan33 @faryus88 @ramonvidall @ozsilverfox @jameslhbartlett @dinisguarda @ricardo_ik_ahau @timo_vi @violetainsights @DioOmega @TravlTechDog @66Iot @PatrickCFOD @OperatingByJohn @TysonLester @BCAgroup
Source: OECD, “Empowering SMEs in the age of AI: The 2026 OECD D4SME Survey”
ALT OECD chart comparing SME AI adoption breadth and technical complexity, with categories ranging from novices to champions. The chart is descriptive of its survey sample.
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Timo Vitikainen retweeted
AI doesn’t learn facts the way humans do. It learns statistical patterns from large amounts of data and uses context to generate the most plausible response.
That means output quality depends heavily on what sits underneath it.
Follow @LindaGrass0 for more insights 💡
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Timo Vitikainen retweeted
AI can warn a maintenance team that a machine may fail, but McKinsey argues that the opportunity lies in what the organization does next.
A prediction is useful when it changes how maintenance is planned and carried out.
That requires more than a model.
Microblog by @antgrasso
Suppose a warning arrives days before a possible breakdown. The repair still waits because the schedule has not changed or the required part is unavailable. The model has supplied information that the process cannot yet use.
Who can act on that warning while there is still time?
McKinsey estimates potential cost reductions and additional uptime across asset-heavy industries. Its analysis emphasizes redesigning the maintenance process so that insights can influence decisions and lead to timely interventions.
I would put maintenance teams at the center of that redesign, giving them access to information while they are planning work and enough authority to adjust priorities.
#IndustrialAI #Maintenance
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Timo Vitikainen retweeted
Keeping up with the market means knowing which AI platforms work best for different tasks and training our teams to use them effectively.
Source @VisualCap via @antgrasso
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Timo Vitikainen retweeted
Who gets the next purchase when an AI assistant helps complete the first one?
OC&C's marketplace analysis maps different ways of integrating with AI, from surfacing information to enabling transactions.
For a marketplace operator, the decision reaches beyond acquiring another source of traffic.
An assistant may make discovery easier while changing who sees customer intent, handles payment and resolves problems. Those choices influence whether the marketplace can serve the customer again.
Before integrating, establish what customer information returns, who owns exceptions and how repeat demand will be supported.
A simple booking and a complex service purchase need not delegate the same responsibilities. The technical design should reflect the commercial relationship the business wants to build.
Source: OC&C Strategy Consultants, “Marketplaces in the Age of AI: What Generative Search Means for Marketplaces”
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ALT OC&C's January 2026 comparison of marketplace integration models, from surfacing listings and browser interaction to native tools and agent-mediated transactions. Capability varies by implementation.
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Timo Vitikainen retweeted
Who can stop an AI-generated customer commitment before it is sent?
BCG's CEO Data Point tracks growing discussion of human oversight in earnings calls. That measures executive attention, not whether a company's controls work.
An assistant drafting an offer and an agent committing the business to a discount need different approval rules.
Define which commitments require review before execution, who can authorise them and what information that person needs.
The capacity to intervene matters too. Requiring approval without allocating time can delay customer responses and encourage workarounds.
For commercial leaders, oversight becomes useful when it can prevent an inappropriate promise before that promise becomes the customer's expectation.
Source: BCG, “The CEO Data Point”
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ALT BCG CEO Data Point chart showing increased earnings-call discussion of human-in-the-loop AI across healthcare, financial institutions, insurance and technology sectors.
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Timo Vitikainen retweeted
AI reduced one company's task from ten days to one. Its customers still waited ten days.
BCG describes how the surrounding business process remained unchanged. The improvement reached customers only after the company addressed the wider workflow.
For a commercial AI investment, that changes the boundary of the project.
If the promise is faster quotations, drafting the proposal is only one step. Pricing approval, specialist input and the handoff to the customer may still determine response time.
Before scaling, examine what improved beyond the task: did the customer receive an answer sooner, could the team handle more demand, or did the cost of serving the opportunity fall?
Faster execution creates value when the business can turn it into a better customer or financial outcome.
Source: BCG, “Look Past Productivity to Get Real Value from AI”
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ALT BCG graphic contrasting optimism about AI returns with the small share of companies reporting meaningful value. It argues for redesigning costs and processes around AI.
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Timo Vitikainen retweeted
A useful test of AI fluency: can the team explain why it rejected an AI recommendation?
McKinsey defines fluency around effective use, verification and the ability to challenge outputs.
For a market-entry decision, AI can assemble market estimates, competitor lists and buyer profiles. The commercial team still needs to check whether the sources describe the right customer segment and whether proposed partners can actually reach it.
A polished brief can conceal those uncertainties.
Develop the capability through real decisions: what was delegated, what was verified and what remained unresolved when resources were committed?
The value of AI fluency should be visible in the quality of the decision, as well as the speed of producing its supporting analysis.
Source: McKinsey & Company, “AI fluency: The next foundation of US economic competitiveness”
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ALT McKinsey chart showing a 13.6-fold increase over three years in US employment in occupations demanding AI fluency, split between STEM and non-STEM roles.
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Timo Vitikainen retweeted
Who decides whether a Bitcoin payment is valid? Not a bank.
Independent nodes check it against shared rules, while miners add blocks.
That allows BTC to move without a central operator, but protecting your private keys remains your responsibility.
Microblog by @antgrasso
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