Many businesses reach the same awkward point with AI. Leadership knows the technology deserves attention, employees are already experimenting with tools, vendors are making ambitious promises, and competitors appear to be announcing AI projects every other week. Yet none of that answers the questions that actually matter:
Where should AI be used in ai consulting? Which ideas are realistic? What should be funded first? Is the data good enough? Can existing systems support the idea? Who owns the risk? And how will anyone know whether the investment worked?
Buying AI software is not the same as having an AI strategy.
AI consulting supports AI strategy by helping organizations connect AI opportunities to business goals, assess readiness, identify and prioritize use cases, evaluate data and technology requirements, estimate business value, establish governance, create an implementation roadmap, support adoption, and measure results.
The useful part of AI strategy consulting is therefore not producing a polished document about how important artificial intelligence will become. Most executives already understand that AI matters.
The harder job is turning broad ambition into decisions that can survive contact with budgets, existing software, messy data, security requirements, employees, customers, and day-to-day operations. A good ai business automation strategy should make those decisions clearer and make execution more realistic.
What Is an AI Strategy?
An AI strategy is a practical set of decisions about where artificial intelligence should contribute to the business, what capabilities the organization needs, which initiatives deserve investment, and how those initiatives will be implemented and controlled.
It should connect directly to broader business objectives. If a company wants to reduce customer-service response times, improve sales productivity, process documents faster, strengthen forecasting, or reduce operational errors, the AI strategy should explain whether AI can contribute to those outcomes and, if so, how.
It also needs to address less exciting but equally important questions. What data will the systems require? Which existing applications must be integrated? Who approves AI use cases? How will sensitive information be protected? Who monitors performance? When is human review required? What happens when a model produces an unreliable answer?
Using ChatGPT across a few departments does not automatically create an AI strategy. Neither does installing an AI chatbot or enabling an AI feature inside a CRM.
Those are technology activities.
Strategy is the coordinated decision-making around why AI is being used, where it belongs, what comes first, what the organization needs to make it work, and what evidence will determine whether it should continue.
What Role Does AI Consulting Play in AI Strategy?
AI consulting should provide a bridge between business objectives and the practical realities of implementing AI.
That requires more than knowing which models or platforms are available. Consultants may need to understand business processes, workflows, data, APIs, enterprise applications, security, governance, financial justification, employee behavior, and implementation constraints. A technically impressive recommendation can still be strategically poor if it does not fit how the organization actually operates.
Good consultants also provide an external perspective. Internal teams can become accustomed to inefficient processes or assume certain constraints cannot be changed. Someone examining the organization from outside may ask different questions and challenge assumptions that have gradually become accepted as facts.
But external perspective has limits. Consultants do not automatically understand a company's customers, politics, workflows, history, culture, exceptions, and informal operating practices better than the people working there.
The strongest strategy work combines both sources of knowledge.
Internal employees explain what really happens. Consultants provide structured analysis, technical perspective, comparisons, implementation knowledge, and an independent challenge to assumptions.
Most importantly, good AI strategy consulting starts with the problem. If a consultant begins with a favorite AI product and then searches around the company for somewhere to install it, the strategy is already moving in the wrong direction.
How Does AI Consulting Support AI Strategy?
Aligning AI With Business Goals
“We need an AI strategy” sounds important, but it is not a business objective.
A consultant should help leadership translate that ambition into outcomes that can actually guide decisions. Perhaps the business wants to reduce repetitive administrative work, improve customer response times, increase sales capacity, shorten document-processing cycles, improve forecasting, reduce errors, or help employees find internal knowledge more quickly.
Once the outcome is clear, AI becomes one possible mechanism rather than the objective itself.
This distinction prevents technology-first projects. A company might be excited about an AI agent, for example, but workflow analysis may show that the real problem is an approval process involving unnecessary handoffs. Fixing the process could deliver more value than adding an intelligent agent to automate the inefficiency.
AI strategy consulting should continually bring discussions back to business value. The question is not “Where can we put AI?” It is “Which business problems matter enough to solve, and is AI an appropriate way to solve them?”
Assessing the Organization's AI Readiness
A strategy becomes unrealistic very quickly when it assumes capabilities the organization does not have.
AI readiness assessment examines the conditions required to implement particular use cases. That can include data availability and quality, infrastructure, APIs, system integrations, security controls, employee skills, governance, workflow maturity, management support, and the organization's ability to operate new technology after deployment.
Importantly, readiness is not binary.
A business might be perfectly capable of deploying an internal knowledge assistant using approved documents while being nowhere near ready for an autonomous system that makes operational decisions across several poorly integrated applications.
This is why a generic “AI-ready” score has limited value by itself. Readiness becomes useful when connected to actual initiatives.
Consultants should identify which gaps block which opportunities. If a promising use case depends on reliable customer information but the CRM contains duplicate and incomplete records, data improvement becomes part of the strategy. If an AI agent needs to take actions across systems that have no usable APIs, the integration problem changes the project's feasibility, cost, and priority.
Identifying Practical AI Opportunities
Opportunity discovery usually requires examining how work is actually performed.
Consultants can map workflows, speak with employees, examine bottlenecks, identify repetitive tasks, study document-heavy processes, understand customer interactions, locate knowledge-access problems, and investigate decisions that depend heavily on prediction or pattern recognition.
Different problems may point toward different approaches. Generative AI may help employees work with language and knowledge. Document intelligence may extract and classify information. Predictive models may support forecasting. AI agents may coordinate certain multi-step activities. Retrieval systems may make internal knowledge easier to access.
But technology categories should not drive discovery.
One mistake businesses make is labeling every inefficient process as an AI opportunity. Sometimes the better answer is a workflow rule, an API integration, a database cleanup, conventional automation, improved software configuration, or simply removing an unnecessary step.
AI consulting adds value when it distinguishes between problems that genuinely benefit from AI and problems that merely happen to exist during the AI boom.
Prioritizing AI Use Cases
Once people start looking for AI opportunities, ideas tend to multiply quickly. Sales wants one system, customer service wants another, operations has several automation ideas, and leadership may have its own ambitious project.
Few organizations can responsibly pursue everything at once.
Consultants can help compare use cases across business impact, technical feasibility, data readiness, cost, risk, implementation effort, strategic relevance, and likely time to value.
In practice, this often resembles an impact-versus-feasibility assessment, although the useful work lies in the reasoning behind the assessment rather than the framework itself.
A modest document-processing project might rank above an ambitious autonomous agent because the required data is accessible, the workflow is stable, the outcome is measurable, and implementation risk is manageable.
That does not mean the larger project is permanently rejected. It may mean the organization should build capabilities and evidence first.
Prioritization protects the strategy from becoming a wish list. It forces the organization to decide what deserves scarce money, technical capacity, management attention, and employee time.
Building the Business Case and Estimating AI ROI
Technical possibility is not the same as economic value.
AI consultants can help connect use cases to measurable outcomes such as hours saved, processing costs reduced, errors avoided, response times improved, conversions increased, customer retention strengthened, or employee capacity released for higher-value work.
The cost side matters just as much.
An AI system may require model or API usage, software subscriptions, integration development, cloud infrastructure, monitoring, security work, maintenance, human review, employee training, and ongoing change management. A prototype that costs little to demonstrate can become substantially more complicated when it must operate reliably at production scale.
ROI estimates should therefore expose assumptions rather than disguise uncertainty.
Before implementation, nobody can know every variable precisely. Adoption may be lower than expected. Human review may take longer. API usage may grow. Accuracy may vary across real-world cases.
A useful business case creates a reasonable decision model and identifies what needs to be validated. It does not manufacture certainty.
Sometimes that analysis leads to an important conclusion: an AI project may be entirely possible and still not be worth doing.
Evaluating Data, Systems, and Technology Requirements
Many attractive AI ideas become less simple once someone asks where the required information actually lives.
Customer information might be split between a CRM, ERP, support platform, spreadsheets, databases, email, and legacy applications. Some data may be outdated. Some systems may offer good APIs, while others are difficult to integrate. Authentication, access controls, cloud architecture, privacy requirements, and security policies can add further constraints.
Imagine a company wants an AI customer-service agent capable of answering account-specific questions. On a presentation slide, the idea looks straightforward. In reality, the knowledge base may be outdated, customer records may be inconsistent, the CRM integration may be unreliable, and escalation rules may exist mainly in employees' heads.
The strategic problem is no longer simply choosing a language model.
Technology assessment helps uncover these dependencies early. It may change the architecture, narrow the use case, increase the expected cost, or move the project lower in the priority order.
That is why AI strategy cannot be separated completely from enterprise architecture and data reality.
Supporting Build-Versus-Buy and AI Technology Decisions
Once a use case survives initial evaluation, the organization still has to decide how the capability should be obtained.
Existing business software may already provide adequate AI functionality. A specialist SaaS product may solve the problem quickly. Commercial AI APIs can provide flexible building blocks. Open-source models may offer greater control in certain situations. Custom development may be justified when the workflow, integration requirements, or competitive value are sufficiently distinctive.
There is no universally correct answer.
Buying can reduce development time, but it may introduce vendor dependency, recurring costs, limited customization, or integration constraints. Building can increase control and flexibility, but it also creates development, maintenance, security, monitoring, and skills requirements.
Consultants should evaluate these tradeoffs in the context of the actual use case.
Custom development is not automatically more sophisticated, and buying a product is not automatically cheaper. The strategically sensible choice is the one that provides enough capability and control at an acceptable cost and risk.
Establishing AI Governance, Security, and Risk Controls
Governance should be designed into AI strategy rather than attached after systems are deployed.
Businesses need to consider what information AI systems can access, which employees can use them, what data can be sent to external services, how outputs are reviewed, how failures are handled, and how important decisions can be traced or challenged.
Accuracy, hallucinations, bias, privacy, security, auditability, access control, model behavior, monitoring, escalation, and human oversight may all matter depending on the application.
Governance should also be proportional to risk.
An internal assistant that helps employees rewrite routine text does not necessarily require the same controls as an AI system influencing financial decisions, employment processes, legal work, or customer outcomes.
Over-governance can make harmless experimentation painfully slow. Under-governance can create serious operational and security problems.
AI strategy consulting should help the organization decide where controls are required and how strong those controls need to be.
Defining AI Ownership and the Operating Model
Someone eventually has to own what happens after the workshop ends.
An AI operating model defines how decisions, responsibilities, approvals, implementation, monitoring, and ongoing improvement are distributed across the organization.
Executive leadership may provide sponsorship and investment authority. Business departments may own outcomes. IT and data teams may support architecture and integration. Security, legal, privacy, or compliance functions may review higher-risk applications. Operational teams may monitor performance and manage human escalation.
The exact structure depends on the organization.
Without clear ownership, companies often accumulate disconnected experiments, duplicate subscriptions, inconsistent security practices, unclear approval processes, and pilots that nobody takes responsibility for scaling.
Some larger organizations may benefit from an AI Center of Excellence or another centralized capability. Smaller businesses may need nothing that formal.
The important question is not whether the organization has an impressive organizational structure. It is whether everyone understands who decides, who builds, who approves, who operates, and who is accountable for outcomes.
Creating a Practical AI Implementation Roadmap
Strategy needs sequencing.
A useful AI implementation roadmap connects assessment, prioritization, pilots, technical work, integration, adoption, deployment, scaling, and monitoring. It should make dependencies visible and clarify what must happen before later initiatives can proceed.
For example, a company may want several advanced AI applications, but the roadmap might show that customer-data cleanup and API improvements need to happen first. Another use case might be ready for a limited pilot immediately because it relies on accessible documents and requires minimal integration.
Consultants can help define milestones, responsibilities, budgets, dependencies, teams, success criteria, and decision points.
A roadmap should not pretend every future detail is known. It should provide enough structure to guide action while allowing new evidence to change later decisions.
A presentation containing 40 AI ideas arranged across a colorful timeline is not necessarily a roadmap. A practical roadmap explains what happens first, why it happens first, what resources it requires, and what evidence allows the organization to move forward.
Planning AI Pilots and Proofs of Concept
Some questions cannot be answered reliably through meetings and spreadsheets.
A proof of concept or AI pilot can test assumptions using limited scope, representative data, real users, and measurable success criteria before the organization commits to larger investment.
A pilot might test whether the technology can achieve acceptable accuracy, whether integrations work, whether employees find the system useful, whether human review becomes a bottleneck, or whether operating costs remain reasonable.
The scope matters. A demonstration using carefully selected examples can make almost anything look impressive. A useful pilot should expose the system to enough real-world complexity to reveal meaningful weaknesses.
Failure is not automatically a bad outcome.
If a controlled pilot shows that the data is inadequate, employees dislike the workflow, costs are too high, or accuracy cannot meet the required standard, the organization has learned something valuable before spending substantially more.
Supporting Employee Adoption and Change Management
AI strategy is partly a technology problem, but it is also an operating change.
AI can alter how employees perform tasks, make decisions, review work, access knowledge, interact with customers, and divide responsibilities between people and software.
Employees therefore need more than a login and a training video.
Consultants can help organizations think through AI literacy, workflow redesign, communication, training, employee feedback, internal champions, acceptable-use guidance, and human review responsibilities.
People also need to understand what the system is good at and where it can fail. Otherwise, some employees may distrust useful AI completely while others trust unreliable outputs far too much.
A technically successful AI system can still produce very little commercial value if employees avoid it, misuse it, duplicate its work manually, or develop workarounds because the new workflow does not fit reality.
Adoption is therefore not something to worry about after deployment. It belongs inside the strategy.
Measuring Results and Refining the AI Strategy
A strategy needs evidence.
Consultants can help define business and technical measures that show whether an initiative is producing the intended result. Depending on the use case, these might include operating cost, processing time, employee adoption, reliability, accuracy, customer outcomes, productivity, revenue impact, error rates, or return on investment.
Technical performance alone is insufficient.
A model can achieve impressive accuracy and still create little value if employees rarely use it. An AI assistant can be popular but financially unattractive if its operating costs exceed the value it creates.
Measurement should therefore connect system performance with business outcomes.
This creates an important feedback loop. Strategy leads to implementation. Implementation produces evidence. Evidence confirms or challenges assumptions. The strategy then changes.
AI strategy should mature as the organization learns. Treating the original strategy document as permanently correct defeats much of the purpose of measuring anything.
What Does an AI Strategy Consultant Actually Deliver?
The tangible outputs of AI strategy consulting depend on the organization and the scope of the engagement.
Typical work can result in an AI readiness assessment, workflow analysis, opportunity inventory, prioritized use-case portfolio, business cases, ROI assumptions, data-readiness findings, architecture or technology recommendations, build-versus-buy analysis, governance principles, operating-model decisions, implementation roadmap, pilot plan, and KPI framework.
Those outputs should not be identical for every organization.
A smaller business exploring a handful of opportunities may primarily need clarity about its best use cases, technology choices, major dependencies, expected costs, and a realistic implementation roadmap.
A large enterprise operating across departments and technology environments may need much more formal work around architecture, governance, portfolio management, security, data access, capability development, cross-functional ownership, and investment decisions.
The document itself is not the real product.
A 100-page strategy that nobody can execute has limited value. The useful output is better decision-making: clearer priorities, rejected weak ideas, understood dependencies, realistic investments, defined ownership, appropriate controls, and an agreed path from exploration to implementation.
If the consulting engagement produces impressive slides but leadership still cannot decide what to do first, the strategy work has not gone far enough.
AI Strategy Consulting vs. AI Implementation Consulting
AI strategy consulting focuses primarily on what an organization should do with AI, why it should do it, what should come first, what value is expected, and what constraints or risks need to be addressed.
AI implementation consulting focuses more heavily on how selected solutions are designed, developed, configured, integrated, secured, tested, deployed, monitored, and maintained.
In practice, the boundary is rarely perfect.
A strategy consultant needs enough implementation understanding to recognize when an attractive idea is technically awkward, excessively expensive, difficult to secure, or unrealistic within the organization's existing environment. Likewise, implementation work frequently produces information that changes strategic assumptions.
A pilot may reveal that data quality is worse than expected. Integration may be more difficult. Employees may use the system differently from what designers anticipated. Operating costs may alter the business case. A previously overlooked use case may suddenly become more attractive.
For that reason, AI strategy should not be treated as a document completed before implementation and then locked away.
Strategy guides implementation, but implementation teaches the organization what the strategy got right and what needs to change.
When Does a Business Need AI Strategy Consulting?
External AI strategy consulting can be particularly useful when an organization wants to adopt AI but lacks a clear starting point.
It can also help when a company has generated too many competing ideas, several departments are running disconnected experiments, pilots are failing to reach production, leadership cannot determine expected ROI, or uncertainty around data, integration, security, governance, and technology choices is slowing decisions.
Organizations with limited internal AI experience may benefit from outside expertise simply because they have not yet developed the technical and strategic judgment needed to evaluate claims from vendors or internal enthusiasts.
An independent perspective can also be useful when departments disagree about priorities.
But consulting is not mandatory.
A business with strong internal capabilities across business strategy, AI, data, software architecture, cybersecurity, governance, implementation, finance, and change management may be perfectly capable of developing its own AI strategy.
The question is whether external expertise improves the quality or speed of decisions enough to justify the cost.
Consultants should fill genuine capability or perspective gaps, not become ceremonial participants in decisions the organization is already equipped to make.
What AI Consulting Cannot Fix on Its Own
Consultants cannot make organizational reality disappear.
They cannot magically turn inaccessible or unreliable data into production-ready information. They cannot create executive ownership if leadership refuses to make decisions. They cannot force employees to participate honestly in workflow redesign, eliminate organizational resistance through a presentation, or make an economically weak idea profitable.
They can identify these problems, explain their consequences, recommend changes, and help structure the work required to address them. The organization still has to act.
This is particularly important when underlying processes are poor.
Adding AI to a confusing workflow can simply automate confusion faster. A business may first need to simplify the process, integrate existing systems, improve data management, clarify responsibilities, or use traditional workflow automation.
Sometimes the most useful recommendation produced by an AI strategy exercise is not to use AI for a particular problem.
That should not be considered a failure.
Avoiding an unnecessarily expensive AI project can be every bit as valuable as identifying a strong one. A strategy should optimize business outcomes, not maximize the number of places where the letters “AI” appear.
How to Choose an AI Consulting Partner for Strategy
A useful AI strategy consultant needs to understand more than AI models.
Look at whether the consultant can understand business processes, investigate workflows, reason about data, evaluate architecture and integrations, discuss security and governance sensibly, build credible business cases, and connect technical decisions with operational consequences.
Implementation awareness matters even when the consultant will not personally build every solution. Someone recommending an AI agent should understand what happens when that agent needs authentication, APIs, permissions, reliable source data, monitoring, human escalation, and production support.
Vendor neutrality is also valuable. If every discovery conversation mysteriously ends with the same platform being recommended, strategy may be serving the technology rather than the business.
Communication and knowledge transfer matter too. Internal teams should understand why priorities were chosen, what assumptions were made, what risks exist, and how future decisions should be evaluated.
One of the simplest tests is also one of the most revealing: a strong consultant should be willing to tell a business when AI is not the best solution.
Consultants who see every business problem as an AI opportunity can create unnecessary technology, cost, maintenance, and risk. Good strategic advice sometimes means recommending conventional automation, process redesign, better data, or doing nothing until a stronger business case exists.
How Do You Know Whether Your AI Strategy Is Working?
Count business outcomes, not AI tools.
A useful strategy should gradually produce evidence that priority initiatives can move into production, employees actually use valuable systems, business KPIs improve, costs remain justified, risks stay within acceptable boundaries, and successful solutions can scale without creating disproportionate operational complexity.
The organization should also become better at AI itself.
Over time, teams should improve their ability to evaluate opportunities, understand limitations, manage data, govern systems, run pilots, measure results, and stop weak projects before they consume excessive resources.
Not every early initiative needs to succeed.
A pilot that disproves an assumption cheaply can be healthier than a weak project that survives because nobody wants to admit it is failing.
The real test is whether the organization learns. A working strategy helps it scale what succeeds, stop what does not, revise assumptions when evidence changes, and become more disciplined about deciding where AI genuinely belongs.
Conclusion
Most organizations do not need another presentation explaining that artificial intelligence is important. They need clarity about where AI creates meaningful value, which opportunities deserve investment, what should be ignored, whether their data and systems can support the proposed applications, what risks require controls, and how promising ideas will become part of daily operations. Those questions cross business functions. AI strategy is not simply an IT strategy because its consequences can reach operations, sales, marketing, finance, customer service, HR, legal, security, product development, knowledge management, and management decision-making.
The strongest contribution of AI consulting is disciplined decision-making. Good consultants help organizations challenge assumptions before expensive commitments are made. They connect AI initiatives with business objectives, examine readiness, expose data and integration problems, compare competing use cases, test financial assumptions, evaluate technology choices, design appropriate governance, clarify ownership, and create an implementation sequence that people can actually follow. External expertise does not replace internal knowledge. Employees and leaders understand company-specific customers, processes, culture, constraints, and priorities that an outsider cannot learn instantly. Effective strategy combines that internal context with external technical and implementation perspective.
Ultimately, the goal of a good AI strategy is not to maximize how much AI a company uses. It is to become better at deciding where AI deserves to exist. Sometimes that means investing aggressively in a high-value opportunity. Sometimes it means running a controlled pilot before making a larger commitment. And sometimes it means choosing conventional automation, improving data, redesigning a process, or leaving a problem alone. The strategy is working when AI improves the business in measurable ways without introducing unnecessary cost, complexity, or risk.
FAQs
What is the role of an AI consultant in AI strategy?
An AI consultant helps translate broad AI ambition into concrete strategic decisions. That normally involves understanding business objectives and workflows, assessing AI readiness, identifying potential use cases, comparing priorities, examining data and technology requirements, developing business cases, considering governance requirements, planning implementation, and defining how results will be measured.
The consultant should not simply arrive with a collection of AI tools to recommend. The more valuable role is helping the organization determine where AI genuinely makes sense, which opportunities should come first, which technical or organizational problems need to be fixed before implementation, and what evidence would demonstrate success. Good consultants also challenge assumptions, including the assumption that AI is necessarily the right answer to a particular problem.
Does a business need an AI consultant to create an AI strategy?
No. A business does not automatically need an external consultant to create a useful AI strategy. Organizations with strong internal capabilities across business strategy, AI, data, software architecture, cybersecurity, governance, implementation, finance, and change management may have the knowledge required to do the work themselves. Internal teams can also possess deeper knowledge of company-specific processes and constraints than any external consultant.
External expertise becomes more useful when those capabilities are missing or fragmented. A consultant may help when the organization has too many competing AI ideas, uncertain ROI, complicated legacy systems, limited AI implementation experience, governance concerns, stalled pilots, unclear priorities, or disagreement between departments. An independent perspective can also help leadership challenge assumptions and evaluate technology choices without becoming overly influenced by a particular vendor or internal preference.
What does an AI strategy consulting engagement include?
The scope depends on the organization's maturity, goals, size, systems, risk environment, and existing AI activity. A typical engagement may involve discussions with stakeholders, workflow and process analysis, AI readiness assessment, opportunity discovery, use-case prioritization, data and architecture review, financial analysis, governance planning, technology evaluation, roadmap development, pilot planning, and definition of business and technical KPIs.
A good engagement should not force every company through exactly the same template. An organization that has already identified and validated its priority use cases may need deeper architecture and governance work rather than another opportunity-discovery exercise. Another company may have strong infrastructure but little idea where AI could create business value. The consulting scope should address the decisions the organization genuinely needs to make rather than generating documents simply because they normally appear in an AI strategy package.
How long does it take to develop an AI strategy?
There is no universal timeframe because the amount of work depends heavily on the organization. A focused strategy for a smaller business with a limited number of processes and systems is fundamentally different from developing an enterprise-wide strategy involving multiple departments, complex data environments, legacy applications, security requirements, existing AI initiatives, and numerous stakeholders.
There is also a balance to strike. Moving too quickly can produce recommendations based on superficial understanding of workflows, data, integration constraints, and employee needs. Spending too long analyzing can create a different problem, where the organization keeps studying AI while technology, business requirements, and assumptions continue to change. The goal should not be to design a theoretically perfect strategy before taking action. It should be to gather enough evidence to make responsible decisions, test important assumptions through controlled implementation, and refine the strategy as real evidence becomes available.
What is the difference between AI strategy consulting and AI implementation consulting?
AI strategy consulting focuses primarily on deciding what the organization should do with AI, why those initiatives matter, which projects should come first, what business value is expected, and what dependencies, capabilities, costs, and risks need to be addressed. It establishes direction and helps the organization make investment and prioritization decisions.
AI implementation consulting focuses more heavily on turning selected initiatives into working systems. That can involve solution design, software development, configuration, data pipelines, APIs, system integration, testing, security, deployment, monitoring, user workflows, and operational support. The boundary is not always clean. Implementation regularly exposes data problems, technical constraints, unexpected costs, user behavior, or new opportunities that require strategic decisions to be reconsidered. Strong organizations therefore treat strategy and implementation as a feedback loop rather than two completely separate phases.
