AI assistants change who makes the first call, so adoption now depends on trust. Learn how human approval, explainability and the override rate turn AI rollouts into managed change, with adoption plans for pricing, sales operations and IT leaders.
Updated: October 14, 2026
Originally published: May 6, 2024
Most pricing organizations expect to use AI within three years. Few have made it stick. In McKinsey’s November 2025 survey of 419 pricing executives, only 5–10% had fully scaled agentic AI across any use case, and nearly half of the companies trying to scale it named change management and accuracy as top obstacles. The technology is rarely what stalls. Trust is.
This guide was first published in May 2024, before AI assistants reached pricing, quoting and rebates. The fundamentals of change management still hold. What has changed is what you are asking people to adopt: not only a new system, but recommendations they did not write, from a tool they have not yet learned to question.
This updated article explores how structured planning and execution maximize adoption, and adds what AI-assisted tools demand on top of it: human approval, explainability, and adoption plans built by role.
It is written for pricing, sales operations and IT leaders who are rolling out AI-assisted tools, or are about to.
What is change management?
Change management is the structured approach organizations use to plan, implement, and support transitions within their business. It involves preparing employees for what’s ahead, equipping them with the tools and knowledge to adapt, and ensuring new processes, systems, or strategies actually take hold. This discipline addresses the human side of organizational shifts, from technology rollouts and restructuring to process improvements and cultural transformations.
Organizations constantly face pressure to evolve. Markets shift, competitors move, and technology advances faster than most teams can keep up. Without a deliberate plan for managing change, even the best strategic initiatives fall flat. People revert to old habits, new systems sit unused, and investments don’t deliver the expected returns.
Effective change management goes beyond announcing what’s different. It requires understanding how changes impact daily work, communicating the reasoning behind decisions, and equipping teams with the skills and confidence they need to succeed. The goal is moving from current state to future state with minimal disruption and maximum buy-in across all levels of the organization.
AI-assisted tools raise the stakes of that last point. When a system recommends a price, a discount or a rebate, adoption is no longer a matter of logging in. It means people act on the recommendation, or knowingly choose not to. That is a question of trust, and trust is a change management problem.
Why change management matters
The only constant in modern business is change. But while change is inevitable, successful adaptation is not.
Adoption is where most initiatives fail. You can roll out the best technology or announce a brilliant new strategy, but none of it matters if people don’t actually use it. Every shift in strategy, new technology implementation, and cultural realignment hinges on effective change management.
Prosci’s benchmarking data shows that initiatives with excellent change management are seven times more likely to meet their objectives than those with poor change management.
Poor change management has real costs: misaligned teams, systems that sit unused, and lost investment. Cencora, a leading pharmaceutical distributor working with Vendavo, demonstrates the alternative. By partnering with Vendavo and implementing structured change management practices, they accelerated their business case and paid back their investment six months earlier than expected. The key was building internal trust, aligning pricing and sales teams, and ensuring smooth adoption of new processes.
That word, trust, matters more now. AI assistants are entering pricing, quoting and rebate workflows, and the organizations that capture their value will be the ones whose people trust them enough to act on what they recommend. The sections ahead cover the types of change you will face, what AI adds to the picture, proven strategies and models, common pitfalls, and how adoption plays out by role.
What changes when the tool is AI
Adopting an AI assistant is a different kind of change from adopting a new system. A new system changes how people do a task. An AI assistant changes who makes the first call.
That shifts the adoption question. People are no longer only asking “do I know how to use it?” They are asking three things:
- Can I see why? Will I understand the reasoning behind a recommendation well enough to defend it to a customer, a CFO or an auditor?
- Can I overrule it? If the recommendation is wrong for this account, can I change it without a fight, and will that read as judgment rather than resistance?
- Who answers for the outcome? If I follow the recommendation and it goes badly, or ignore it and it would have worked, who is accountable?
Leave those unanswered and adoption stalls, however capable the model. Copperberg and Vendavo’s 2025 survey of senior pricing, commercial and finance decision-makers at manufacturers and distributors in North America and Europe found that just 13% had deployed AI in pricing workflows and only 9% reported measurable ROI. Vendavo’s reading of the gap: distrust, fragmentation and a lack of transparency.
The same research found that 85% of organizations still rely on spreadsheets in daily pricing workflows. Copperberg and Vendavo’s Pricing Execution Gap Playbook reads that dependence as a sign of low trust in the official workflow: the spreadsheet fills a gap the workflow leaves open. Spreadsheets are what people reach for when they don’t yet trust the tool in front of them.
Explainability is necessary, but it is not enough. It shows how a recommendation was produced. Trust depends on whether a seller, a finance leader or an executive recognizes their own business in it. Vendavo’s view is that trust has to be designed in before launch, not added afterward, by bringing pricing, sales, finance and executive leaders into the decisions about inputs, constraints and success measures early.
The three questions map to three practices, covered in detail below: human approval (who decides), explainability (can I see why), and the override rate (how you know trust is growing).
Types of organizational change
Not all change looks the same. Understanding what type of change you’re dealing with helps you tailor your approach and set realistic expectations for how long adoption will take. An AI rollout usually involves several types at once.
- Transformational change: Large-scale shifts like mergers, acquisitions, or digital reinvention that fundamentally reshape how the business operates. For pricing teams, this could mean pivoting from cost-plus pricing to value-based models across the entire product portfolio. The scope is wide, the stakes are high, and the timeline stretches across quarters or even years.
- Incremental change: Gradual improvements to processes, tools, or structures that refine existing operations without turning everything upside down. Think of adjusting pricing segmentation rules or adding new discount approval workflows. These changes build momentum over time and tend to face less resistance because they feel less disruptive.
- Technological change: New systems or tools that impact workflows and daily tasks. Implementing a quoting platform, rebate management software or an AI assistant falls into this category. Adoption hinges on how well people understand the tool and trust it to make their jobs easier. With AI, trust has a specific meaning: people need to see why a recommendation was made and know they can overrule it.
- Cultural change: Shifts in values, behaviors, or mindset across the organization. This happens when sales teams move from relationship-driven negotiation to data-driven pricing decisions, or from overriding by instinct to working with guidance. If the culture still rewards deep discounting to close deals, the best pricing software in the world won’t deliver results.
- Strategic change: Adjustments to business direction or market positioning that require realigning teams and processes. When a company shifts from transactional sales to strategic account management, pricing and revenue operations need to adapt their entire playbook to support longer sales cycles and value conversations.
Strategies for successful change management implementation and adoption
Change doesn’t happen when you decide. After you decide, you have to implement and adopt those changes in a way that ensures lasting success. The right strategy coupled with strong execution makes the difference. The three phases below keep the original blueprint and add what AI-assisted tools require at each stage.
Pre-change: setting the foundation
Before announcing any changes, you need a solid foundation. This phase determines whether your initiative gains traction or stalls before it begins.
- Make structured plans.
A successful change management plan needs structure and clear organization, so every facet is aligned and the trajectory is focused. Corning Optical’s implementation with Vendavo demonstrates this. By working with Vendavo Professional Services for speedy implementation and expert guidance, they achieved $10 million in positive financial impact within the first year. - Prioritize key elements for maximized adoption.
Identify the specific problems your project aims to address. Set clear objectives and make sure your team understands them. Cencora’s pricing team faced pressure from healthcare reform and price transparency mandates, so they focused their Vendavo partnership on streamlining workflows at both product and customer levels. - Get the right stakeholders involved.
Engaging all relevant parties from the beginning prevents surprises and fosters collaboration. Cencora aligned its pricing team with sales and financial planning early, which proved critical to developing revenue-generating initiatives that supported strategic growth. - Decide what the AI is allowed to decide.
Before launch, define which recommendations apply automatically, which need review, and which need approval, and who gives it. McKinsey frames the test as whether a decision can be executed with clear rules, auditability and reversibility. The next section shows how. - Check the data before you promise the outcome.
In McKinsey’s survey, more than half of respondents cited data quality and availability, along with integration complexity, as the most common barriers to realizing value from AI in pricing. Bring IT in at the start, not at go-live.
Execution: driving change forward
Once the groundwork is laid, execution determines whether change takes hold. This phase requires consistent leadership presence and transparent communication.
- Understand the importance of leadership.
Successful adoption needs committed leaders who initiate projects, then champion them with sustained support. One major manufacturer working with Vendavo had executives keep sending messages throughout implementation about why the project mattered to company success. - Prioritize regular communication.
Regular updates keep stakeholders informed and engaged. The same manufacturer distributed a newsletter every two to three weeks to keep people informed, which generated strong interest across all stakeholder groups. - Engage employees to encourage change.
Top-down support cultivates an environment where change is embraced. Huhtamaki enhanced user adoption by improving feedback loops. They didn’t just implement new systems; they made sure sales teams could capture and share customer insights effectively. - Cultivate a culture of learning.
Organizations that make learning and adaptability part of their culture are better positioned to manage change. Corning Optical transitioned 225 active users from an aging, specialist-only analytics system to Vendavo’s platform, where product line management teams could quickly access and work with data themselves. - Start with a pilot that proves value in months.
McKinsey advises choosing a first use case that can show measurable impact within three to six months, with limited integration complexity, clear ownership and tangible business outcomes. - Show the reasoning every time.
A recommendation that arrives without its why teaches users to ignore it. Build the explanation into the workflow where the decision is made.
Post-change: measuring success and sustaining momentum
Change doesn’t end at implementation. The post-change phase determines whether new processes stick or fade back into old habits.
- Measure adoption and beyond.
Assessing success typically revolves around ROI and user engagement. Software can show how many users actually log in and work in a system. For AI-assisted tools, go a step further and track what happens to recommendations: how many are accepted, changed or rejected, and why. - Build credibility through consistent delivery.
Nothing sustains change like proven results. Cencora’s partnership with Vendavo focused on consistent delivery of measurable outcomes. By accelerating their business case and paying back their investment six months earlier than expected, they strengthened stakeholder confidence in ongoing pricing strategies.
The right strategies lay a strong foundation. But recognizing potential roadblocks is equally critical, because even well-laid plans can meet unforeseen challenges that derail the process if they are not managed.
Building trust into AI-assisted pricing: approval, explainability and the override rate
Three practices answer the three questions people ask of an AI recommendation. Together they turn adoption from a leap of faith into a managed process.
Human approval: decide what AI may decide
AI adoption goes wrong when everything is automated at once or nothing is. The better question is where decisions can be safely delegated. McKinsey’s guidance is that agentic AI can operate with more automation where decisions can be executed with clear rules, auditability and reversibility, and that where those conditions do not hold, AI should support people with faster analysis and better explanations.
In practice, write the tiers down before launch. The split below is illustrative. Your governance sets the real thresholds.
| Tier | Typical decisions | Who decides |
| AI applies, within guardrails | Routine, reversible updates inside defined price floors and ceilings | AI acts; humans review exceptions |
| AI recommends, human approves | Discounts below target, list price changes, rebate exceptions | Seller, deal desk or pricing lead, by threshold |
| Human decides, AI informs | Strategic accounts, new-to-market pricing, contract terms | Pricing, Sales and Finance leaders |
The point is not to limit the AI. It is to make accountability visible. Vendavo’s position is that explainable AI works alongside pricing expertise and that teams stay in control of pricing strategy while AI speeds up analysis. Every decision should be traceable and auditable, with a clear rationale, inputs and outcome.
Explainability: show the reasoning where the decision happens
A recommendation people cannot explain is a recommendation they will not use. Each one should show the inputs, assumptions and constraints behind it in business language, inside the workflow, not in a separate report.
McKinsey’s case of a $15 billion distributor shows the payoff. More than 1,000 sales consultants received not only new prices but also clear, defensible language to explain them in customer conversations. The AI Pricing Assistant follows the same principle. It runs predefined algorithms, validated by Vendavo data scientists, on your transactional data, so the same question returns the same answer and each number traces back to its inputs.
Then go one step further. Add commercial context: the account’s history, its strategic status, the expected effect on the metrics each role owns. Explainability shows how. Context shows why it makes sense for this customer, which is what moves a seller or a CFO to act.
The override rate: how you know trust is growing
The override rate is the share of AI recommendations that people change or reject, with the reason recorded. Treat it as a signal, not a policing tool.
- A high rate points to a problem with the data, the logic or the level of trust. Find out which.
- A rate near zero deserves a question too. It may mean the recommendations are good, or it may mean people stopped looking.
- Reason codes are the real value. “Strategic account” and “stale cost data” call for different fixes.
McKinsey describes a building materials distributor whose sellers overrode system-generated prices in more than half of transactions, citing unreliable product data and inconsistent pricing logic. After the company fixed its product data and deployed a new pricing architecture, manual overrides fell 80% and margin expanded by more than 200 basis points. The sellers had not been resisting. They had been telling the company something.
Track the override rate next to logins and usage. Review it monthly with Pricing, Sales Operations and IT in the room, because each of them can fix a different cause.
Change management in action: adoption by role
Change management isn’t theoretical. It plays out differently depending on who leads the charge and what they are trying to accomplish. The same AI recommendation raises a different question for each role.
| Role | What they need before they trust it | What to put in place | What to measure |
| Pricing and commercial leaders | Authority to set guardrails; evidence behind each recommendation | Approval tiers, governed guardrails, an audit trail | Override rate and reasons; time saved on analysis |
| Sales operations and sales leaders | Guidance inside the quote or deal workflow; a reason they can give the customer | Clear thresholds, faster approvals, reason-for-price language | Quote turnaround; share of deals needing approval |
| IT and operations | Clean, integrated data; security and compliance; control over who can change what | Data ownership, integration plan, access and audit controls | Data quality; integration health; security review status |
For pricing and commercial teams: transforming how pricing is structured and delivered
Pricing teams face a unique challenge. You are not just rolling out new software. You are shifting how the organization thinks about value, discounting and profitability. With AI, you are also moving from doing the analysis to supervising it: setting guardrails and governing what is automated.
Cencora’s experience shows what this looks like in practice. Facing pressure from healthcare reform and price transparency mandates, they aligned their pricing team with sales and financial planning early. That enabled revenue-generating initiatives that supported strategic growth. “We were able to accelerate our business case and pay back the investment six months earlier than expected,” says Maureen Swanson, Vice President of Pricing at Cencora.
DeLaval took another effective approach, moving from cost-plus to value-based pricing across multiple markets. They started with comprehensive training for a large cross-functional team before rolling out organization-wide, so teams understood not just the new system but the future direction of pricing at the company.
Time is the early proof point for AI. A medical device manufacturer using the AI Pricing Assistant saves about 80% of its time on pricing analysis, roughly 20 hours a week for the team. That time is what lets pricing leaders move from preparing numbers to challenging and governing them.
For sales operations and sales leaders: rolling out new tools and process changes
Sales leaders often face the most resistance when implementing change. Sales teams have habits and approaches that made them successful, so asking them to change can feel like questioning their expertise. The shift from relationship-driven negotiation to data-driven pricing is particularly hard.
One major manufacturer addressed this by having executives communicate continuously about why the project mattered. They also created a newsletter distributed every two to three weeks to keep stakeholders informed, which generated strong interest across all groups.
Dell Technologies shows what is possible when sales enablement and change management align. After implementing Vendavo across a $60 billion product portfolio, they improved quote velocity to just 4 hours for 90% of deals. The transformation required C-suite buy-in first, then a systematic move from simple calculations to optimized pricing strategies. Success came from helping sales teams trust that the system would make their jobs easier, not harder.
For AI-assisted tools, sales operations owns the details that decide adoption: where the recommendation appears (in the workflow, not beside it), which approvals still apply, and whether a seller can say why a price is right when the customer pushes back. The Copperberg and Vendavo playbook describes strong pricing execution as sellers completing pricing decisions without leaving the quoting workflow.
For IT and operations teams: managing digital transformation and tool adoption
IT and operations teams carry the technical burden of change management. You are responsible for system integration, data migration, and making sure everything works while people are still learning.
Corning Optical transitioned 225 active users from an aging, specialist-only analytics system to Vendavo’s platform, where product line management teams could quickly access data themselves. They worked with Vendavo Professional Services for implementation support and expert guidance. “Vendavo’s integrated professional services have helped us climb the learning curve faster, implement more smoothly, and deliver on the value of this investment which we’ve calculated in the first year at approximately $10 million in positive financial impact,” says Ken Foret, Manager of Pricing Enablement at Corning. Their success came from outlining metrics for success upfront, setting clear goals and measuring progress consistently.
AI raises two issues for IT. The first is the data and infrastructure foundation. In the Copperberg and Vendavo survey, only 14% of organizations described their pricing infrastructure as fully scalable and 46% called themselves only partially automated.
In McKinsey’s survey, more than 60% of early-stage respondents struggled with incomplete, inconsistent or siloed data and systems. The second arrives with maturity: close to 60% of adopters with high AI performance named security and compliance as a top concern as AI use spreads across sensitive commercial processes. Plan for both from day one, and give users help where they work. The AI Documentation Assistant answers how-to questions inside the product, which takes pressure off formal training.
Proven models of the change management process
Change management isn’t new. Researchers and practitioners have developed several proven frameworks over the decades. They provide structure when you’re navigating transitions and help you avoid the pitfalls that derail adoption. The right framework depends on your organization’s needs, culture, and the type of change you’re managing. Some models focus on individual transitions, others take a broader organizational view. Each one below includes a note on how it applies to an AI rollout.
ADKAR model
ADKAR breaks individual change into five sequential building blocks: Awareness, Desire, Knowledge, Ability, and Reinforcement. Developed by Prosci founder Jeff Hiatt in the 1990s, it gets each person through change one stage at a time. You can’t move to the next stage until you’ve completed the previous one. Employees won’t develop the desire to change until they understand why it’s necessary.
If someone resists a new system, ADKAR helps you pinpoint where they’re stuck. Maybe they understand why the change is happening (Awareness) but don’t see a personal benefit (Desire).
With AI: Knowledge and Ability should include how to read a recommendation and when to override it. Reinforcement is the monthly override review.
Best used when: You need a people-first approach that tracks individual progress, particularly for technology rollouts where you can measure adoption at the individual level.
Kotter’s 8-step process
John Kotter’s model is a sequential roadmap for leading large-scale change. The steps: create urgency, build a guiding coalition, form a strategic vision, enlist a volunteer army, enable action by removing barriers, generate short-term wins, sustain acceleration, and institute change. It emphasizes momentum and quick wins, and recognizes that change needs champions at multiple levels, not just executive sponsorship.
It shines in transformational change, such as moving from cost-plus to value-based pricing across an entire organization.
With AI: Build the guiding coalition across pricing, sales, finance and IT. Make the first pilot your short-term win.
Best used when: You’re leading large-scale transformations that need sustained momentum over months or years, with a clear sequence of milestones.
Lewin’s 3-stage model
Kurt Lewin’s model is one of the oldest and simplest. It has three stages: Unfreeze (prepare for change), Change (implement the transition), and Refreeze (solidify the new state). Before people can adopt something new, they need to let go of the old way. Refreezing makes new behaviors the standard operating procedure rather than a temporary experiment.
It works well for incremental changes, such as adjusting discount approval workflows or refining segmentation rules.
With AI: Unfreezing means retiring the shadow spreadsheet. Refreezing means the recommendation becomes the default starting point for every deal.
Best used when: You’re making focused, incremental improvements and need a framework that’s easy for everyone to remember.
McKinsey 7-S framework
The 7-S model examines seven interdependent elements: Strategy, Structure, Systems, Shared Values, Style, Staff, and Skills. Change succeeds when all seven align. You can’t change one element without considering how it affects the others. New pricing software (Systems) requires people with analytical capabilities (Skills and Staff), leaders who value data-driven decisions (Style and Shared Values), and a structure that supports collaboration between pricing and sales.
It excels at diagnosing why change efforts stall.
With AI: If recommendations are ignored, check Structure and Shared Values first. Your systems may be modern while compensation still rewards relationship-based selling.
Best used when: You need to diagnose why initiatives are struggling, or you’re planning complex transformations that require alignment across multiple dimensions.
Bridges’ transition model
William Bridges distinguishes between change (external events) and transitions (internal psychological processes). His model has three phases: Ending, Neutral Zone, and New Beginning. People experience loss when old ways disappear, even if the change is positive. The Neutral Zone is the uncomfortable in-between where old methods no longer work and new ones aren’t yet established.
It is particularly valuable for cultural change. When sales teams move from relationship-driven negotiation to data-driven pricing, they are letting go of an identity and approach that defined their success.
With AI: For sellers and pricing analysts, an AI assistant changes what their expertise is for. Name that shift openly. Judgment moves from producing the first answer to knowing when the answer is wrong.
Best used when: Change involves significant cultural or identity shifts, where acknowledging the emotional side of transition is critical.
Avoid these common issues and pitfalls in change management
Several common issues can hinder a change initiative. They tend to stem from organizational, procedural or interpersonal challenges. The first eight apply to any change. The last four are specific to AI-assisted tools.
- Resistance to change. It can come from fear of the unknown, discomfort with new processes, or a perceived threat to job security or routines. Resistance can surface at any level, from executives reluctant to allocate resources to frontline employees who don’t see why their current approach needs fixing.
- Lack of effective communication. Insufficient or unclear communication leads to misunderstandings about why the change is happening, what it brings, and how it will unfold. “Change management strategies often fail or fizzle out when leaders don’t communicate enough after the initiative is announced,” says Mary Sharp Emerson at Harvard DCE.
- Inadequate leadership support. Leaders must be actively involved, giving the change the authority and momentum it needs. When executives don’t visibly model new behaviors or dedicate time to the initiative, it signals that the change isn’t a priority.
- Poor planning. Leaders often focus on what the change is and why it matters, but neglect to plan how it will actually happen. That leads to short-term tactical decisions that undermine long-term results.
- Insufficient training and resources. Employees must be prepared for new systems or processes, with comprehensive training and ongoing support. Cutting change management budgets to save money upfront almost always costs more later, when support has to be retrofitted after implementation stumbles.
- Lack of stakeholder engagement. Leaving key stakeholders out of planning and implementation costs buy-in and loses input from the people who know best what the change will affect. Misalignment from leadership derails an initiative immediately. Frontline exclusion does it gradually.
- Ignoring company culture. If the change contradicts established norms and values, expect substantial resistance. People resist when they lack confidence that the organization will support and reward new behaviors over the long term.
- Failure to address change fatigue. Constant change takes a cumulative toll on performance, morale and enthusiasm. Pushing too hard, for too much, too quickly, increases the risk of mistakes and burns out your team before the change takes hold.
- Black-box recommendations. Users who can’t see why a price was suggested will not defend it to a customer, and will quietly stop using the tool.
- No path to override. If overruling the AI feels like a career risk, people will either comply without thinking or work around the tool. Both hide the problem. Make overriding easy, expected and recorded.
- Automating before the data is ready. Weak product, cost or customer data produces weak recommendations, and users notice first. McKinsey’s survey ranks data quality and integration among the most common barriers.
- Treating security and compliance as an afterthought. Concern about them tends to grow as AI use spreads across sensitive commercial processes. Involve IT, Legal and Finance before the pilot, not after the first question from an auditor.
Proactively addressing these issues reduces the risk and improves the likelihood of adoption. The key is recognizing the pitfalls early and building strategies to counter them before they undermine your initiative.
10 change management best practices
Following best practices helps keep transitions smooth and the improvements lasting. Here are ten, updated for teams adopting AI-assisted tools.
- Set a clear vision and objectives. People need to understand where you’re going and why it matters. Your vision should be simple enough to explain in two minutes but compelling enough that people remember it.
- Identify and involve all key stakeholders. Bring the right people to the table from day one, including IT, Finance and the sellers who will use the tool. When stakeholders help shape the change instead of just receiving it, they become advocates instead of obstacles.
- Communicate effectively and often. You can’t over-communicate during change. What feels like repetition to leadership is often the first time frontline employees are hearing the message.
- Get commitment and buy-in from leadership. Prosci’s research found that projects with an extremely effective sponsor met or exceeded objectives more than twice as often as those with a very ineffective one. Leaders can’t just send an email announcing support. They need to be present, ask questions and visibly use the new way of working.
- Equip your team with comprehensive training. Handing someone a new system without training is like giving them a sports car without driving lessons. For AI-assisted tools, teach people how to read a recommendation, question it and override it, not only where to click.
- Tailor your strategies to unique business needs and outcomes. Cookie-cutter approaches rarely work. Your industry, culture and specific challenges should shape how you manage change, not the other way around.
- Establish KPIs and monitor them regularly. Define success before you start, then track it closely. Include the override rate and its reasons alongside usage and ROI, so you can course-correct while it still matters.
- Celebrate milestones throughout the process. Long transformations lose steam without wins along the way. Recognize progress publicly to prove the effort is paying off and keep teams motivated.
- Ensure your new process will be sustainable. The real test comes six months after implementation. Build accountability mechanisms, such as a standing monthly review of approvals and overrides, that sustain new behaviors after the initial excitement fades and the change team moves on.
- Anticipate resistance. Expect pushback and plan for it. Understanding where resistance will emerge lets you address concerns before they harden into active opposition. With AI, much of what looks like resistance is a legitimate question about data, logic or accountability. Treat it as feedback.
Implementing these practices minimizes disruption and maximizes the effectiveness of change initiatives, leading to better outcomes and greater organizational resilience.
The journey toward improvement
Change management is a continuous journey, not a one-time initiative. It requires ongoing commitment, strategic planning and active involvement from all levels of an organization. A structured approach helps organizations handle the complexity of modern business and position themselves for long-term resilience.
With AI-assisted tools, the journey has one more element: trust, built deliberately rather than assumed.
- Decide what AI may decide, and write down who approves the rest.
- Show the reasoning, with commercial context, inside the workflow.
- Track the override rate and its reasons, not only logins.
- Plan adoption by role. Pricing, Sales Operations and IT each need something different.
Ready to bring AI-assisted pricing, quoting and rebates into your organization with trust built in? Schedule a demo to speak with a Vendavo expert.
Keep reading: Why Trust Matters More Than Explainability in Pricing AI and 5 Ways AI Is Reshaping the Future of B2B Pricing.