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Categories: Sales Kickoff | Artificial Intelligence
Is improving AI adoption, impact and outcomes a priority for your 2027 SKO?
In this article, we provide detailed breakdowns and planning guides for the following (click to jump to section):
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How to Use the Sales Kickoff to Improve Adoption of Existing AI Tools
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How to Prepare Managers to Reinforce AI Adoption and Improve Execution
- How to Measure Success of AI Adoption After the SKO
Keep reading for everything you need to know about planning your 2027 SKO with AI top-of-mind. For more resources on planning a SKO that improves execution and sales performance, check out our Sales Kickoff Planning Resource Guide.
Why AI is a Critical Priority for the 2027 SKO
Although we're now several years into the AI boom, workforce AI is still emerging as a top priority in 2027 planning and many kickoff agendas.
Misalignment on AI deployment, adoption and governance is a major source of revenue risk for sales organizations today. 85% of organizations increased their AI investment last year, but only 10% reported achieving significant ROI. Lackluster results from AI deployments are hurting revenue margins and efficiency as pressure mounts to remain competitive in an AI-first world.
Phase one of AI deployment was the 'move fast, break things' stage, pursuing innovation at all costs. As we look forward to 2027, many leaders are strategizing for phase two: functional integration of AI tools into workflows, planning and workforce development. In this era, AI success is not measured by activity alone; measurable impact on productivity and revenue is paramount.
The 2027 SKO is an important opportunity to move from AI-forward strategy to elite AI-enabled execution. Sales, RevOps and Enablement leaders can use their 2027 SKO to:
- Drive AI adoption across priority sales workflows
- Provide hands-on AI training using realistic sales scenarios
- Define where AI sits in revenue-driving GTM workflows
- Teach sellers how to evaluate AI-generated output
- Prepare sales managers to coach and reinforce AI usage
- Define processes for using AI data in performance measurement and improvement
- Connect AI productivity gains to measurable revenue ROI
The objective is clear: sellers should leave SKO knowing how AI can help them execute the sales process more effectively, where their judgment remains essential and what they will be expected to do differently once they return to the field.
How Can You Plan a SKO that Improves Adoption of Existing AI Tools?
1. Understand Current Execution and AI Usage
Many organizations do not need another AI launch. They need to improve the use of tools they already own.
Low adoption is not always caused by seller resistance. It can also signal that the organization has not clearly connected the tool to the sales process. To ensure AI tools are relevant and aligned to day-to-day activities, you must first understand the current workflows. Take the time to do internal discovery on how GTM process are actually executed today, and where AI fits into them.
Then, before planning your SKO, diagnose where adoption is breaking down.
AI adoption diagnostic
| Adoption problem | What may be happening | SKO response |
|---|---|---|
| Teams don't use preferred AI tools | The use case is unclear or disconnected from the workflow | Show exactly when and why sellers should use it |
| Teams use AI inconsistently | Teams have no shared standard | Define the required workflow and expected output |
| Sellers generate low-quality output | Prompts, inputs or source data are weak | Teach sellers how to provide structured context |
| Usage is high but performance is unchanged | Underlying sales processes may be unclear | Connect use to shared language, process and accountability |
| Managers do not reinforce it | Managers were not prepared to coach the workflow | Run a separate manager enablement session |
| Sellers use unapproved tools | Governance feels unclear or impractical | Clarify approved use, demonstrate value of approved tools |
| The tool creates more work | It sits outside the operating rhythm | Redesign the workflow before pushing adoption |
Use these insights to structure your SKO session around resolving specific adoption barriers rather than repeating a general introduction to the technology.
2. Develop Your AI Adoption Plan
A strong AI adoption plan should answer five questions:
| Guiding question | What must be defined |
|---|---|
| What problem are we solving? | The seller task, workflow gap or execution challenge AI should improve |
| Where should AI be used? | The stage, activity or decision where the tool supports the seller |
| What does good output look like? | The standards sellers should use to evaluate AI-generated information |
| What requires human judgment? | The decisions sellers and managers remain accountable for |
| How will we know it is working? | The behavior, productivity and performance metrics that will be measured |
Answer these questions before building the SKO agenda. Otherwise, the AI portion of the event can easily become a product demonstration that generates initial interest without changing how sellers work.
- Where are sellers losing the most time?
- Which activities vary significantly across the team?
- Where does poor execution create downstream revenue risk?
- Which use cases already have enough process structure and data to support AI?
- Which use cases can managers realistically reinforce?
- What should sellers be able to do differently immediately after SKO?
The final question should drive the agenda.
3. Reinforce Elite Sales Execution
Often, AI productivity and efficiency gains can be improved by strengthening the sales processes surrounding the tool. When teams have unclear processes for pipeline progression, handoffs and coaching, adding technology only serves to muddy the waters further. Ensure success by using your SKO to reinforce a repeatable sales process that drives predictable revenue.
Many teams think they have alignment on the value-based sales process, but in reality, day-to-day execution is highly variable. Your SKO should create greater consistency around the behaviors that drive successful customer conversations and opportunity progression.
Focus reinforcement on the following actions:
- Strengthen value-based business conversations. Train sellers to uncover customer pains, priorities and desired outcomes, then connect your solution to measurable business impact. AI-generated insights should help sellers prepare for stronger conversations rather than replace their judgment.
- Reinforce clear qualification criteria. Define the information sellers must uncover and validate to progress an opportunity. A consistent framework, such as MEDDICC, gives sellers, managers and AI tools a shared standard for evaluating deal quality.
- Align AI use cases to the sales process. Clarify where AI should support preparation, qualification, messaging or opportunity review, along with the inputs and success indicators required for each use case.
- Prepare sellers to navigate complex buying groups. Reinforce how to identify missing stakeholders, reach decision-makers and build consensus across the buying committee. AI can help surface gaps, but sellers still need the skills to secure agreement.
- Protect the human advantage. Use data and AI to improve preparation and execution while continuing to develop the curiosity, business acumen and conversational skills that build trust with buyers.
- Equip managers to reinforce the process. Give managers a structured approach for identifying successful behaviors, coaching execution gaps and developing greater consistency across the team.
The stronger and more consistent the underlying sales process, the more effectively AI can reinforce execution, surface useful insights and improve performance.
How Should You Roll Out a New AI Sales Tool at SKO?
1. Decide What AI Should Improve Before Planning the Session
Organizations are most effective at using AI to improve sales team execution when they understand and can communicate the specific workflow challenges they are attempting to solve with the technology. Implementing AI tools without clarity on what they solve is one of the biggest mistakes that create failed AI launches for sales teams.
To ensure productivity gains, start by identifying the workflow you need to improve. Use the below table as an example to guide you.
AI use-case planning table
| Current challenge | AI-supported workflow | Desired seller behavior | Business impact |
|---|---|---|---|
| Sellers spend too long researching accounts | Account and industry research | Sellers enter meetings with relevant business context | More productive preparation time |
| Discovery conversations are inconsistent | Meeting preparation and call review | Sellers prepare focused questions and identify missing information | Better opportunity quality |
| CRM updates are incomplete | Call summaries and data capture | Sellers validate and record required opportunity information | Greater forecast confidence |
| Messaging varies across the team | Message preparation | Sellers adapt approved messaging to the buyer’s situation | More consistent buyer engagement |
| Managers lack time to inspect every opportunity | Deal review and coaching preparation | Managers focus coaching on the most important gaps | More targeted coaching |
| Sellers struggle to identify deal risk | Opportunity analysis | Sellers validate stakeholders, decision criteria and next steps | Stronger qualification |
| Follow-up is slow or generic | Post-meeting preparation | Sellers create relevant follow-up based on agreed customer outcomes | Better deal progression |
Communicating use case and outcome to sellers and managers will help them understand where tools fit in their workflow, when they should be used, and motivate them to adopt the desired tools and behaviors.
2. Make Your AI Rollout Practical and Hands-On
Research shows that 61% of workers feel overwhelmed by the volume of new AI information. By giving clear use cases and training on practical application, you can help reduce the noise and provide a clear roadmap to adoption.
Avoid giving an overview of technical capabilities; instead, tie each capability and use case to how it will help each role be more successful. Clearly outline the cadence or triggers on which the tool will be used and what is expected from each role.
1. Suggested 60-minute AI rollout session
| Time | Agenda item | Purpose |
|---|---|---|
| 0–10 minutes | Business reason for the rollout | Explain the execution problem and expected outcome |
| 10–20 minutes | Workflow demonstration | Show the capability inside a realistic seller scenario |
| 20–40 minutes | Hands-on excercise | Have participants complete the task using an active or sample opportunity |
| 40–50 minutes | Output review | Compare results and identify what made an output useful |
| 50–60 minutes | Expectations and next steps | Define usage standards, manager reinforcement and follow-up actions |
Avoid using the full session for a live demonstration. Watching someone else use AI does not prepare sellers to apply it during an actual opportunity. Sellers should leave the session having completed the intended workflow themselves, while managers should understand how to identify and measure success.
Example SKO exercise: AI-assisted meeting preparation
Provide sellers with a sample account, customer situation and upcoming meeting.
Ask them to use the approved AI tool to:
- Identify relevant business context
- Develop discovery questions
- Connect the questions to the organization’s sales methodology
- Flag assumptions that require validation
- Select the questions they would actually use
The final step is important. Sellers should not treat AI-generated output as a finished deliverable. They remain responsible for determining whether the information is accurate, relevant and appropriate for the buyer.
3. Equip Sellers to Evaluate AI Output Using Value-Based Frameworks
Organizations get inconsistent AI productivity results when they don't clearly define the human-led process behind the technology. The quality of AI output depends heavily on the quality of the inputs, the structure of the sales process and the standards teams use to review the result. AI works best when it reinforces a clear sales motion, common language and consistent approach to creating, capturing and qualifying value.
Your AI initiative at SKO is a great opportunity to refresh value-based language and methodology in the context of new, AI-powered workflows. Make sure the following is clearly defined, and sellers are prepared to apply it to their AI inputs and outputs:
- The business value of your solution
- Customer pain points and use cases that align to your solution
- Shared value-based language around your solution
- Qualification and deal stage criteria
- What an ideal deal looks like for your company
Give sellers a simple review standard, customized to your value-based language and methodology, that they can apply after every AI-supported task.
How to Prepare Managers to Reinforce AI Adoption and Improve Execution
Managers are a critical part of ensuring adoption and behavior change after the SKO, which is why we recommend training managers ahead of the sales kickoff to improve ROI.
Your front-line managers should be instrumental to your AI adoption plan. Provide them with a framework and cadence for inspecting AI usage and coaching teams to desired behaviors. The manager framework should cover the following areas:
- Expected behavior: What sellers should do differently, when they should use AI, and what strong execution looks like. Create a cadence for updating, reviewing and cataloging standards and examples of success.
- Inspection points: Where managers will review AI-supported work within existing deal reviews, one-on-ones and forecast calls. Provide guiding frameworks for desired messaging and qualification practices, and equip managers to inspect AI usage for alignment with value-based methodology.
- Coaching questions: How managers will assess the quality of inputs and outputs, challenge seller assumptions and connect insights to next actions. Ensure managers are prepared to dig deeper on seller-verified data and assess the process that was used to reach that
- Performance signals: Which adoption, productivity and execution metrics managers should monitor to measure success.
- Escalation path: Where managers should go for clarity and reporting about data quality, governance or tool performance.
Ensure managers are briefed on the content of your SKO training ahead of time and have clarity on their role. Their help administering roleplay and practices will strengthen seller understanding as well as their own ability to judge and develop what good looks like. For more resources on enabling managers, check out our front-line manager success resources.
How to Measure SKO Success
Ensuring adoption after your SKO is arguably the most important part of this process. We have lots of resources on planning your sales kickoff reinforcement plan for improving sales execution. When it comes to your sales kickoff AI initiative, the approach is similar:
- Make the initiative a visible priority. Have leaders reinforce its importance before, during and after SKO through consistent language, communication and example-setting.
- Make new behaviors immediately relevant. Tie SKO content to current customer challenges, live opportunities and each role’s day-to-day responsibilities.
- Build the initiative into existing workflows. Embed new technology and desired behaviors into your existing tech stack and sales workflows.
- Set clear post-SKO benchmarks. Use 30-, 60- and 90-day measures to track adoption, identify gaps and course-correct quickly.
- Equip managers to reinforce execution. Give managers coaching playbooks, inspection criteria and a consistent cadence for driving accountability and adoption.
Defining Metrics to Measure AI Success After the SKO
Below is a table to illustrate examples of potential metrics to track to measure the impact of AI tools after your SKO initiative. Don't just measure activity; to get full visibility on AI adoption and its impact on key business objectives, measure metrics from multiple categories below. Then, cross-reference them to identify areas of weakness and opportunity. Example: Did teams with better tool input quality see higher forecast accuracy?
| Measurement area | Metrics to track |
|---|---|
| Adoption |
- User-level activity by team/role/individual |
| Productivity |
- Meetings per rep, frequency of meetings |
| Execution quality |
- Pipeline progression/qualified stage exits |
| Revenue impact |
- Forecast accuracy |
Plan a Sales Kickoff that Drives Revenue
Force Management has been helping sales organizations align and improve execution to drive rapid, scalable growth for over 25 years. Our team has participated in thousands of sales kickoffs, and we know what separates the ones that impact the bottom line from those that are forgotten in two weeks.
Explore our full list of sales kickoff planning resources for leaders and enablement teams.
