Your AI Strategy Starts with One Platform

By Allison Bouchard, VP of Global Accounts at Outreach

If your AI plans feel exciting on paper but clunky in practice, you’re not alone. Most teams we work with admit their tools are loosely connected, their data lives in silos, and AI is sprinkled in ways that don’t deliver real outcomes. In our recent webinar, Your AI Strategy Starts with One Platform, I sat down with guest speakers Katie Linford, Principal Analyst, Forrester, and Hamish Hill,Director of Marketing Technology, SolarWinds, to explore why consolidation isn’t just about saving budget. It’s the foundation that makes AI work at scale across your revenue ecosystem. Here are the highlights. 

Consolidation ≠ Cost Cutting—It's Your AI foundation

The first question most revenue leaders face today is: How are you cutting costs? The second: What’s your AI strategy? The truth is, those aren’t separate challenges, they’re connected. 

Yes, there’s real money to be saved by consolidating tools. Many of our customers have cut hundreds of thousands of dollars from their budgets this way. But if you stop at cost savings, you’re missing the bigger opportunity. Consolidation is really about creating the foundation for something far more powerful: AI-powered sales transformation. 

Without unified data and connected workflows, AI can’t deliver the productivity, precision, and predictability that leaders expect. As Forrester’s Katie Linford shared on the webinar, too many organizations fall into “tech sprawl”, duplicate systems, disconnected data, and one-off integrations that slow everything down. By consolidating, you reduce redundancy, improve data quality, and set the stage for AI that actually scales. 

Working with enterprise accounts on a global scale, I see this pattern repeatedly: teams with fragmented tech stacks struggle to get meaningful insights from their AI investments. They have point solutions for prospecting, another for call analysis, a third for deal forecasting — each with its own data model and limited context. The result? AI that feels more like a collection of party tricks than a strategic advantage.  

The companies that succeed with AI take a different approach. They build from a unified foundation where data flows freely, context is preserved, and AI can see the full picture. That’s where you start seeing real outcomes, not just efficiency gains, but fundamental improvements in how revenue teams operate. 

The Data Layer That Turns Signals into Insight

Here’s what most leaders miss: AI is only as good as the data it can access. Fragmented stacks create latency and noise. Centralized, governed data enables real-time AI that actually helps your teams make better decisions.  

In the webinar, Katie outlined a simple four-layer model that makes this concrete: 

1. Data: Unified customer and prospect information 

2. Analytics & AI: Intelligence that processes and interprets that data 

3. Orchestration: Workflows that act on those insights 

4. Experience: The seller and buyer interactions that result 

When these layers are disconnected, AI can’t connect the dots. A prospect’s email engagement doesn’t inform the next call script. Meeting insights don’t automatically update deal risk scores. Sellers end up switching between systems, manually connecting information that should flow seamlessly. 

But when you consolidate around a unified tech stack, something powerful happens. Emails, calls, meetings, and deal data live in one place. AI can see patterns across the entire revenue cycle. Kaia, Outreach’s conversation intelligence platform, doesn’t just capture what happened in a meeting, it understands how that conversation fits into the broader account strategy and suggests next steps accordingly.  

This is where productivity, precision, and predictability converge. Sellers spend less time hunting for context. AI recommendations become more accurate. Revenue forecasts become more reliable because they’re based on complete, connected data. 

Kaia, Conversation Intelligence Platform

"Just Start": Pilot the Next Best Experience

One of the biggest barriers to AI adoption isn’t technical, it’s psychological. Teams get paralyzed by the scope of transformation required. They think they need to overhaul everything at once or wait for the perfect solution. 

Hamish’s advice from SolarWinds: “Just start.” You can’t stop the business to rebuild the stack. Pick one use case, time-box it, define KPIs, and avoid “permanent pilots”. 

The key is choosing the right starting point. Look for workflows where: 

• Data quality is already strong 

• User adoption is likely to be high 

• Business impact can be measured quickly 

• Success can be scaled to other teams 

Maybe it’s automating meeting follow-ups so notes get into the platform immediately, instead of days. Or using AI to score leads based on engagement patterns your best reps already recognize. The specific use case matters less than proving the concept and building momentum.  

Equally important: educate teams on what AI is and isn’t. Set governance so adoption builds trust rather than skepticism. I’ve seen too many pilots fail not because the technology didn’t work, but because teams didn’t understand how to work with the technology.

Customer Spotlight—How SolarWinds Unified for AI-Ready Selling

The Challenge

SolarWinds faced a common problem that gets worse with growth: tool sprawl across acquisitions. Their revenue team was managing enrichment and leading acquisition across duplicate systems. Customer data was split between Marketo, Salesforce and e-commerce platforms. Sellers were system hopping to get basic information about their accounts. 

In the webinar, Hamish explains, “We had really good individual tools, but they weren’t talking to each other in meaningful ways. Our reps were spending more time navigating systems than selling. 

The Approach

Rather than rip-and-replace everything. SolarWinds took a strategic consolidation approach. They centralized core data in Salesforce, expanded automation capabilities, and reduced system-hopping by integrating their most critical workflows.  

A key part of this was re-operationalizing Outreach with close partnership between Sales, RevOps, and IT. Instead of treating it as just another tool in the stack, they positioned it as the hub that connects their other systems and makes AI possible at scale. 

The Results

The results speak for themselves: 

• Higher adoption rates across the revenue team 

• Doubled Kaia licenses as teams saw real value in AI-powered insights 

• Meeting follow-ups completed in minutes instead of a full day 

• More proactive optimization based on real-time data 

• Faster path to new AI use cases as the foundation got stronger 

“Once we had that unified foundation,” Hamish noted, “new AI capabilities became something we could experiment with and scale, rather than one-off projects that never got traction.” 

This connects directly to what we’re seeing with AI agents — when your data and workflows are unified, adding intelligent automation becomes a natural evolution rather than a disruptive overhaul. 

What Teams Overlook: Process, Change Management, and Adoption

Here's a hard truth: tools don't fix broken processes. They amplify whatever processes you already have. 

If your current workflow involves five different systems and three manual handoffs, adding AI to that workflow won't magically make it efficient. You'll just have AI-powered inefficiency. 

Before you think about AI strategy, audit your current processes: 

• Where do sellers lose time switching context between tools? 

• What information gets lost in handoffs between teams? 

• Which manual tasks could be automated if data were more accessible? 

The goal isn't to eliminate all manual work—it's to reduce context switching and enable sellers to focus on the high-value activities that actually drive revenue. 

Change management becomes critical here. Plan for training, identify champions, and make the "what's in it for me" crystal clear. I've seen brilliant AI implementations fail because teams didn't want to change their habits, and I've seen modest consolidation efforts succeed because leaders invested in adoption from day one. 

The most successful deployments involve sellers in the design process. They understand the pain points better than anyone, and they're more likely to embrace solutions they helped create. 

A 30-Day Quick Start to an AI-Ready Stack

Want to get started but not sure where? Here's a practical roadmap: 

Week 1: Inventory tools + seller pain points. Map out your current tech stack and talk to your sellers about what slows them down. Choose one AI-amplified use case that addresses a real frustration—maybe automated meeting notes that sync directly to CRM records, or lead scoring that surfaces the hottest prospects automatically. 

Week 2: Set KPIs, governance, and success criteria. Define what good looks like, both from a business metrics standpoint and a user experience perspective. Select your pilot team and champions—the people who are most likely to embrace change and help others along the way. 

Week 3: Implement + enable. Focus on execution and training. Make sure your pilot team understands not just how to use the new capability, but why it matters and how it fits into their overall workflow. Instrument analytics so you can measure both usage and outcomes. 

Week 4: Review, decide, and scale. Look at the data, talk to users, and make an honest assessment of what's working. If the pilot is successful, create a plan to expand it. If it's not, figure out why and adjust. Use this momentum to remove one redundancy or integrate one key data source—keep building the foundation. 

The key is maintaining momentum while avoiding the temptation to boil the ocean. Each successful pilot builds credibility for the next one. 

Bring It Together: One Platform, Real Outcomes

Let me be direct: consolidation isn't about using fewer tools for the sake of fewer tools. It's about creating the conditions where AI can deliver real productivity gains and give leaders the visibility they need to make better decisions—today, not someday. 

When your data is unified, your workflows are connected, and your AI has complete context, something remarkable happens. Sellers stop fighting their tools and start focusing on what they do best: building relationships and closing deals. Managers get real-time insights into pipeline health and team performance. Leaders can forecast with confidence and adjust strategies quickly when market conditions change. 

This is exactly why we built Outreach as an AI-powered revenue workflow platform that unifies GTM motions, data, and governance. It's not just about having AI features—it's about having AI that understands your entire revenue context and can act on it intelligently. 

The companies that figure this out first will have a massive competitive advantage. The ones that continue to treat AI as a collection of point solutions will fall further behind.