Forward Deployed Expertise is changing what teams can build
How AI-enabled, multidisciplinary teams build better products faster
The promise of integrated product development is finally within reach
Imagine getting your CIO, CTO, business leaders, product managers, engineers, and designers in the same room to discuss a product. Instead of weeks of working through competing priorities and back-and-forth approvals, the team aligns and begins prototyping in a single day. To most product managers, that sounds like a dream.
Not only is this becoming possible—in many ways, it’s the future of product development we’ve been working toward for decades.
For years, product teams have been trying to break down the barriers between the people who envision products and those who bring them to life. Agile made significant strides, replacing lengthy development cycles and rigid handoffs with more iterative, collaborative ways of working. Yet even as teams moved closer together, specialized tools and workflows continued to enforce silos between disciplines.
Forward Deployed Expertise (FDX) is changing that. With generative AI making technical capabilities more accessible, FDX is creating new opportunities for experts across disciplines to work in a shared environment, building on one another’s ideas and responding to feedback as products take shape, rather than waiting for handoffs that slow everything down.
We’re beginning to see what happens when human expertise and technical execution come together in new ways: better-informed decisions, more compelling products, and a faster path to business value.
Multidisciplinary expertise connects technical speed to business outcomes
This shift is arriving at precisely the moment when almost everything is being treated as a product: applications, internal workflows, AI agents, even individual business processes. The possibilities are expanding, but so is the competition for people’s attention and the pressure to create something genuinely valuable.
With so many possibilities, knowing what to build requires more than technical capability. It requires a clear understanding of the business problem, intended users, and the value it needs to deliver to the business and the people who use it. AI-enabled multidisciplinary collaboration brings those perspectives into the development process earlier, allowing teams to make more informed decisions about what they create and why.
One way companies are bringing expertise closer to the work is through Forward Deployed Engineering (FDE), which embeds technical specialists directly alongside business teams to shorten the distance between an idea and something they can test and react to. AI makes that proximity even more powerful, accelerating development and iteration.
But faster engineering doesn’t necessarily translate into better products or greater adoption. FDX extends that same principle across disciplines. By embedding AI-enabled product leaders and experience designers alongside engineers, teams can apply their distinct expertise to the same evolving solution, with business and human considerations informing technical decisions throughout development.
The distinction goes beyond team composition. In a traditional process, a team might spend weeks researching a request before returning with wireframes for review. With FDX, AI-enabled tools allow specialists to work from shared business and technical context from the start, clarifying the problem, then returning in days with working code shaped around the client’s existing architecture. Product decisions shape engineering work in real time, and technical discoveries feed back into the experience as it evolves.
“That distinction—between accelerating engineering outcomes and accelerating human and business outcomes—is at the heart of what makes forward deployed expertise so powerful,” explains Tom Dahl, senior director of product design at Slalom.
As AI takes on more of the implementation work, teams can devote greater attention to what happens on either side of the build: defining the right problem upfront and evaluating whether the resulting product delivers on its promise.
From faster development to better product decisions
The difference becomes concrete in practice.
At one wealth management firm, financial advisors spent up to 90 minutes preparing for each client meeting, manually gathering information from disconnected systems. Working with Slalom, the firm developed an AI-powered preparation assistant that brings together portfolio data and relevant research into a ready-to-use client briefing. As a result, preparation time dropped from an hour and a half to approximately five minutes per meeting. Rather than spending that time gathering data, advisors could focus on what they do best: understanding their clients’ situations more deeply and making more informed recommendations.
Behind a solution like this is more than technical execution. Product expertise helps define the underlying business problem: giving advisors more time for substantive client interactions while improving the quality of their insights. Experience designers consider how to present complex information in ways that support advisors’ decisions. Engineers develop specialized AI agents to analyze different dimensions of a client’s portfolio and synthesize those findings into a briefing aligned with how advisors work.
On the surface, this sounds like what product teams have always done. What changes is how quickly those perspectives can build on one another. Rather than waiting for feedback at predetermined milestones, specialists can evaluate and refine the same working product together. And because changes can be tested almost immediately, the backlog itself becomes more fluid. A new insight might prompt the team to rethink a feature entirely or redirect investment toward something more valuable.
This gives human expertise even greater influence throughout development. AI can generate functional solutions quickly, but knowing which ideas deserve to move forward, what makes an experience compelling, and when a product is ready for users still requires human judgment.
As Peterson puts it, “You still need human craft, taste, judgment—all of those things.”
That judgment also extends to what happens after the prototype. By accounting for existing architecture and operational requirements from the outset, teams can build with a path to production from day one. Once a solution is in use, adoption data and business performance provide new evidence to evaluate whether it’s delivering the expected return and guide what gets improved next. The same feedback-driven approach that informs the initial product continues to shape its evolution.
Extending AI-enabled collaboration beyond the product team
One of the less obvious benefits of working this way is what leaders take away from the experience. As Peterson puts it, they get to see “the art of the possible”—a firsthand view of how an AI-enabled, multidisciplinary team actually operates. This can be a powerful catalyst for reimagining how their own teams could work.
At one major retailer, an already AI-sophisticated merchandising team brought in Slalom’s FDX team to accelerate a specific use case. The engagement offered an opportunity to demonstrate new AI-enabled ways of working, with potential applications across other business units. The trickier challenge was that the solution itself touched nine different groups, all equally important to its success, with no single stakeholder empowered to validate whether it met their collective needs.
Seeing a different way of working is one thing. Extending it beyond the product team requires leaders to clarify who owns a solution across functions and who can make decisions when priorities compete. Forward Deployed Expertise helps surface those dependencies, bring the right stakeholders into the work, and model shared practices that teams can carry into future products.
Four shifts for product and technology leaders
At Slalom, we’ve long believed that business viability, technical feasibility, and human desirability should inform product development together. AI is creating new opportunities to put that principle into practice, building on decades of progress in how teams collaborate, turn decisions into working solutions, and bring products to market.
For product and technology leaders, four shifts can help translate that opportunity into measurable business value.
- Rethink where teams invest their time. As AI makes it faster to build working software, teams can rethink how they spend their time across the product lifecycle. Rather than filling the time saved with more development, reinvest that capacity in deeper problem exploration, more rigorous evaluation of business outcomes, and a clearer accounting of whether the investment paid off. The goal is better product decisions, not just more features delivered.
- Connect expertise through shared context. Give product, design, and engineering specialists access to the same evolving business and technical context. AI-enabled tools can connect product priorities, user personas, experience maps, design assets, release horizons, and existing codebases, allowing experts to contribute directly to the work as it develops. The aim is to preserve the depth of each discipline while reducing the friction between them.
- Establish decision rights before they become a constraint. Identify who can validate business outcomes and make decisions on behalf of affected stakeholders, particularly when a solution spans multiple business units. Bring those stakeholders into feedback cycles early so decision-making can keep pace with rapid iteration.
- Design for production and continue validating value. Build early prototypes with existing architecture and security requirements in mind, including the operational considerations that will determine whether a solution can scale. Once deployed, use adoption data, performance signals, and ROI checkpoints to guide ongoing refinement. Reaching production is an important milestone, but value continues to be realized and measured afterward.
AI gives multidisciplinary teams the means to realize a longstanding promise: the right people, working closely together, can build better products faster. When product strategy, design, and engineering share the same context and working environment, the distance between a good idea and a working solution shrinks, leaving more room for the judgment, creativity, and collaboration that make products worth building.
Ready to see what your team could build together?
Contributors
AI makes it easier for specialists to contribute directly to the same evolving product without mastering every technical tool. With access to shared business and technical context, product strategists, designers, and engineers can test ideas against working software and build on one another’s contributions in real time. This creates more continuous feedback loops, allowing product decisions and technical implementation to evolve together.
Both approaches help bring technical capabilities closer to business needs. Forward deployed engineering embeds technical specialists alongside business teams to accelerate development and implementation. Forward deployed expertise extends that model across disciplines, bringing product strategy and experience design together with engineering. By integrating business viability, technical feasibility, and human desirability throughout development, FDX helps business and technology leaders align on which solutions are worth pursuing before significant investment is made.
The choice starts with what the product needs to do, who it serves, and the environment it needs to work in. Teams can build on capabilities already in your technology stack or consider other platforms if they’re a better fit. Slalom’s depth across partner platforms, flexibility across vendors, and partner investment in our people’s expertise bring current, hands-on knowledge to those decisions.
Development speed is one measure, but it doesn’t establish whether a product delivers value. Teams can measure outcomes such as time saved, revenue generated, user adoption, service quality, and operational costs against the business problem they’re solving. After deployment, user feedback, performance data, and ROI checkpoints help inform continued investment and refinement.
Yes. Forward deployed teams can embed within existing business and technology environments rather than operate as separate delivery teams. Depending on the engagement, specialists can help prioritize opportunities, develop working prototypes, collaborate with internal engineering teams, and support the path to production. Client teams also gain firsthand exposure to AI-enabled, multidisciplinary ways of working.
