LevelShift is included in The Salesforce Consulting Services Landscape, Q4 2025 by Forrester — recognized for its North America focus and its work across Agentforce, Data 360, and Field Service.
When Forrester named LevelShift in its Salesforce Consulting Services Landscape, it recognized us specifically for Agentforce — one of the three areas (alongside Data 360 and Field Service) where buyers told the analyst they engage us for differentiation.
We were glad to see it. But recognition isn’t the story. What matters is what sits behind it: a point of view on how enterprise AI agents are actually built, governed, and made to pay off.
So rather than restate the report, this piece does something more useful. It opens up the Agentforce work that recognition points to — what separates an agent that ships from one that quietly stalls, and what we’ve learned deploying them inside real enterprise environments.
The demo is easy. Production is the hard part.
Almost anyone can stand up an impressive Agentforce demo. A clean dataset, a friendly question, a tidy answer. The problem is that the demo and the deployment are different sports. In production, the agent meets messy data, ambiguous intent, edge cases, compliance constraints, and users who will abandon it the moment it gets something wrong.
Deploying an agent is not the milestone. Deploying one that works, is trusted, and keeps working is.
Rule #1: Your Agent Is Only as Good as Your Data
This is the single most important thing we tell clients, and it’s why our Agentforce and Data 360 recognitions belong together. An AI agent is a reasoning layer on top of your data. If that data is fragmented across systems, duplicated, ungoverned, or stale, the agent doesn’t fail loudly — it fails plausibly. It gives confident answers that are subtly wrong, which is far more dangerous than no answer at all.
That’s why most stalled AI initiatives don’t actually break at the AI layer. They break at the data layer. Before we deploy an agent, the foundational work is unglamorous and decisive: unify the relevant data in Data 360, resolve identities, establish governance, and make sure the Einstein Trust Layer is masking sensitive information before anything reaches the model. Get this right and the agent has something trustworthy to reason over. Skip it and you’ve automated your data problems.
Build for the workflow, not the wow
The second failure pattern is the agent that’s technically clever but bolted onto nothing. It can answer questions, but it doesn’t live inside the work people actually do, so adoption never happens.
We design agents around a specific workflow and a specific outcome, not around the novelty of the technology. That means starting from the job to be done — resolving a service case, qualifying a lead, getting a field technician the right part — and working backward into what the agent needs to see, decide, and trigger. An agent embedded in a service rep’s console, drafting case summaries and surfacing next steps inside their existing flow, gets used. A standalone chatbot that requires people to leave their workflow to consult it does not.
1 Use Case at a Time: Why We Never Ship AI in Big Bets
Forrester’s guidance to buyers is to favor partners who are outcome-driven and work in increments rather than locking clients into rigid multi-year programs. Nowhere is that more true than with AI agents, where the technology and the pricing are both moving quickly.
The pattern that works is narrow and fast. Pick one high-value use case — say, automating case summarization or giving agents AI-drafted responses. Instrument it so you can measure the result. Prove the value. Then expand to the next use case on a foundation you’ve already validated. This isn’t timidity; it’s risk management. Each phase produces measurable impact before the next begins, and you’re never betting a year of budget on an assumption you haven’t tested.
What good looks like
When the pieces come together — governed data, workflow-native design, incremental delivery — the questions a buyer should be asking get easy to answer with evidence rather than adjectives:
- Did service response and resolution times actually drop? Not “can the agent respond,” but did handle time and case backlog measurably improve.
- Are sellers more productive? Did the agent remove real steps from the selling motion, or just add another tab.
- Did first-time fix rates in the field improve? The hardest, highest-value test — and exactly why Field Service is one of our recognized areas.
- Can you trace every answer? Governance and auditability aren’t paperwork; they’re what lets you scale an agent past a pilot.
Certifications tell you a team understands the platform. Outcomes like these tell you they can execute. That distinction is the whole point.
Why 2026 Raises the Bar for Every Agentforce Deployment
Salesforce’s pricing is shifting, AI is reshaping how delivery happens, and the cost of an over-customized, under-governed environment keeps rising. Agentforce raises the ceiling on what’s possible and the floor on what’s required — it rewards organizations with a trustworthy data foundation and punishes those without one. The enterprises that win this phase won’t be the ones that deployed the most agents. They’ll be the ones whose agents they can actually trust.
That’s what we think our Forrester recognition really represents: not a logo, but a way of working in the areas — AI, data, and service — where this market is heading.
If you’re deploying Agentforce on a data foundation you can trust — or want a second opinion on a stalled initiative — talk to our Salesforce team.
For the wider market view behind this recognition, read our companion piece: What buyers can learn from Forrester’s Salesforce Consulting Services Landscape.
