Methodology
How do you turn AI usage into business lift?
This is SightLift’s methodology. It takes a company from ad hoc, unmeasured AI use to deliberate, data-driven AI use, starting with go-to-market teams.
Manifesto
AI is a system in engineering and a science project for the rest of us.
Late 2022 was the ChatGPT moment. Every company adopted it the same way: a few people tried it, then everyone did.
Engineers picked their tools and then built a system around them, because that is what engineers do: scaffolded data, centralized tooling, standard metrics. Best practice spread on its own.
Go-to-market picked its tools and stopped there. A few citizen builders got very good at AI. The go-to-market organization did not transform.
4 years on, most businesses still cannot measure what AI changes.
The promise of AI – better rep efficiency, faster pipeline, more revenue – still feels like science fiction. Nobody can measure it, though we all plod ahead anyway.
Two teams are helping along the way. RevOps often steps up to own the tools: roadmap, vendor evaluation, and deployment. Enablement, often inside HR, helps humans use AI better. Everyone’s trying their best.
Despite our best efforts, AI adoption in GTM is a slog and it’s not clear where we’re heading. Teams ask for more tools and more training. Vendors deliver more work for RevOps. The skills spreadsheet is six months stale. Citizen builders do their thing, and the rest attend the lunch and learn but don’t meaningfully pull ahead. And, meanwhile, the CEO isn’t going to stop asking about those “AI efficiency gains” any time soon.
Leaders, we see you. There is a better way, and it is the one we know and love when we build our teams: data-driven, outcome-focused, and human-first.
Run AI the way you run the business: on data.
The way out is what we already know how to do: put the data to work, find what works to hit our goals, and do more of it.
It’s a strange trick of the AI boom that business leaders have spent this much time, money, and worry on AI without good data. We would never accept that blindness on lead gen, sales pipeline, or a key customer renewal! We should be able to say which AI work moved a business result we care about.
Once you can, decisions get easier. Sellers learn which skills to adopt. RevOps sees whether a new vendor helps you win. Enablement learns what to teach and who to teach it. The tools improve too, as the fruitful edits fold back into the shared version to lift the whole team.
That is the methodology. Map the usage, join it to outcomes, lift what works, and run it again.
You bring the business questions and the people who own the work. SightLift’s team connects the sources, runs each step with your owners, and writes the readout you take to leadership.
Step one
Map AI usage.
Collect every AI surface into one record, shaped to your own topics, teams, and tools.
How you do it
- Connect each AI surface, read-only, with your IT team.
- Define the topics with the people who own the work, at the level the business actually runs on.
- Enrich each interaction by topic, team, account, tool, and how complex the work was.
- Count the homegrown tools too: the conversations that built them and the model calls they make.
What you get
Who uses what, for which work, across every tool, read as patterns over people: topics, teams, and trends first. A baseline by team, and the power users nobody had on a list.
Where companies are today
Seats bought, usage up. That is the whole dashboard.
Most companies can say what they pay for and roughly how much each tool gets used. Few can say what the work was about, or which team did it.
Step two
Join business outcomes.
Marry AI usage to your systems of record, like your CRM, so each AI effort shows what it changed in the business.
How you do it
- Pick a handful of questions leadership already asks: does the renewal workflow move total contract value, does the outreach agent move pipeline velocity. Not everything, the ones that matter first.
- Join each AI workflow to the metric it touched, in the system that already holds it.
- Compare the teams and periods that used it with the ones that did not.
What you get
Which AI efforts move business results, and which only move activity.
Where companies are today
Cost is visible. Outcomes stop at time saved.
The bill is the one number everyone can produce. After that the story is time saved, self-reported, and leadership has stopped accepting it.
Step three
Lift what works.
Spread the best of what your team invents, power enablement with data, and keep learning as the business and AI change.
How you do it
- Rank the enablement plan by measured business impact, and refresh it as the data moves.
- Spread the workflows that moved a metric. Consolidate the duplicates. Retire what nobody uses.
- Give every shared tool an owner, a version, and a current release, and fold your best users’ edits back into it.
- Run the loop again when new tools arrive.
What you get
A team that improves from its own best work, an enablement program that can show what it changed, and a monthly readout you present to leadership.
Where companies are today
Power users pull ahead. The rest get a lunch and learn.
A few people get three to ten times more effective. Everyone else gets a skills spreadsheet that goes stale, and a prompt on version five while the team runs version one.
SightLift runs each step, with a team behind it.
The methodology stands on its own. The product does the analysis. Our team sits with your owners to define the topics, pick the questions, and write the monthly readout.
Step one · Map
Every chat, agent, and tool call across Gemini, Claude, ChatGPT, and more, sorted by topic and team in words your leaders already use.
AI Use ScoreOne company-wide number for how well your people use AI, compared across teams, tracked to a target, and ready for the board.
Step two · Join
Each AI workflow tied to the business result it moved, compared with the teams that went without, so the return is a number, not a story.
AI CostLive AI spend across every tool, ahead of the invoice, tied to the team or skill that drove it, with the paid seats nobody uses called out.
Step three · Lift
A living plan for who to teach what next, ranked by business impact and refreshed as the data moves, so training goes where it pays off.
Self-Learning SkillsShared tools with an owner and a current version, improved from real usage and vetted by people, so everyone runs the best release.
What people ask before they start.
How long does one loop take?
What do we need to bring?
Is this only for go-to-market?
What data does it need?
Is this employee monitoring?
Where does the AI Use Score fit?
Could we run this ourselves?
Which AI tools does it work with?
Run it on your own data.
SightLift maps your AI usage, joins it to the business questions you already ask, and starts lifting what works.