Generative AI Archives

Signals from the Edge: SEO Isn’t Dead. It’s Becoming Findability in the Age of AI Buying (and AI Buyers)

As 2026 gets underway, a new GTM pressure is showing up in tech marketing conversations: buyers are moving from “searching” to “asking.” Instead of ten tabs and a spreadsheet, they’re getting a shortlist from an AI answer engine and treating it like a first draft of the market.

This transition does not eliminate SEO; instead, it transforms its function within the field.

In classic SEO, you fought to rank a page. In AI-mediated discovery, you’re fighting to be selected as a source and named in the answer. The new discipline is about findability: the probability your brand appears (and appears correctly) when a buyer (or their AI) asks.

Where enterprise tech marketers stand today

In client work, strategy sessions, and early pilots, we’re hearing the same set of questions surface:

“How do we show up in AI-generated shortlists, not just Google results?”
“How do we measure whether our category presence is improving?”
“What content actually influences what AI systems cite and repeat?”

These discussions are quickly evolving into strategic priorities for marketing teams as AI’s influence over the buyer journey intensifies.

The buying committee is bigger (and AI agents just joined)

Committee buying has been the reality in enterprise tech for years. What’s changed is the scale and who participates.

Forrester’s buyer research puts the average buying group at 13 people, with 89% of purchases involving two or more departments. Gartner cites up to 16 people across as many as four functions. That’s not a messaging problem you can solve with a single audience-agnostic thought leadership piece. It’s a consensus problem across security, IT, finance, procurement, and functional owners — each with different risk tolerances and success criteria.

Now add a new entrant: AI agents.

Forrester reports that almost 95% of buyers anticipate using generative AI to support their decision and purchase process. McKinsey is already describing “procurement agents” as systems that ingest context, plan work, suggest options, and act autonomously. This accelerates the shift toward a hybrid workforce where procurement professionals collaborate with digital coworkers.

The implication for B2B tech marketers is that the buying committee is becoming part human, part machine.

2026 is the year of dual-track content

Most teams already tailor content by persona. The new layer is tailoring content by consumer type:

Humans reward narrative, clarity, visual proof, and interactive experience.
Machines (LLMs/agents) reward structure, consistency, explicitness, and retrievability.

This is the pivot. The same truth needs to ship in two optimized forms.

Track A: Human-optimized content

This is where you win attention, belief, and internal alignment.

If you’re marketing an enterprise SaaS platform (say, a FinOps + governance solution), human-track assets increasingly need to do more than “explain.” They need to help stakeholders decide:

Interactive ROI/TCO tools and budget planners
Visually dynamic demos (journey maps, workflows, architecture explainers)
Outcome-led case studies that show before and after states with constraints and tradeoffs
Buying committee enablement modules (security briefs, implementation plans, procurement-friendly packaging)

Track B: Machine-optimized content

This is where you win shortlist inclusion and reduce mis-positioning when buyers rely on AI summaries.

Machine-track deliverables look different:

Markdown “factsheets” for each solution and industry (what it is / who it’s for / key differentiators / proof)
FAQ content that answers shortlist questions directly
Schema-based markup (e.g., Organization, Product/SoftwareApplication, FAQPage, HowTo)
A canonical “claims library” so your positioning stays consistent across web surfaces

The game plan is to make it easy for AI systems to retrieve, cite, and repeat the right story.

Findability is now a buying-committee deliverable

When buyers use genAI to shortlist vendors, your discoverability becomes upstream of your funnel. And because committees are large, different stakeholders (and their agents) are each asking different questions:

Security asks: “Which vendors meet SOC2/ISO requirements and support private deployment?”
Finance asks: “Which solutions reduce spend fastest, and what’s the payback period?”
IT asks: “Which platforms integrate with our stack and have proven implementation playbooks?”
Procurement’s agent asks: “Compare vendors across contract terms, support SLAs, and compliance posture.”

Dual-track content lets you meet those questions in two ways:

a compelling human experience that builds preference, and
a structured machine-readable layer that ensures you show up and show up correctly.

A practical measurement shift from rankings to “Share of Answers”

If findability is real, it has to be measurable.

We’re seeing leading teams treat AI discovery like a new channel, with its own equivalent of share-of-voice: Share of Answers.

A simple operating model works:

Build a stable “prompt pack” across buyer intent:
- category discovery, shortlist intent, comparisons, proof-seeking
Score responses consistently:
- mentioned (Y/N), tier (Top 3/5/10), positioning accuracy, evidence quality, link/citation quality
Ship controlled interventions:
- update one cluster (owned, earned, or explanatory) at a time
Re-score monthly and track movement

This demonstrates how findability is anchored in data-driven insights rather than being merely an intuitive process.

The three surfaces that shape what AI systems retrieve

Across AI-sourced recommendation sets, we consistently see retrieval fall into three content classes:

Owned (“things that look like answers”)
Your solution pages, use cases, integrations, customer stories, security/IT documentation.
Earned (“things that rank answers”)
Directories, review platforms, partner ecosystems, credible roundups.
Explanatory (“things that explain the topic”)
Category guides, decision frameworks, glossaries, and thought leadership.

Winning findability means designing all three as a system.

Intercept Labs: co-investing in what’s next

This shift is happening faster than most playbooks can keep up with. That’s why our approach through Intercept Labs is built around co-investment: prototyping with clients, running controlled pilots, and turning what works into repeatable modules.

In 2026, one of the most active areas of collaboration is GEO and findability, building systems that help brands win in both human and machine discovery.

We’re currently piloting a modular program that can be adopted à la carte or through a managed service desk:

GEO monitoring and alerting (prompt-pack tracking across key surfaces)
Scoring and “Share of Answers” reporting (baseline, trend, and intervention impact)
Recommendations and roadmap (owned/earned/explanatory priorities)
Content development and implementation (interactive human assets and markdown/schema machine assets)
Governance (claims library, consistency checks, brand-safe structured publishing)

It’s a natural extension of what strong tech marketing teams already do. Build proof, structure it, distribute it, measure it, and iterate.

Four signals to watch

The committee is now two audiences.
Buying decisions still happen through people, but discovery is increasingly mediated by machines. Winning means designing persuasive experiences for humans and structured, retrievable truth for LLMs.
Shortlists are being formed upstream of your funnel.
More buyers are arriving with a pre-shaped point of view, which is often a vendor set and evaluation criteria they didn’t assemble manually. The fight for consideration starts before the first click.
Agents are compressing the research cycle.
As AI assistants move from “answering” to “doing” (summarizing, comparing, extracting requirements), the window to influence narrows. Brands that package proof in reusable modules will travel further in less time.
Content strategy becomes a systems problem.
It’s no longer “make a campaign.” It’s “ship a content system” involving interactive assets that earn attention and machine-readable formats that earn retrieval, governed by a single claims layer so the story stays consistent everywhere.

Key takeaway: The teams that lead in 2026 will treat findability like a product. Instrument it, run controlled interventions, and iterate monthly until “how buyers discover us” is as measurable as “how buyers convert.”

Ready to explore?

If you’re building a 2026 content system that serves humans and machines — and you want a measurable program for discoverability, Share of Answers, and controlled GEO interventions — Intercept Labs is actively collaborating with teams in pilot phase.

About Signals from the Edge

Signals from the Edge is Intercept’s executive insight series, designed for marketing leaders inside global technology organizations, tracking practical shifts at the intersection of AI, audience behavior, and GTM execution.

What if your customer is no longer human?

I just returned from MAICON, the Marketing AI Conference.

Jeremiah Owyang, General Partner at Blitzscaling Ventures, shared a simple but seismic idea: marketing is shifting from business-to-business (B2B) or business-to-consumer (B2C) to B2A… business to agent.

It resonated with me.

Because it connects directly to a conversation we’ve been having at Intercept about what’s being called the Dead Internet. The term sounds dramatic, but it captures today’s reality. The internet, as we knew it, is no longer predominantly human.

According to Imperva’s 2025 Bad Bot Report, automated traffic now accounts for 51% of all web activity. Within that, 37% is classified as malicious or manipulative. The web we create content for (our ads, websites, and campaigns) is now more machine-read than human-read.

The Agentic Layer

AI agents are here.

They’re not just chatbots or simple automation tools. They are autonomous systems that can perceive their environment, make decisions, and act to achieve specific goals, often learning and adapting over time.

Agents access and add to memory. They self-determine a plan of action, verify their steps, complete complex tasks, and continuously improve. In simpler terms, they don’t just follow instructions; they decide how to accomplish them.

Some will work directly with people. These are primary agents that act as personal or enterprise-facing assistants. Others will operate in the background. Execution agents that carry out delegated tasks such as research, procurement, or reporting.

Together, they create what I call the Agentic Layer. This is a new interface where non-human actors drive discovery, evaluation, and decision-making long before a person ever sees your message.

If that sounds futuristic, it’s already here. A Salesforce-commissioned IDC survey found that adoption of agentic AI is expected to grow 327% by 2027 as organizations experiment with autonomous workflows. And while projections vary, most analysts agree a significant share of job roles will change as enterprises deploy agents across marketing, operations, and procurement.

From Journeys to Jobs

For decades, marketing has been organized around the funnel. A sequence that moves humans through awareness, consideration, and decision. It assumes time, emotion, and persuasion.

Agents don’t move that way. They don’t browse or deliberate. They execute jobs. And their process is logical, not emotional: find, compare, decide, justify, transact.

AI agents are getting smarter. Over time, primary agents will learn the full context of the people they serve by reading their emails, scanning their calendars, mapping the people they meet with, and understanding the industries they operate in. They’ll track headwinds, budget cycles, and project timelines. When they detect a potential need, they’ll proactively search for solutions before their human ever types a query.

Once they’ve found potential vendors, these agents will go a step further to recompose the content they collect into the format each person prefers. Audio for auditory learners. Visuals for the spatial thinkers. Text or summaries for those who want speed. They’ll translate, condense, and sequence your content into the medium and tone their user responds to best.

That shift sounds subtle, but it comes with massive implications. Humans respond to narrative. Agents respond to clarity. So if the funnel was built for persuasion, the agentic world is built for precision.

In practice, that means your content can’t just be well-written. It has to be machine-optimized.

From B2B to B2B2A

This is where Jeremiah’s idea becomes real. The next era of marketing won’t be purely business-to-business. It will be business-to-business-to-agent.

Your buyer’s first line of research is no longer a search engine or a colleague. It’s their agent. And that agent doesn’t care about creative metaphors or brand voice. It cares about truth, format, and data quality.

At Intercept, we’ve been mapping what dual-track content models could look like. One track continues to tell the human story. The narrative that builds trust and emotion. The second track is designed for the machines that now mediate discovery – clear, structured information that agents can parse, score, and recommend.

This is where Generative Engine Optimization (GEO) replaces traditional SEO. Instead of optimizing for search rankings, GEO ensures your content is readable by large language models and retrieval systems, which have become the new gatekeepers of attention.

The questions shift from “Does this headline grab interest?” to “Can an agent extract this fact correctly?”

Brand as Training Data

If agents learn from the public internet, your brand is only as strong as the data trail you leave behind.

Every press release, product description, case study, and help article becomes training data. Together, they define how you’re represented inside the models that buyers and their agents rely on.

Your share of voice becomes your share of the model.

That requires a new kind of discipline to treat every piece of content as both communication and contribution. The marketers who understand this will stop chasing reach and start building semantic credibility.

Because when agents go searching, they won’t look for stories. They’ll look for the most consistent, verifiable source. And if your content contradicts itself or hides your value behind vague adjectives, you’ll simply be filtered out.

The Human Upside

All of this sounds mechanical, but it actually opens the door for something deeply human.

If agents handle the mechanical work, like searching, validating, and transacting, then marketers can refocus on what only humans can do. To me, that means making ideas clear, designing experiences, and telling stories that still move people once the machines have done their jobs.

Bottom line, the Dead Internet isn’t the end of creativity.

But in this new era, clarity becomes the strongest currency of persuasion. The next generation of marketing will be built on transparency, structure, and truth.

And it will reward the brands that are easiest to understand… by people and by their agents.

Signals from the Edge: Inside the Acceleration of Enterprise AI Marketing

Enterprise tech marketers are moving faster than many expected. Rather than waiting for packaged playbooks, global technology brands are co-creating, piloting, and testing AI marketing workflows with us in real time.

At Intercept, this has reshaped how we think about adoption. When we launched Intercept Scale, our thesis was that mid-market clients would be the proving ground for AI in marketing, with faster cycles and more room to experiment. What we did not anticipate was how quickly global enterprise leaders would close that gap. Across North America, Europe, and Asia-Pacific, it is enterprise teams who are now shaping the playbook with us, testing and learning in real campaigns at scale.

Where enterprise marketers stand today

In nearly every major account Intercept supports, at least one AI-powered proof of concept or pilot is underway. These are not theoretical exercises. They are live tests tied to real campaigns that reflect enterprise-scale marketing challenges.

Statement-of-work structures and compliance reviews still matter, yet teams are finding paths to navigate new. We are seeing champions emerge within product, field, and alliance marketing teams who are leading focused tests that balance innovation with governance.

From pilots to practice: what we see on the ground

Across the portfolio, we are testing workflows that were once too costly or slow to attempt in a traditional human-centric approach:

AI avatars for video to expedite turnaround and update product messaging on the fly, including the added benefit of localizing quickly.
AI-powered research services to access a wider spectrum of sources and surface insights faster than human-only teams can.
Purpose-built AI agents to generate content variants at scale.
Buying-committee mapping that aligns proof points to each role in the decision.
AI-supported outbound tele programs that warm dormant lists and then hand off to account leads for human engagement.
AI-enabled Sales Dojo pilots that use inbound scenarios to test messaging, surface objections, and strengthen seller training.

These pilots sit alongside core campaign activity and are beginning to influence how workflows are designed.

Appetite for experimentation

Yes, risks are real. Infrastructure approvals, data governance, and legal oversight are part of every initiative. Even with that, the appetite to experiment is strong. Pioneers inside enterprise teams are willing to start small, measure carefully, and then scale what works.

Creative testing comes into focus

A clear signal in enterprise marketing is the shift toward AI-powered creative testing. In the past, creative choices often relied on opinion, focus groups, or long feedback cycles. The tension was always the same: campaigns had to move quickly, but untested creative risked wasted spend.

Intercept Cortex was built to close that gap. By combining AI with neuroscience, Cortex predicts audience response across four dimensions: attention, emotion, cognition, and memory. This allows marketers to validate concepts before launch rather than waiting on post-campaign data or subjective reviews.

The outcome is not the replacement of human judgment, but its reinforcement. Strategists still shape the story, while Cortex provides predictive evidence that supports creative decisions, guides prioritization, and reduces effort spent on visuals less likely to succeed.

We share the full model and use cases in our new eBook: The End of Creative Guesswork

Why this matters for marketing

AI in enterprise marketing is ultimately about better ways of working.

Content that was previously too expensive to scale can now be produced and versioned.
Research cycles that took weeks can be completed in hours.
Campaign ideas that felt out of reach can run as measured pilots.
Creative can be validated before launch, which saves time and investment.

We are still early. Some initiatives will stall and others will scale. Enterprise marketers are no longer waiting for a finished guide. They are building it in collaboration with partners who can move with them.

Four signals to watch

Across our enterprise client work, several patterns are beginning to stand out. These signals show how AI adoption in marketing is evolving from isolated pilots to a more systematic shift:

Pilots are moving into live workflows. What started as contained tests is now shaping day-to-day campaign execution, with learnings feeding directly into global programs.
Enterprise champions are leaning in. Leaders inside product, field, and alliance teams are taking ownership of AI pilots, willing to test new ideas and prove impact to their organizations.
Creative validation is becoming the norm. Tools like Intercept Cortex are setting new expectations for how campaign assets are tested, shifting validation from post-launch analytics to pre-launch prediction.
Governance is a foundation, not an afterthought. Security, compliance, and brand safety are embedded in adoption conversations from the start, making trust and transparency key differentiators for agencies.

Innovation as shared commitment

For teams beginning their AI journey, Intercept provides structured workflows that reduce risk and build confidence. For leaders on the edge, we explore new territory through Intercept Labs, our internal innovation center. Labs is where we prototype AI-powered marketing solutions and run controlled pilots in partnership with clients who want to test the next wave of possibilities.

This model reflects our belief in co-investment. By sharing the cost, risk, and learning curve, we can help enterprise marketers accelerate adoption while ensuring the ideas that scale are grounded in real-world impact. The result is not theory, but working models that move from prototype to practice in the field.

Ready to explore?

If you are assessing AI for research, creative testing, or campaign acceleration, let’s connect.

Signals from the Edge

How AI is reshaping B2B tech marketing

Enterprise tech marketers are in the midst of evolution. Generative AI has moved beyond experimentation to revise standards for building campaigns, distributing assets, and measuring impact. Leading organizations aren’t only adopting. They’re reconfiguring.

At Intercept, we see this shift firsthand across our work with global product, field, and alliance marketers. Pressure to personalize at scale grows. Campaign timelines shrink. Internal stakeholders are wondering about AI speed, quality, and security of production.

Where enterprise marketers stand today

In discovery work, campaign strategy sessions, and innovation pilots, we’ve observed several recurring questions from our client stakeholders:

How do we integrate AI into our workflow while protecting our brand and voice?
What guardrails should exist between AI acceleration and human creativity?
Can we quantify the efficiency AI introduces without overstating its ROI?

Many of these conversations tie directly to budget and scope decisions. Clients no longer ask if AI belongs in the process. They ask how to operationalize it without friction or risk.

From one-off pilots to platform thinking

What began in isolated GPT-assisted tasks is quickly organizing into a fully fledged, AI-shaped campaign system. At Intercept, our process begins with signal scanning driven by AI-powered research. Watchtower is our proprietary research platform using AI to analyze unprompted digital conversations across social, search, and thought leadership channels. It surfaces real-time insight into how enterprise buyers talk, share, and engage each other.

These insights enable more informed campaign design. They help us shape the bill of materials, identify the buyer triggers, and pressure-test our positioning with AI-modeled persona panels. From there, we create content that better targets our audience, within markdown-structured formats optimized for AI ingestion. The result is a library of assets easily indexable for quick access, customization and re-deployment as demand dictates. Want to go deeper? Download our eBook on AI-Powered Research to explore how Watchtower and generative tools are transforming insight development.

AI-ready content is becoming the new default

As enterprise teams build internal AI agents, they rethink how to structure content. Intercept now designs campaign assets pre-formatted for ease of implementation. Markdown syntax, metadata tagging, and modular structuring aren’t bells and whistles; they’re our table stakes. You can think of these as SEO for internal AI tools, classifying and sorting content assets to become instantly searchable, retrievable, interchangeable.

Trust, compliance, and governance are essential

While enterprise clients formalize AI guidelines, procurement teams tighten their expectations of vendors. Legal, compliance, and IT stakeholders are increasingly part of the conversation. More than ever, marketing partners are evaluated based on how they responsibly manage AI workflows, handle data, and maintain tooling transparency.

At Intercept, we’ve adopted a closed-tenant model for AI-powered workstreams and established an internal AI Task Force to lead responsible implementation. Our cross-functional team tests against agency-specific criteria to guide pilot rollout for our teams.

Intercept’s governance protocols match the expectations of our enterprise clients to ensure our AI programs align with their standards for data integrity, brand safety, and IP protection. These include scenario-specific risk assessments, safe experimentation boundaries, and staged change management processes to onboard new tools.

The buying committee has shifted — have your campaigns?

Dynamics of influence inside enterprise tech organizations are reorienting fast. Gen Z and Millennial stakeholders are embedded into buying committees with real sway over vendor selection, content evaluation, and campaign credibility. Their expectations were defined by consumer-grade digital experiences as fast, personalized, and contextually relevant.

Through our innovation center Intercept Labs we’re helping enterprise marketers meet the changing of the guard head-on. Our prototypes incorporate persona signal scanning and pre-launch message simulation to reflect how today’s buyers think, share, and decide. For clients this means better campaign perception and alignment with real-world dynamics.

What’s next: AI in the system as much as the output

Intercept’s approach to innovation is operational, not theoretical. We proactively offer AI-powered alternatives to traditional workflows, helping clients explore safe, smart, and scalable delivery models. Imagine campaigns adjusting to automated signals, seller feedback, or audience drop-off. Consider content engines that generate personalized variation across industry and role. Our philosophy is rooted in co-investment. In many cases, we test and build in partnership with clients who want to lead in their category.

Four signals to watch

From pilots to platforms: AI must be built into your workflow, not layered on top.
Governance as value driver: Trust, transparency, and security are now differentiators.
Dynamic content ecosystems: Campaigns and modular assets can adapt in real time to input signals.
Audience simulation pre-launch: Synthetic buyers can validate creative before it ships.

Innovation as shared commitment

Clients early in their AI journeys benefit from our proven, bespoke workflows. For those on the leading edge, we co-invest in novel solutions, prototyping before proving viability. These dual tracks let us investigate what’s next without compromising what works today.

Ready to change?

If you’re exploring structured ways to embed AI into your marketing workflows, enablement assets, or research infrastructure, let’s talk. We offer AI use case-mapping workshops and capabilities briefings tailored to product, field, and alliance teams, from pilot to platform.

For a deeper look at adoption patterns, emerging tools, and internal barriers across the tech marketing ecosystem, download our latest Q2 Trends Brief: AI in B2B Marketing.

About Signals from the Edge

Signals from the Edge is Intercept’s executive insight series, designed for marketing leaders inside global technology organizations. Each edition captures practical implications at the intersection of AI, audience behavior, and go-to-market execution.

As a specialist agency serving enterprise tech brands, Intercept brings a unique vantage: we work with product, field, and alliance partner teams on modular content ecosystems to deliver programs at global scale. Signals from the Edge is our dispatch from the frontlines.

How to Turn a Hero Asset into a High-Performance Campaign in 9 Clicks

Every B2B marketing team knows what it takes to create “hero assets.” After pouring weeks into perfecting a white paper, it gets its moment in the spotlight on social, and then what? It’s filed away with other assets that never get the opportunity to perform.

It can feel like you’ve run a marathon only to head back to the starting line to do it again. This content creation cycle can seem relentless, especially given today’s fast-paced market and the growing content appetites of B2B buyers.

There’s got to be a better way

This is where Jasper steps in with its handy feature called Campaigns. Imagine being able to transform your hard-earned hero asset into a full-fledged campaign with just a few clicks. It’s possible today.

Here’s a quick look at how we use it. One of our research studies is called Nology, which surveys 500 technology buyers on their digital transformation maturity, buying needs, and B2B marketing preferences. After producing a hero white paper, we felt it had more potential and turned to Jasper for some extra content production support.

Creating a campaign in 9 clicks

We started by naming our campaign, selecting a voice, and uploading our hero asset.

We then chose the type of content we were looking to produce to support our digital campaign and hit “Generate Campaign.”

While it’s not magic, it does take the edge off starting from square one. One of our copywriters was able to take the initial outputs and refine them to ensure they were accurate, engaging, and on-brand.

The Impact: More Than Just Time Saved

Embracing Jasper has led to several impressive results:

Efficiency: Speedy draft generation means we can take on more without our teams feeling swamped.
Quality: Letting AI handle the heavy-lifting means our team can focus on finessing the content, adding that essential human touch.
Empowerment: With the bulk of content generation sorted, team members can expand their horizons and tackle new projects, leading to professional growth and job satisfaction.
Focus: Our team can zero in on their core roles, reducing time spent on repetitive tasks.
Streamlined Briefing: Jasper drastically cuts down the time spent wrangling multiple case studies and makes onboarding new team members a breeze.

AI as an Enabler of Human Creativity

Rather than replacing human jobs, AI tools like Jasper play a crucial role in the workplace by enabling professionals to focus on more creative and strategic tasks.

In the context of writing and producing content, this means less time spent on drafting and more on refining to ensure the final output is high quality and on-time.

The Future of AI-Driven Content Creation

As AI continues to evolve, businesses at the enterprise and mid-market levels will further realize its integral role in content creation and management and as a tool for innovation.

For companies looking to scale up their content production without compromising quality, feature-rich solutions like Jasper are a game-changer. They bring consistency, efficiency, and a certain level of creativity that, when combined with human oversight, can consistently produce exceptional results.

Looking to leverage Gen AI in your B2B marketing operations? Get in touch for more insights.

Jasper Names Intercept a World-Class AI Marketing Agency Partner

“Now is not the time to wait and see. ‘Marketing’ has been given a new superpower. The question is, what will I do with it?”
Andrew Au, Managing Partner at Intercept

Jasper recently announced their list of world-class marketing agencies for their AI Solutions Partner Program, and we’re proud to say Intercept is among the global top 20.

To earn this accolade, we focused on B2B marketing in the global tech sector. Our deep understanding of generative AI (gen-AI) and its transformative potential set us apart.

Becoming a recognized leader in gen-AI marketing

Our AI journey started in 2017 with white papers and eBooks that explored possibilities for Big Tech clients. As early adopters, we leveraged AI tools internally for content creation and testing. Microsoft’s investment in Open AI validated our path, spurring further exploration.

By early 2023, we expanded into other gen-AI platforms like ChatGPT, DALL·E, and Midjourney, focusing on textual and text-to-image capabilities. Our comprehensive research evaluated tool functionality alongside data privacy and security. This led to a curated suite of tools, each chosen for their unique strengths, from infographic design to analytics insights.

In this mix, Jasper became a central tool in our approach to marketing, excelling in several areas:

Output consistency
Data privacy
Content control
Mitigating hallucinations
Maintaining a consistent knowledge base

Tailored Jasper solutions for every need

We understand that every business is at a different stage in adopting and leveraging gen-AI technology. Whether you’re looking to take full control in-house or prefer a fully managed option, we offer a spectrum of Jasper-related services.

Bring Jasper in-house: We provide comprehensive guidance to jumpstart your Jasper journey, specializing in training models, building knowledge bases, defining brand voice, and optimizing naming and tags. This package includes all the guidance and support your marketing team needs to access Jasper’s capabilities with confidence.

Gain hands-on control with expert support: This offering combines the automation power of Jasper with our agency’s expertise. We help build your in-house capabilities by providing comprehensive training, strategic guidance, and tailored copywriting support.

Stay hands-off and focused on strategic priorities: In this full-service offering, we handle everything – from turnkey license management and setup configurations to campaign strategy and content production. You’ll gain the peace of mind that comes from having an award-winning B2B agency managing your marketing needs, ensuring efficient and impactful results.

Embracing the gen-AI revolution

“The new program is purpose-built to incorporate AI functionality into marketing workflows, removing the barrier to experimentation and disrupting the traditional hours-based model with a move towards value-based pricing.”
Al Biedrzycki, Director of the Solutions Partner Program at Jasper

If your organization is not keeping pace with AI, then it’s falling behind. This isn’t about job loss; it’s about job evolution. Using gen-AI purely as a content factory is not enough. The real opportunity lies in a strategic, creative, and critical approach.

The landscape of outsourced services is changing too. As traditional hourly billing makes way for value-based pricing, a shift in mindset becomes vital for future partnerships.

Navigating data security, ethics, and bias

“People often think of humans as unbiased and rational, whereas machines are seen as more biased. But in reality, human bias is harder to spot.”
Andrew Au

Unconscious bias is part of human nature, whereas gen-AI biases are more identifiable and correctable. Legal complexities in copyright laws present challenges, but major tech companies are stepping in with legal support. At Intercept, we navigate these intricacies with care, ensuring our clients are well-informed about content ownership.

Transparency in AI use is non-negotiable. We have established AI policies, and our contracts clearly outline data confidentiality and proprietary information. We chose Jasper partly for its robust data privacy and security, aligning with our operational framework.

By adhering to ethical guidelines and security protocols, we offer our clients a stable and reliable gen-AI experience.

AI is a co-pilot, not an autopilot

Relying on AI as a form of autopilot is a sure way to crash. It excels in supporting, but not replacing, human reasoning. Strategic planning, critical thinking, and creativity should remain in human hands.

Human oversight is also crucial for course corrections and model refinement. A meticulous human-led review process ensures alignment with strategic goals, brand identity, and client expectations

The near and long-term impact of gen-AI

Currently, gen-AI shines in extracting key details from complex documents. In the short term, it’s set to boost transactional communications, like automated webinar invites and thank you messages.

Long term, we see immense potential in text-to-image platforms. The current challenge is producing on-brand content efficiently. Our experience involved generating 400 AI images to find a starting point. These platforms are promising but still evolving.

Platforms like Jasper are leading the gen-AI revolution – disrupting traditional methods of campaign setup, content analysis, and optimization.

If you’re looking to harness gen-AI for growth and are wondering where to start, let’s have a conversation.

To view some of our award-winning campaigns with enterprise clients, check out our case studies page.