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Todd VanderStelt is the Co-founder of MixShift, a firm that helps Amazon agencies, aggregators, and brands scale operations and reporting for their Amazon accounts. He is also the Managing Partner at Dash Applications, which delivers software to improve operations and sales for retailers and brands, and the Founder of TidalBore Group, a management consulting firm specializing in retail. Previously, Todd held leadership roles at Amazon, where he built forecasting tools and operational systems.

Sam Hager is the Co-founder and CEO of MixShift. He began his career in the Amazon space, managing multimillion-dollar business books across advertising and agency services. Before MixShift, Sam was the Marketplace Strategist and Amazon Advertising Manager at Booyah Advertising.

Here’s a glimpse of what you’ll learn:

[1:15] How Todd VanderStelt and Sam Hager met and founded MixShift
[3:45] Todd shares his forecasting background at Amazon and the origins of MixShift’s top-down model
[7:43] How MixShift connects ad spend and revenue to guide forecasting decisions
[10:30] The three key drivers of MixShift’s forecasting model: ad spend, seasonality, and organic trends
[14:25] How historical data shapes forecasts and reveals insights from unexpected sales spikes
[19:05] Integrating MixShift’s forecasting tools into inventory planning
[22:30] Future developments for unit-level forecasting and customer acquisition modeling at MixShift

In this episode…

Forecasting in the Amazon ecosystem is one of the most frustrating challenges for brands and agencies. With so many shifting variables — from ad spend to competition to inventory fluctuations — how can companies predict sales and plan budgets without wasting resources or missing opportunities?

Amazon forecasting and automation veteran Todd VanderStelt and eCommerce strategist Sam Hager have developed a forecasting method for Amazon brands. They emphasize simplifying forecasting through a top-down model that uses a few key data inputs — ad spend, seasonality, and organic growth — to generate highly accurate predictions. Sam and Todd advise building forecasting tools that align teams around data, tie budgets directly to performance, and factor in real-world contexts, like promotions or unexpected press coverage, to make informed decisions.

In this episode of The Digital Deep Dive, Aaron Conant talks with Todd VanderStelt and Sam Hager, Co-founders of MixShift, about building accurate and actionable Amazon forecasting models. Together, they explain why top-down forecasting outperforms traditional methods, how linking ad spend to revenue improves planning, and the future innovations transforming eCommerce analytics.

Resources mentioned in this episode:

Aaron Conant on LinkedIn
BWG Connect
Todd VanderStelt on LinkedIn
Sam Hager on LinkedIn
MixShift: Website | Forecasting Tool | Email | LinkedIn

Quotable Moments:

“One of those things that hasn't changed is forecasting — is still a really hard thing to do.”
“We were trying to solve our own problems, frankly, and got pulled into a lot of those data problems.”
“If you can identify the key inputs that drive your forecast, you can basically aggregate all those errors.”
“There's this trade-off of profitability and revenue that we're always kind of faced with as operators.”
“You can't actually isolate the impact of advertising on your revenue without separating out these pieces.”

Action Steps:

Build forecasting models from the top down: Starting with total revenue and spend data reduces complexity and improves accuracy across large catalogs. This approach helps teams make confident, data-driven decisions even with incomplete SKU-level data.
Connect ad spend directly to revenue outcomes: Understanding the relationship between marketing investments and sales allows for smarter budgeting and realistic growth goals. It ensures spending aligns with actual performance rather than assumptions.
Incorporate qualitative insights into quantitative models: Adding context like promotions or media exposure helps refine forecasts and explain unexpected sales spikes. This balance of data and human insight strengthens decision-making across teams.
Use historical data for trend recognition: Analyzing at least two years of sales and spend history reveals reliable seasonality and organic growth trends. These patterns guide strategic planning and help brands anticipate market shifts.
Continuously validate and adjust models: Monitoring forecast accuracy over time ensures the model evolves with changing business conditions. Regular refinement keeps predictions relevant and actionable for leadership and operations.

About BWG Connect...

BWG Connect provides executive strategy and networking sessions that help brands from any industry with their overall business planning and execution. We network and knowledge share together to better understand and adapt to the newest trends, strategies, and pain points shaping growth in the digital space. BWG Connect, in conjunction with BWG Strategy, has built an exclusive network of 125,000+ senior professionals and hosts over 2,000 virtual and in-person networking events on an annual basis.

In addition, we have had 1x1 conversations with over 5,000 brands and have a real-time pulse on what digital strategies are successful and why. This in-depth knowledge allows our team to operate on the cutting-edge and provide our clients with best-in-class guidance on how to win in the digital world. We have provided free consultations and strategy sessions for companies of all sizes, from start-ups to Fortune-100, to enable growth, resolve issues and make curated service provider introductions that can impact your digital footprint.

We have held to the philosophy of providing high-level insights and actionable knowledge with no sales pitches in order to provide to our network the ability to listen, learn and act to improve themselves as well as their organizations. Our ultimate goal is to be the resource out there to help digital executives find the research they need to excel in the modern marketplace.

If you are interested in getting involved with any of our current or past events, you can find them here. If you are looking for help & would like to set up some time to chat with our team, you can schedule a time or reach out directly to Aaron@bwgconnect.com or Tiffany@bwgconnect.com.

Spencer Millerberg is the Founding Partner of DetailPage, which helps brands optimize traffic and gain Amazon market share. He founded OneClickRetail, the original provider of Amazon market share data that was sold to Ascential. As an eCommerce and data analytics expert, Spencer has helped over 650 brands improve sales, margins, and search positions on eCommerce sites.

Here’s a glimpse of what you’ll learn:

[1:54] Spencer Millerberg shares his background at Amazon and Walmart and how he founded OneClick Retail before DetailPage
[4:11] The rise of AI-driven shopping and ChatGPT’s influence on product discoverability
[8:48] How brands can improve visibility through detailed website content and ChatGPT product feeds
[14:30] Why most brands fail at SEO by prioritizing internal product language over customer terms
[18:22] How adding context to product content improves discoverability
[25:19] Using ROI dashboards to identify and optimize overlooked B SKUs
[31:13] How DetailPage helps brands sustain organic growth and outperform competitors

In this episode…

In today’s digital marketplace, visibility is everything. With AI tools like ChatGPT shaping how shoppers discover products, brands are facing a new kind of competition — one where search optimization meets machine learning. How can companies ensure their products are recommended when customers ask AI what to buy?

According to eCommerce and data analytics expert Spencer Millerberg, the key lies in mastering both traditional and generative optimization. He urges brands to speak the customer’s language, not their internal catalog terms, and to keep content fresh to trigger algorithmic boosts. By understanding how AI prioritizes retailer and brand content, and by revisiting SEO regularly, brands can secure lasting visibility and growth.

In this episode of The Digital Deep Dive, Aaron Conant welcomes Spencer Millerberg, Founding Partner of DetailPage, back to the show to discuss AI-driven discoverability. Spencer explains why retailer content dominates AI recommendations, how quarterly content updates improve search ranking, and why optimizing neglected B SKUs drives major growth.

Resources mentioned in this episode:

Quotable Moments:

“About 61% of customers are using AI to shop now.”
“Retailer content actually has become the highest weighted source for these AI recommendations that you have.”
“You got to have the why in order to match up.”
“If you're going to remodel your kitchen, don't just take care of the sink.”
“We're seeing about a 17% average lift on organic search when customers optimize for GEO and SEO.”

Action Steps:

Audit and update your SEO content quarterly: Regular updates keep your listings aligned with changing search algorithms and customer behavior. Fresh content also earns a temporary bump in visibility from major retailer platforms.
Optimize for both SEO and GEO (Generative Engine Optimization): Balancing search engine keywords with generative AI context ensures your products are discoverable across Google, Amazon, and ChatGPT. This dual strategy strengthens visibility in traditional and AI-driven recommendations.
Focus on your B SKUs: Mid-performing products often have untapped potential and can drive major growth when optimized. Prioritizing these SKUs boosts sales without relying solely on top sellers.
Use customer language, not internal product terms: Writing listings the way customers actually search bridges the knowledge gap. This shift improves discoverability and aligns your brand with search intent.
Leverage retailer and brand site content strategically: Ensure both your retailer listings and brand pages contain rich, structured product data. These are the highest-weighted sources AI tools and LLMs use for product recommendations.

About BWG Connect...

Aaron Schwartz is the Co-founder and Co-CEO of Orita.ai, a software company that improves email deliverability. He is also an advisor and investor in various commerce tech companies, including Passport and EcoCart. Previously, Aaron built and managed the DTC brand Modify Watches and was the President of Loop.

Here’s a glimpse of what you’ll learn:

[1:23] Aaron Schwartz shares his career trajectory from founding Modify Watches to co-founding Orita.ai
[4:22] Why brands overuse email during downturns and how it hinders engagement and deliverability
[9:20] How machine learning reveals insights missed by rules-based customer segmentation
[15:17] Why inbox placement and timing matter more than click or spam metrics
[19:51] Common mistakes brands make in retention marketing
[23:18] How retention marketing leadership varies across organizations
[31:34] The founding story of Orita.ai
[35:42] Aaron’s views on AI and personal shopping agents

In this episode…

Email remains a core channel for reaching customers, but brands often misuse it. As customer acquisition costs climb and competition intensifies, many brands resort to blasting their audiences with more campaigns, only to see engagement fall and customers unsubscribe. How can companies harness smarter strategies to keep customers engaged without burning them out?

According to eCommerce veteran Aaron Schwartz, the answer lies in data-driven retention marketing. Over-sending emails may provide a short-term lift, but ultimately damages deliverability and customer trust. By tapping into customer behavior data, brands can determine when someone actually wants to hear from them, creating more meaningful engagement. Shifting from a marketer’s mindset to a consumer’s perspective — and using modern analytics instead of outdated rules of thumb — can transform email, SMS, and direct mail into profitable retention channels.

In this episode of The Digital Deep Dive, Aaron Conant talks with Aaron Schwartz, Co-founder and Co-CEO of Orita.ai, about retention marketing best practices. Aaron discusses why brands must move past intuition and embrace data, how machine learning enables smarter segmentation, and why balancing timing with customer preferences drives higher ROI.

Resources mentioned in this episode:

Quotable Moments:

“Let's not think about segmentation based on time bound. Let's go build a very, very big model.”
“Take off your marketer hat and put on your consumer hat; don't be the brand that emails somebody.”
“Stop thinking about metrics and start thinking about your customer and when they want to hear.”
“If you're not questioning your priors all the time, those are the places it becomes hard.”
“Focus on what is the job to be done first before you go try solutions.”

Action Steps:

Use data-driven segmentation instead of rules of thumb: Relying on clicks or opens over fixed timeframes misses valuable customers. Machine learning models uncover deeper engagement patterns and help send the right message at the right time.
Balance communication frequency with customer preference: Over-sending emails can lead to unsubscribes and deliverability issues, even if it boosts short-term revenue. Respecting audience tolerance builds long-term trust and stronger retention.
Expand beyond email to multiple retention channels: SMS and direct mail can be profitable when targeted to the right segments. Diversifying outreach increases touchpoints without overwhelming customers.
Adopt a consumer-first mindset in marketing decisions: Thinking like your customer shifts the focus from what the brand wants to when the customer is ready. This improves engagement and reduces wasted effort.
Align leadership expectations with data insights: Mandates to send more campaigns often conflict with what the numbers say will work best. Empowering teams to follow the data prevents burnout and strengthens customer relationships.

About BWG Connect...

Lauren Livak Gilbert is the Executive Director of the Digital Shelf Institute (DSI), which shapes the future of digital and advances commerce through community, content, and education. In her role, she defines the strategy for creating a global industry commerce community to support brands, retailers, and future leaders in the complex digital environment. As a thought leader in digital and design, Lauren has over 10 years of experience driving impactful multichannel design content, transformative digital shelf solutions, and high-conversion web UX designs across multiple regions. Before DSI, she held numerous roles at Johnson & Johnson, where she owned the digital shelf for consumer products in North America.

Here’s a glimpse of what you’ll learn:

[1:37] Lauren Livak Gilbert introduces the reinvented omnichannel organization of the future
[3:01] Why companies must evolve their organizational structure to match consumer shopping habits
[6:21] The stages of organizational maturity and the three key principles of reinvention
[11:38] Amazon’s shift toward automated negotiations and Walmart’s expanding partnership opportunities
[16:25] How AI can automate forecasting and content creation
[23:23] Lauren’s future vision of humans as strategic orchestrators supported by AI agents
[32:32] Embracing data-driven decisions and learning through experimentation and failure

In this episode…

The pace of change in digital commerce has forced brands to rethink their organizational structures. Traditional structures built solely for brick-and-mortar sales can no longer keep up with evolving customer expectations and constant platform updates. How can brands transform their organizations to stay relevant in the omnichannel marketplace?

Seasoned digital commerce strategist Lauren Livak Gilbert urges brands to transform their overall operations instead of merely choosing where eCommerce belongs in the company. Presenting yourself to retailers as a single unified company, setting shared goals across teams, and using AI to automate tasks like forecasting and content creation are essential. Lauren also highlights the need for clean, standardized data and advises HR leaders to prepare the next generation of digital talent. These shifts position businesses to adapt quickly and thrive.

In this episode of The Digital Deep Dive, Aaron Conant sits down with Lauren Livak Gilbert, Executive Director of The Digital Shelf Institute (DSI), to discuss building the reinvented omnichannel organization of the future. Lauren explains why shared goals drive higher eCommerce sales, how AI frees teams for strategic thinking, and why HR must rethink talent development.

Resources mentioned in this episode:

Quotable Moments:

“What I actually found is that we're asking the wrong question about where eCommerce should sit.”
“You have to show up to your customers as one company, not separate disconnected teams.”
“If every team is doing something different, it's just common sense you're not going to reach goals.”
“You can use AI to automate a lot of forecasting and free up more mindshare.”
“Brands waiting to play around with AI will be light years behind when competitors move quickly.”

Action Steps:

Establish shared goals across departments: Aligning marketing, sales, and eCommerce teams ensures everyone works toward the same outcomes and prevents teams from competing against each other. This unified focus drives higher eCommerce sales and builds organizational cohesion.
Present a single face to retailers: Showing up as one company when working with retail partners fosters trust and clarity. This approach avoids miscommunication and helps negotiate stronger opportunities for growth.
Invest in data standardization: Cleaning and organizing your data infrastructure prepares the business for seamless AI integration. Without accurate and consistent data, AI tools cannot deliver reliable insights or value.
Use AI to automate repetitive tasks: Automating forecasting, content creation, and other manual work frees employees to focus on strategy and innovation. This shift enables teams to think more holistically and drive long-term growth.
Redefine HR recruitment and career paths: Updating hiring practices to identify and develop digital-savvy leaders ensures a strong talent pipeline. Preparing future leaders now safeguards the organization against skill gaps as technology and commerce rapidly evolve.

About BWG Connect...

Jana Weinstein is the Director of Social Commerce and Affiliate Strategy at Blue Wheel Media, an omnichannel marketing and operational agency, where she leads influencer, creator, and affiliate strategies across managed programs and creator-led initiatives. As a sales and growth leader, she specializes in TikTok Shop, business development, go-to-market strategies, and full-funnel social commerce performance. Before Blue Wheel, Jana was part of the founding team of TikTok Shop, where she launched the partner ecosystem and built enterprise agency partnerships. She also served as the Director of Partnerships for TikTok Shop at Pattern®.

Here’s a glimpse of what you’ll learn:

[1:13] How Jana Weinstein joined TikTok Shop’s founding team and her leadership role at Blue Wheel Media
[2:43] Insights on TikTok’s growth and opportunities for brands
[6:42] Three key reasons to leverage TikTok Shop: audience reach, the halo effect on other channels, and revenue potential
[12:43] How affiliates and creators drive TikTok Shop revenue and an overview of the affiliate program
[20:39] The shift from creating viral content to focusing on targeted influencer and affiliate marketing
[23:14] New AI-driven tools and first-party data initiatives on TikTok
[28:08] Navigating TikTok Shop operations by partnering with agencies

In this episode…

While many brands recognize TikTok Shop’s potential to boost sales and discoverability, they often hesitate to get started on the platform. Concerns about content creation, budget limits, and measuring results can make it difficult to identify a starting point. How can brands start and scale on TikTok Shop?

According to social commerce expert Jana Weinstein, TikTok Shop is worth the investment. She advises brands to begin by building an authentic, organic presence through simple, consistent content to train the platform’s algorithm. Paid media can boost visibility while affiliate programs generate revenue, provided brands cultivate strong creator relationships and gamify engagement. Achieving growth on TikTok requires an intentional long-term strategy rather than a quick viral hit.

Tune in to this episode of The Digital Deep Dive as Jana Weinstein, Director of Social Commerce and Affiliate Strategy at Blue Wheel Media, returns to speak with Aaron Conant about why TikTok Shop is a long-term marketing investment. Jana shares three reasons to get started on the platform, TikTok’s recent growth and new opportunities, and how to navigate operations on TikTok Shop.

Resources mentioned in this episode:

Quotable Moments:

"I was one of the first 50 founding members of the TikTok Shop US team."
"Creators are the lifeblood of the platform, so after you gain followers, lean into affiliates."
"If you are going to be measuring your affiliates within the platform, TikTok Shop is the way."
"There is validity in investing in this channel, but it can’t be with hopes to go viral."
"It's important to build that community…because they essentially are your salespeople selling for you."

Action Steps:

Start posting authentic TikTok content consistently: Regular posting trains the algorithm and builds recognition without requiring polished production. Consistency helps brands reach audiences faster and sets the foundation for future paid growth.
Use paid media to amplify reach: Boosting organic content with paid ads accelerates follower growth and improves placement on For You pages. This early investment ensures content reaches potential customers beyond initial followers.
Build an affiliate creator program: Affiliates drive the majority of TikTok Shop revenue and act as dedicated brand advocates. Developing a strong creator network increases sales and provides authentic promotion to engaged audiences.
Cultivate long-term relationships with key creators: Establishing ongoing partnerships fosters loyalty and enhances brand storytelling over time. Strong relationships encourage creators to consistently promote your products and generate repeat revenue.
Monitor and adapt to TikTok’s evolving features and data tools: The platform changes rapidly, from AI-driven analytics to new seller tools. Staying updated allows brands to leverage new capabilities and maintain a competitive edge.

About BWG Connect...

Udayan Bose is the Founder and CEO of NetElixir, a digital marketing agency designed to help the growth of eCommerce businesses, B2B companies, and retail brands through strategic solutions and an AI-powered platform. Udayan is also the Co-founder of The Udaan Trust, a nonprofit foundation of NetElixir that supports underprivileged young women in India to pursue their education goals and build successful careers.

Here’s a glimpse of what you’ll learn:

[1:55] Udayan Bose talks about NetElixir’s 21-year history and its shift to an AI-first agency
[4:35] How generative AI creates measurable business value and its difference from earlier forms of AI
[6:40] The role of predictive analytics and machine learning in engaging high-value customers
[11:50] Building trust in AI through privacy, security, and proprietary systems
[18:34] The exponential growth of applied AI and its potential across various industries
[23:10] Key considerations for choosing agencies and investing in future-driven strategies
[29:45] The long-term vision for human-AI collaboration and its impact on marketing

In this episode…

AI is transforming the way businesses operate, but the question remains: How can AI create measurable value instead of being just another buzzword? As industries experiment with generative tools, the stakes are high for brands trying to keep up with rapid technological shifts. What does it take to integrate AI responsibly to drive growth and innovation?

Digital marketer and AI researcher Udayan Bose says companies must rethink how AI fits into core strategies rather than treating it as an add-on. They can leverage machine learning and generative models to identify high-value customers, streamline complex workflows, and uncover new opportunities for revenue growth. The advantage comes from reinvesting efficiency gains into new roles, experimentation, and innovation. When paired with a human-centered, responsible approach, AI can reshape industries.

In this episode of The Digital Deep Dive, Aaron Conant sits down with Udayan Bose, Founder and CEO of NetElixir, to discuss unlocking business value with generative AI. Udayan talks about the disruptive potential of applied AI, why responsible adoption balances efficiency with human innovation, and how to select agencies and invest in future-proof strategies.

Resources mentioned in this episode:

Quotable Moments:

“We are really going through a massive transformation, from performance marketing agency to AI-first digital agency.”
“Generative AI essentially took the entire AI concept into a different level altogether, by horizontalizing everything.”
“We said, do we really need 43 tasks, or can we achieve output with fewer?”
“In the last two or three months, we were able to get a 30% time-saving.”
“Humans plus AI will always beat out just humans, because many tasks can be done faster.”

Action Steps:

Embrace AI as a core business strategy: Treating AI as central — not supplemental — helps unlock efficiency, scalability, and innovation across operations. Organizations that do this can stay competitive and future-ready as the technology evolves.
Quantify AI’s business impact: Establishing clear ROI metrics ensures AI investments deliver measurable value beyond hype. This accountability helps leaders secure buy-in and allocate resources strategically.
Reinvent workflows with AI-driven optimization: Breaking down and rethinking processes allows teams to streamline operations while maintaining quality. Even small workflow changes can save significant time and cost.
Balance efficiency with responsible workforce development: Instead of reducing headcount, channel AI-driven gains into new roles and innovation. This approach sustains morale and builds long-term organizational resilience.
Prioritize privacy and security in AI adoption: Implementing safeguards like proprietary data layers protects customer trust. Responsible data practices also ensure compliance with regulations and reinforce brand reputation.

About BWG Connect...

Will Nelson is the Copilot Lead at Microsoft, where he leads federal executive engagements for Microsoft Copilot, focusing on AI applications. With a background in product operations, internal tooling, and early-stage tech companies, he has launched new products, built scalable systems, and bridged gaps between emerging tools and real-world outcomes. Before Microsoft, Will was a Founding Product Ops Intern at Pie Insurance, where he grew the commercial auto vertical from zero to $2 million in premiums.

Here’s a glimpse of what you’ll learn:

[3:04] How Copilot enhances Microsoft Teams meetings with transcription, translation, and context-aware summaries
[10:22] Automating low-value tasks with Copilot
[13:21] Will Nelson explains how Copilot chat clarifies acronyms and technical terms in real time
[17:45] The benefits of indexing organizational knowledge in Teams
[22:30] A demonstration of Copilot’s universal translation capabilities
[29:28] Copilot’s integration in Word with inline editing and sidebar tools
[40:11] How context-rich prompts improve AI outputs

In this episode…

Many organizations waste time in meetings, scatter knowledge across various tools, and engage in repetitive administrative tasks. Employees often feel bogged down by minutiae rather than focusing on high-value work that drives impact. How can integrating AI into daily office applications reduce inefficiency, free up human creativity, and improve team productivity?

Microsoft Copilot embeds AI directly into familiar tools like Word and Teams. The technology includes meeting transcription, automated agendas, real-time translations, and post-meeting recaps that free teams from administrative burdens. AI integrator Will Nelson recommends using AI to clarify acronyms, generate podcasts from transcripts, and role-play for interviews or presentation prep. By leveraging AI as a facilitator rather than a replacement, businesses can focus on mission-critical work rather than minutiae.

In today’s episode of The Digital Deep Dive, Will Nelson, Copilot Lead at Microsoft, returns to chat with Aaron Conant about how AI tools can enhance daily workflows. Will talks about streamlining project management, enhancing collaboration in Teams, and the importance of context in prompting.

Resources mentioned in this episode:

Quotable Moments:

"You now have a facilitator built into every single meeting that is keeping things on track."
"If you are not using or adapting and using these… tools, you will be left behind."
"The narrower of a scope that we give, generally, the less hallucinations you have."
"Now you are empowered to go do with these meeting minutes, with these deliverables, what you need."
“This is the worst it will ever be. It's the worst it will ever be."

Action Steps:

Leverage AI to automate meeting recaps: Automating summaries, agendas, and deliverables saves time and ensures critical details aren’t lost. This shifts attention away from tedious note-taking and back toward mission-focused work.
Use translation features to expand collaboration: Real-time translation in meetings makes diverse teams more inclusive and effective. It removes language barriers, allowing everyone to contribute equally and with confidence.
Adopt AI as a facilitation tool: Integrating facilitator agents keeps meetings structured and accountable. This ensures goals are met and follow-ups are completed without overburdening project managers.
Increase knowledge density through AI summaries: Condensing long meetings into digestible summaries or podcasts helps employees absorb essential information quickly. This improves decision-making and boosts productivity without requiring extra time investments.
Practice with AI role-play scenarios: Using AI to simulate interviews or presentations provides a safe environment to refine communication skills. This preparation boosts confidence and reduces the risk of underperforming in high-stakes situations.

About BWG Connect...

Joe Luchs is the Founder and CEO of DatalinxAI, an AI-powered data refinery that simplifies customer data. As an expert in AI, AdTech, MarTech, and SaaS, he previously served as the Global Head of AWS and Amazon Ads at Amazon, where he led global partnerships and AI business development. Joe was also the Commercial Founder and Head of Revenue at Beeswax (acquired by Comcast) and held a leadership role at Oracle.

Here’s a glimpse of what you’ll learn:

[1:19] Joe Luchs talks about combining AWS and Amazon Ads at Amazon
[5:12] Why brands struggle to implement custom AI models
[9:17] DatalinxAI’s hybrid cloud architecture for secure, in-place data processing
[16:22] The risks of AI creativity without regulations
[19:46] Prioritizing data readiness and preparation before integrating AI tools
[26:44] How brands can adapt to growing demands for hyper-personalization

In this episode…

Organizations are beginning to implement AI for personalization, automation, and business intelligence. Yet, messy, unstructured, and inconsistent data hinder smooth implementation. How can businesses bridge the gap between cutting-edge AI models and the data readiness required to scale them?

According to AI and data expert Joe Luchs, companies often spend years and vast resources on data prep before they notice ROI on AI models. He recommends using secure hybrid cloud architectures, embedding data compliance measures, and automating preparation processes to free up teams from tedious work. By focusing on high-quality data readiness, businesses can accelerate AI adoption, empower staff to focus on innovation, and deliver the hyper-personalized experiences consumers expect.

In today’s episode of The Digital Deep Dive, Aaron Conant sits down with Joe Luchs, Founder and CEO of DatalinxAI, to discuss preparing data for widespread AI adoption and integration. Joe explains how consultancies have pioneered data readiness, the future of AI-driven brand experiences, and the dangers of leveraging AI without proper regulations.

Resources mentioned in this episode:

Quotable Moments:

“Bad inputs lead to bad outputs. Most business leaders just want to use these AI tools effectively.”
“You hire these data science and engineering resources, then deploy 80% of their time on data prep.”
“Our mission is how can we accelerate our customers’ success with AI by giving them the right data fuel?”
“If you could have those folks doing high leverage work, rather than commoditized work, your organization operates functionally.”
“Consumers have come to a point where they just expect great personalization. That world of broad targeting is dead.”

Action Steps:

Automate data preparation processes: Reducing manual prep work frees data scientists and engineers to focus on innovation and model development. This shift boosts efficiency and improves morale by eliminating repetitive, low-value tasks.
Strengthen data security with hybrid cloud architecture: Keeping data in place while enabling secure access reduces risks of breaches and compliance violations. This approach ensures enterprises can adopt AI at scale without compromising sensitive customer information.
Establish clear data guardrails and compliance checks: Embedding privacy measures like hashing and consent streams ensures adherence to regulations such as GDPR and CCPA. These safeguards build trust with consumers and protect the business from costly legal pitfalls.
Focus on data quality: Ensuring clean, structured, and integrated data inputs maximizes the performance of AI models. High-quality data directly translates into more accurate predictions, effective personalization, and measurable business growth.
Repurpose technical staff toward high-leverage work: Shifting talent from repetitive data cleanup to AI model building accelerates organizational impact. This reallocation maximizes minutes on mission, driving both innovation and employee engagement.

About BWG Connect...

Here’s a glimpse of what you’ll learn:

[1:22] Will Nelson talks about his role as Microsoft’s Copilot Lead and how he transitioned from insurance technology
[4:43] How Copilot differs from ChatGPT and other AI tools
[7:01] The role of synthetic data in AI model training
[10:20] Microsoft Copilot as an AI coordinator that integrates context, tools, and user data
[13:41] How Copilot safeguards and indexes organizational data
[22:11] Tips for prompting AI chatbots to improve responses
[25:58] An overview of Microsoft Copilot’s research agent and real-world use cases of the tool

In this episode…

Many organizations are racing to adopt AI but struggle to apply it securely, effectively, and at scale. Leaders worry about whether they need multiple tools, how to trust AI outputs, and how to prepare their teams for this shift. With so many platforms available, how can businesses identify where AI delivers the most value without adding unnecessary complexity?

According to AI expert Will Nelson, Microsoft Copilot functions as an AI coordinator that integrates tools, data, and context to enhance outputs. This tool allows companies to conduct deep research and secure their data through tenant-based governance. Will maintains that organizations don’t need world-changing use cases to benefit from AI; even simple applications like meeting preparation and document summarization can significantly increase productivity. This can be accomplished through effective, specific prompting.

In this episode of The Digital Deep Dive, Aaron Conant chats with Will Nelson, Copilot Lead at Microsoft, about how Microsoft Copilot enables organizations to adopt AI effectively. Will talks about the future convergence of large language models, how government agencies are adopting AI, and how to use synthetic data to train AI models.

Resources mentioned in this episode:

Quotable Moments:

"This is the worst this technology will ever be. It's only getting better from here."
"Copilot exists within your tenant. So when you ask a question, that never leaves."
"We think about Copilot chat as an AI orchestrator, not just a single underlying model."
"Sometimes it just takes a better set of instructions, a better prompt, and specificity."
"The output is useful, the process is not. Copilot shifts time back to meaningful work."

Action Steps:

Use AI as an orchestrator, not just a chatbot: Treating AI as a tool that integrates context, data, and functionality ensures results are accurate and relevant to your needs. This shifts AI from novelty use to practical daily value.
Prioritize data governance in AI adoption: Keeping AI usage within secure organizational systems protects sensitive information and aligns with compliance standards. This builds trust in the technology across leadership and employees.
Invest time in crafting better prompts: Providing detailed, specific instructions helps AI deliver more accurate and useful responses. Even small improvements in prompting can significantly enhance productivity.
Start with simple, repeatable use cases: Begin with tasks like meeting preparation, email summarization, or report generation before scaling to complex projects. This approach accelerates adoption by showing clear value quickly.
Leverage AI for preparation and training scenarios: Role-playing or analyzing meeting notes with AI reduces wasted effort and increases “minutes on mission.” This empowers teams to focus more on impactful, high-value work.

About BWG Connect...

Here’s a glimpse of what you’ll learn:

[1:19] Joe Luchs talks about combining AWS and Amazon Ads at Amazon
[5:12] Why brands struggle to implement custom AI models
[9:17] DatalinxAI’s hybrid cloud architecture for secure, in-place data processing
[16:22] The risks of AI creativity without regulations
[19:46] Prioritizing data readiness and preparation before integrating AI tools
[26:44] How brands can adapt to growing demands for hyper-personalization

In this episode…

Resources mentioned in this episode:

Quotable Moments:

“Bad inputs lead to bad outputs. Most business leaders just want to use these AI tools effectively.”
“You hire these data science and engineering resources, then deploy 80% of their time on data prep.”
“Our mission is how can we accelerate our customers’ success with AI by giving them the right data fuel?”
“If you could have those folks doing high leverage work, rather than commoditized work, your organization operates functionally.”
“Consumers have come to a point where they just expect great personalization. That world of broad targeting is dead.”

Action Steps:

Automate data preparation processes: Reducing manual prep work frees data scientists and engineers to focus on innovation and model development. This shift boosts efficiency and improves morale by eliminating repetitive, low-value tasks.
Strengthen data security with hybrid cloud architecture: Keeping data in place while enabling secure access reduces risks of breaches and compliance violations. This approach ensures enterprises can adopt AI at scale without compromising sensitive customer information.
Establish clear data guardrails and compliance checks: Embedding privacy measures like hashing and consent streams ensures adherence to regulations such as GDPR and CCPA. These safeguards build trust with consumers and protect the business from costly legal pitfalls.
Focus on data quality: Ensuring clean, structured, and integrated data inputs maximizes the performance of AI models. High-quality data directly translates into more accurate predictions, effective personalization, and measurable business growth.
Repurpose technical staff toward high-leverage work: Shifting talent from repetitive data cleanup to AI model building accelerates organizational impact. This reallocation maximizes minutes on mission, driving both innovation and employee engagement.