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8 Tech Investors Share Predictions for 2023

2022 was a busy year for the team at Insight. As hype started to build around the use of AI in our everyday lives, Insight held its first ScaleUp:AI conference, featuring top industry speakers and hosting over 1,700 attendees. The firm also grew the Onsite team — Insight’s dedicated ScaleUp engine of Sales, CS, Product, Marketing, and Talent experts — to over 120 operators to better support portfolio companies, help them focus on metrics that move the needle, and prepare them for whatever comes next.

As we wind down the year, eight of Insight’s Managing Directors share some thoughts about what’s top of mind for tech investors going into 2023.

insight partners investors
From left to right:
Ryan Hinkle, AJ Malhotra, Rebecca Liu-Doyle, Thomas Krane
Michael Yamnitsky, Nikhil Sachdev, Lonne Jaffe, George Mathew

We’re going to hear a lot more about AI.

If 2022 was the year of crypto, 2023 will be the year of AI truly breaking into the general population’s awareness.

The shift from analytical AI to generative AI

Lonne Jaffe: “Many had been operating under the assumption that manual labor and simpler knowledge work would be most disrupted by AI and automation, but with large foundation models like GPT-3 and DALL-E, we’re seeing AI systems make enormous progress in highly creative tasks like design, programming, music, and creative writing. This will likely continue in 2023 with the release of systems like GPT-4. At the moment, the reliability of these models is still a major challenge — they often hallucinate answers that are false but still ‘speak’ confidently. This kind of unreliability could be problematic for a lot of use cases, like customer service, education, and healthcare. If you don’t already know the answer, it can be hard to tell whether some AI-generated responses are correct.”

Nikhil Sachdev: “We’re moving from analytical AI (analyzing/parsing data and identifying trends and patterns) to generative AI (creating new content or interactions based on patterns). Applications we’re seeing now are benefiting from powerful (often open source) large language models, cheaper computing costs, and established MLOps platforms. These AI applications are starting to overtake human functions and have the potential to augment and disrupt existing entrenched software apps.”

George Mathew: “More of us should be talking about explainability and bias detection as more large language models (LLMs) get to scale and production. We should all be preparing for what opportunities will emerge with a multi-trillion parameter large language model like GPT-4 being released.”

Lonne Jaffe: “It will be very interesting to watch where the value will accrue and where economic moats will be the deepest. Some believe that the economic moats will accrue to the companies building the large foundation models because they require so much time, skill, and infrastructure spend. Others think that the moats will be with the companies fine-tuning the models for specific use cases because of the feedback data demand-side economies of scale. Still others believe that the value will be in the non-AI software that allows the models to integrate with real-world systems. There may even be a layer of value in between the foundation model creation and fine-tuning, requiring a new set of MLOps tools and skills that focus the foundation model for a specific domain, but in a way that is more involved in modifying the internals of the foundation model than needed during the fine-tuning process.”

Moving from AI in infrastructure to AI in applied real-life situations

lonne jaffe quote

Lonne Jaffe: “One area where we’re likely going to see continued huge progress in 2023 is in applied computer vision AI in healthcare. The tech is already approaching human ability in domains as varied as polyp detection in colonoscopies, diagnosing gum disease in dentistry, breast cancer screening in a mammogram, etc. This can improve diagnostic accuracy, save physician time, surface candidates who would benefit from clinical trials, and even reshape how the industry works.”

The metrics investors care about in 2023 will shift to retention and efficiency.

“More nailing it, less scaling it.”

Ryan Hinkle, Managing Director: “2023 is about more nailing it, less scaling it. 2023 should be a year where it’s efficiency first, additional costs second. It is really difficult to focus on efficiency when you are adding costs. That is the fundamental pendulum shift: it has abruptly shifted from ‘if you believe it, it will come’ to ‘if you can’t see it, it doesn’t exist.’”


Metrics that matter

Nikhil Sachdev: “Customer NPS is always important, even more so in this environment. (Are you nice to have? Or, I can’t live without you?) NPS flows through all the relevant financial metrics in a business. The more customer value/love you generate, the better your logo growth, pricing power, retention, and efficiency. And goes without saying in this market, it’s no longer growth at all costs. Companies and investors are focused on durable, efficient growth.”

George Mathew: “Gross retention — more than ever, you have to be able to retain customers to stabilize your 2023 growth plans.”


Thomas Krane: “Path to breakeven based on current balance sheet, cash burn as a multiple of net-new ARR.”

AJ Malhotra: “It’s all about how you’re investing to drive efficient growth. My key metrics are about the same: previously, it was all about net-new ARR, and now gross profit matters more. Your true gross (and net) retention becomes very, very important as well — this separates strong companies from weak ones. Cash burn also becomes imperative in this environment.”

Rebecca Liu-Doyle: “In this environment, two things investors are watching especially closely are gross margin and gross retention, both of which are prime leading indicators for steady-state free cash flow potential. In steady state, will this be a 15%+, 25%+, or 50%+ FCF business?”

DevOps will prioritize simplicity.

Michael Yamnitsky comments on the developer perspective: “The great vibe shift of 2023 is a return to simplicity! Back in 2017, it was cool to tinker with the nuts and bolts of Kubernetes, but as of 2022, we’ve reached peak complexity and specialization in cloud infrastructure, and the pendulum is swinging back. Developers want to simplify their stack and ship code faster. To this tune, we’ll see a resurgence of PaaS and other developer-friendly services that eliminate the toil while retaining all the benefits of 10+ years of advances in cloud technology.”

Thomas Krane, Managing Director: “In DevOps, cost pressure will put new pressure on public cloud workload adoptions and reinforce the need to have interoperability between on-premises IT and cloud services. This creates opportunities for new vendors in the space.”

Rust will be all the rage

Additionally, Michael adds: “Rust is all the rage and demand for rust programmers is growing. The performative nature of this programming language makes it a fit for backend-heavy development, particularly in the infrastructure and developer tooling space where performance can be a key differentiator.”

The overall economic environment will be uncertain for a while, but it’s not all bad news.

Ryan Hinkle: “None of us are used to inflation. Inflation hasn’t been a consideration for literally 30 years. Because of inflation, if you aren’t growing 8%, you are shrinking on a real basis. We enter 2023 with a great deal of known issues — inflation being front and center — but no real ability to forecast what comes next. In 2023, we will need to re-evaluate on a quarterly basis or even more frequently, as a year will feel like an eternity. Years make sense as forecast building blocks when things are well-behaved. These are not well-behaved times.”

Nikhil Sachdev: “Market sentiment is as negative as it has been since the Great Recession. We are seeing a combo of inflation, rising rates, cratering multiples, geopolitical turmoil, and de-globalization, which is impacting our supply chains. On top of that, the demand curve is being whipsawed – first as we lap a period of strong pull forward in digital growth driven by the pandemic period, and now budgets and spend tightening. It’s time to go back to basics — focusing on durable growth and building/scaling efficiently are the fundamentals that will enable companies to succeed regardless of the macro. Just remember that things are never as bad as they seem at the bottom and never as good as they seem at the top.”

Thomas Krane: “Companies that largely sell into tech companies with products linked to headcount will see a significant medium-term downdraft in revenue, but there will be a strong recovery on the other side for those that survive.”


Survival of the strongest will drive consolidation

Nikhil Sachdev: “So much of the bad news is out and now baked in the cake that on balance I think equity markets will be more constructive over the next year. I think we’ll see more private tech dealmaking. Growth-stage companies will still need to raise money, maybe at different multiples than before. We are also going to see much more consolidation as companies that can’t or don’t want to continue down the standalone path look to partner with strategics.”

AJ Malhotra: “We’ll see consolidation — lots of companies have raised lots of money, with unsustainable burn rates, cost structures that may not be efficient, and that means some will not be able to raise follow-on rounds and will need to sell. The velocity of fundraising that happened in the tailwinds of Covid from 2021-2022 was a unique moment in time.”

Ryan Hinkle: “Whatever this recession will be, it will really test what is ‘needs to have’ vs. ‘nice to have’ and inform what gross and net retention looks like. We have not had a meaningful downturn since SaaS emerged as a dominant trend in digital transformation.”

There are opportunities in uncertainty


Lonne Jaffe offers several examples of how tech, and AI specifically, could help to alleviate inflationary pressure: “The go-to reaction to inflation is to have Federal Reserve Bank raise interest rates and to slow the economy and raise unemployment. But this comes at a huge cost. Despite the anxiety around robots and automation taking jobs, there can be an opportunity for tech to help alleviate inflationary pressure by increasing efficiencies and making us all more productive. In a similar way as collaboration software helped the economy cope with isolation from the pandemic, this kind of AI-powered efficiency improvement, in a way, could become the unsung hero of this inflationary crisis period.”

George Mathew: “Backoffice sectors like supply chain, procurement, and business process outsourcing all have fundamental opportunities to be transformed by generative AI.”

Thomas Krane: “The cost of cloud services will create opportunities to preserve and even expand on-premises IT.”

Michael Yamnitsky: “One of the positives of continued economic uncertainty going into the new year: the spotlight shifts away from the hype-chasers and storytellers and towards the humble entrepreneur who has been quietly owning their craft.”

Rebecca Liu-Doyle: “Certain categories — like beauty in consumer and automation in enterprise SaaS — have counter-cyclical tailwinds, and this may be their moment to shine.”

AJ Malhotra: “New company formation will increase because of layoffs, and lots of talented folks will have new time on their hands to build something new.”


Hiring might get easier.

George Mathew: “There will be a much more available labor market as hundreds of thousands of tech workers are being laid off at the ‘Big Tech’ firms.”

AJ Malhotra agrees: “Hiring is a big opportunity right now! A lot of good people are in the job market because of layoffs. Hiring may become easier given the talent out there. We have dueling realities — giant tech companies are doing layoffs and hiring freezes, but unemployment is low. We’re still seeing hiring in many industries.”

There’s still a lot to be excited about in tech.


Nikhil Sachdev: “While I acknowledge we are in a peak hype cycle for AI, I think the secular trend is real and feels like we are on the verge of an explosion here. AI will impact horizontal and vertical segments within software.”

Michael Yamnitsky: “I’m excited about WebAssembly. It has the potential to bring unparalleled levels of efficiency and security to computing and transform the way developers organize and collaborate around code. But most importantly, it’s portable — making it a unique fit for the next wave of distributed applications.”

Thomas Krane: “Threat intel will finally get recognition as a critical baseline/foundational priority for a strong cybersecurity stack.”

AJ Malhotra: “It’s easy to be a pessimist but there are a lot of good things happening right now: hybrid work environments are better overall and have provided more flexibility to people, there’s low unemployment. There’s tons of opportunity to do things more productively and more efficiently. Tech dealing with carbon emissions and clean energy transitions, enterprise software selling into financial services, software for the build environment, and tech dedicated to improving healthcare delivery are all exciting areas right now.”

This post was compiled and edited for conciseness and clarity by Jen Jordan.

7 Habits of Effective Data Leaders

CxOs are realizing every executive in the organization is a data leader in the age of digital transformation. Whether their background is in data analytics or not, successful CxOs are navigating this transition by actively engaging with data departments to fill gaps in knowledge. They proactively build a set of practices and habits that drive useful insights. In doing so, leaders have pivoted from a defensive to an offensive data strategy and culture.

Analytics teams previously collected data, analyzed it and offered insights directly to leadership. Instead, modern data teams are much less siloed. They work with, and in support of, multiple executive officers and stakeholders in different departments, in a decentralized way, throughout the company’s digital transformation.

Data Results | By Company Size

How the leadership navigates this spectrum varies from company to company. However, highly effective data leaders have established a series of best practices to guide them through the growth curve, and continue to follow these practices until they become habits. As Pulitzer Prize–winning writer and productivity expert Charles Duhigg wrote in The Power of Habit: Why We Do What We Do in Life and Business, “there’s nothing you can’t do if you get the habits right.”

1. Shift the culture and strategy

The role of data executives can be transformative. Their ability to collect and analyze data, then create real operational value from the business insights that data reveals, makes them a transformational force.  It’s in any executive’s interest, therefore, to embrace data technologies. Every department stands to gain, from finance (maximizing revenues and minimizing costs) through enhancing sales and marketing practices (buyer intent, lead generation and opportunity conversion to order), to the C-suite (organizational transformation, long-term growth, customer churn and competitive strength).

Data leaders rely on data to actively predict customer behavior, and refine their own engagement practices and pipelines. How sophisticated they are often depends on where the organization is in its digital journey.

Data-driven organizations are maturing as they move beyond relying on low-level automation tools such as chatbots, to measure successful customer experiences, using this insight to predict future customer behavior. A 2021 Gartner report confirmed that customer service departments get most value from technologies that analyze customer data.

Objective | Pivot from a defensive data strategy to an offensive, democratized strategy. Approach > Key Objective > Core Activity > Data Elasticity

Data Culture & Strategy | Approach and Focus

2. Refine the business context

The business context in which data is collected is changing too. Increasingly, organizations are focusing on customer-centric research to enrich their analytics strategies. A large part of data leadership has focused on identifying short-, medium- and long-term metrics to better understand and predict customer behavior. Although some customer trends stay the same, others fluctuate, year by year, as the ways customers interact with technology slowly evolve.

shows 7 criteria meant to drive customer adoption.
Data Strategy | Customer-First Principles

In response, business leaders are learning to modify their organization’s operations, sales and analytics strategies and to adopt more customer-focused data solutions. Data pipeline company Rudderstack provides a data platform to help organizations implement this data-driven strategy for improved customer support. The platform allows enterprises not only to track customer data, but to directly engage with the customer.

3. Grow the data pipeline with the company

As mentioned above, data strategies shift as companies grow. Early-stage companies often have a very different set of tactics — and see very different results — than multi-million dollar scaleups. As they scale, though, data leaders realize that the range and level of data-driven insight must scale with them.

Leaders often progress from planning a data strategy that merely harvests data, to one that focuses on action by using data to shape organizational decision-making at every level. This is a crucial transition, as it requires data departments to undergo a fundamental intellectual shift, from being passive collectors of data to active advisors. In this way, data streams and enterprise systems are integrated so that predictive data can actively inform decisions.

Data analytics innovator Kubit, for instance, provides a self-service behavioral analytics platform. It harnesses behavioral insight that allows organizations to optimize sales and marketing conversion.

4. De-silo the architecture

The shift towards more proactive data strategies also involves rethinking the data architecture. Previously, organizations might have organized their data by team or purpose. Now that companies need greater volumes of better quality data, siloed datasets can hinder proper access to the data.

Many data leaders are migrating from a data warehouse-centric architecture to a data lake-centric one. Data lakes provide much greater reporting capability and analytical flexibility thanks to their accommodation of unstructured data. Conversely, data warehouses require structured data while data lakes can accept almost anything: Structured data, unstructured data, media and more.

Diagram showing Data Warehouse (Late 1980s), Data Lake (2011), Data Lakehouse (2020)

Data Architecture Evolution | Source: Databricks

However, the structured data of a warehouse works well for analytics, but is too rigid for advanced AI or machine learning (ML) models to work with. Data lakes are not without their challenges either: They provide great flexibility for AI-driven processes but are unsuitable for reporting dashboards. As a way to overcome the gaps in functionality for both alternatives, the data engineering company Databricks offers data lakehouses for both advanced reporting and agile AI.

Regardless of the implementation, executives are leading the move to a more unified, de-siloed architecture where data from multiple streams and teams can be integrated to inform better decision-making. A new trend is to master customers in the data lake versus the CRM systems, allowing for a complete 360 view of the customer available for real-time AI.

5. Build trust in data

Earlier, we described the growth trajectory of early-stage startups as they scale, in terms of their shift in focus in data strategy. This doesn’t mean, however, that DataOps principles such as governance and security take a back seat. Privacy, compliance and security remain critical to maintaining customer trust in any organization. Data leaders are always sensitive to these concerns and are constantly working to enhance trust in their company’s data policies.

Proper governance is a huge pain point for startups —  Gartner’s 2021 outlook predicted that, through 2025, 80% of organizations will see efforts to scale their business fail because of a lack of modern data governance. To make matters worse, some businesses don’t even have visibility into how effective their governance policies are. Over 40% of executives surveyed had no metrics to measure whether their data governance policies were actually working.

Fixing this problem is an ongoing challenge as executives struggle with perception as much as reality. Every time a data breach becomes public knowledge, company leaders confront a tide of general suspicion and distrust from customers, even if the breach happened to a different company.

Credit bureau Experian, for instance, saw its brand tarnished by the infamous Equifax breach that exposed the financial data of 143 million Americans.

Displays different tiers (lines of defense) for data governance

Data Governance | Prioritization & Lines of Defense

To deal with much bigger threat vectors, executives have begun instituting strict policies at every organizational level: From the adoption of tools like multi-factor authentication (MFA), to precise cloud-access control policies and updated engineering best practices for sensitive data.

Many companies prefer to buy — rather than build — their governance and security infrastructure. Rather than rolling their own authentication and security suites, enterprises opt for third-party offerings like Privacera. Privacera provides a unified suite of access control, data governance and security solutions for data warehouses. This type of third-party, all-in-one solution reduces the workload of a company’s IT and engineering teams by removing the need to maintain and constantly update security code. It also leaves security in the hands of the experts, promoting greater reliability.

6. Focus on people

Building a scalable data department in today’s dynamic, post-pandemic workplaces requires leaders to have an innate ability to collaborate with stakeholders from across the enterprise. Data teams are more decentralized, as previously mentioned, with data scientists embedded within teams from other departments such as engineering, product sales or marketing and finance.

Mission | Establish a Center of Excellence with Standing Teams for ML & Data. Somain Business SME; Product Manager/ Data Analyst; Sata Scientist; Data Engineer

Data Standing Teams | Framework

Just as the remit of the data department has expanded, so has the role of its leaders. Previously siloed chief data officers are now chief data and analytics officers (CDAO), forming skilled teams in an environment where data-driven insight is viewed as a key competitive advantage. And for both growing and large businesses practicing new habits, “Small wins are a steady application of a small advantage,” Duhigg wrote. This collaborative mindset often becomes a culture through the entire department, enabling data teams to work more smoothly with other stakeholders.

7. Drive value

The final responsibility modern data leaders carry is the job of driving consistent and tangible value for an organization. In bringing modern analysis techniques to company teams, data executives have an opportunity to influence genuine change. Evidence shows that by being responsive to customer behavior companies can improve satisfaction, loyalty and drive new revenues.

It’s often the case that one successful project will breed curiosity in other parts of the business. Where successful proofs of concept are adopted by other departments, overall efficiencies are amplified. Data-driven optimization initiatives that help companies transition from insight to action have led to real gains. Driving this kind of optimization helps establish the importance of the rise of data leaders and CDAOs as an engine room for company credibility and growth.

New data habits enable successful digital transformation

The emergence of every CxO as a data executive has cemented the importance of data to a company’s digital transformation. As analysis methodologies become more sophisticated, and the use of data continues to evolve, the ability of leaders to form new habits will be crucial. Storytelling using data and visuals can change a company’s trajectory from being a follower to becoming a leader — as well as a disruptor — in an industry segment.

Note: Insight is an investor in Rudderstack, Kubit, Databricks and Privacera

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